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Special Issue "Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (13 February 2021).

Special Issue Editors

Prof. Dr. Rafal Burdzik
E-Mail Website
Guest Editor
Faculty of Transport, Silesian University of Technology, Katowice, Poland
Interests: acoustics and vibration; vibration measurement and control; predictive maintenance; transportation science and engineering, numerical simulations
Special Issues and Collections in MDPI journals
Prof. Dr. Minvydas Ragulskis
E-Mail Website
Guest Editor
Center for Nonlinear Systems, Kaunas University of Technology, Kaunas, Lithuania
Interests: nonlinear dynamics; time series analysis; permutation entropy; pattern recognition; big data and deep learning
Special Issues and Collections in MDPI journals
Prof. Dr. Maosen Cao
E-Mail Website
Guest Editor
1. Jiangxi Provincial Key Laboratory of Environmental Geotechnical Engineering and Disaster Control, Jiangxi University of Science and Technology, Ganzhou 341000, China
2. Institute of Dynamics and Control, College of Mechanics and Materials, Hohai University, Nanjing 210098, China
Interests: predictive fault diagnosis; machine learning techniques; big data and deep learning; structural health monitoring, uncertainty analysis
Special Issues and Collections in MDPI journals
Dr. Radosław Zimroz
E-Mail Website
Guest Editor
Wrocław University of Science and Technology, Wrocław, Poland
Interests: mining machines; field measurements; condition monitoring; advanced signal processing; data analytics; acoustics; predictive maintenance
Special Issues and Collections in MDPI journals
Dr. Chaari Fakher
E-Mail Website
Guest Editor
National School of Engineers of Sfax, Sfax, Tunisia
Interests: machine and structure dynamics; vibro-acoustic behavior of machines and structures
Special Issues and Collections in MDPI journals
Dr. Łukasz Konieczny
E-Mail Website
Guest Editor
Silesian University of Technology, Katowice, Poland
Interests: structural analysis; finite element modeling; structural dynamics; automobile engineering; nonlinear analysis; dynamic analysis
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

I would like to interest you in this Special Issue: Sensing Technology and Data Interpretation in Machine Diagnosis and Systems at Sensors and cordially invite you to submit your articles. The purpose of this Special Issue is to compile studies on knowledge, research practice, and forecast development trends in the field of machine and system diagnostics, with particular emphasis on measuring systems and signal processing methods to extract useful information. The dynamic development of the Smart concept in all engineering areas indicates the need for consolidation and exchange of knowledge in this area, for which this Special Issue is an ideal platform. Hence, in terms of engineering applications and research problems, we are not limiting the articles that can be submitted to this Special Issue.

Machine diagnosis and systems condition monitoring are fundamental processes for decision making protocols in all mechanical systems. Control and steering of all systems determine its operational and functional activities. These decisions must be made based on proper data. Therefore, the most appropriate data must first be obtained and then the important information components must be separated. All components of the logical decision path that may degrade the quality of the data acquired or reduce operational reliability must be avoided and eliminated. Therefore, it is important to correctly indicate the data acquisition points, select the most suitable sensors, correctly complete the entire measurement path and dedicated signal analysis.

This Special Issue will focus on recent attempts in development of sensors and sensing technology due to novel possibilities in machine diagnosis and systems condition monitoring to underline this new knowledge and application, especially for the trend in smart machines and systems with self-diagnosis properties. An example of this is the concept of smart cities and intelligent systems, which is trending worldwide. The huge demand for continuous comprehensive information in relation to all areas of the city's functioning, especially transport, is a considerable challenge. This Special Issue will collect interdisciplinary approaches on sensors and sensing technology in machine diagnosis and systems condition monitoring, including the consideration and development of some innovative directions in research.

The potential scope includes but is not limited to the following:

  • Methods and apparatuses for machine diagnosis and systems condition monitoring;
  • Signal processing, data fusion, and deep learning in sensor systems;
  • Damage detection and identification in machines;
  • Condition monitoring in systems;
  • Sensors in control and steering of the system;
  • 5G/6G technologies;
  • Identification of machinery non-stationary and anomalous operation;
  • Advanced signal processing methods for machine diagnosis and condition monitoring;
  • Practical cases of machine diagnosis and systems condition monitoring;
  • Machine and system assessment under noisy conditions;
  • Intelligent transport systems;
  • Sensor network and relationships;
  • Smart/intelligent sensors;
  • Sensor technology and application for machine diagnosis and systems condition monitoring;
  • Internet of Things for machine diagnosis and systems condition monitoring;
  • Localization and object tracking in smart cities;
  • Machine learning applications;
  • Complex machine and system analysis using multiple sensors;
  • Techniques for online, real-time system condition monitoring.

Prof. Dr. Rafal Burdzik
Dr. Minvydas Ragulskis
Dr. Maosen Cao
Dr. Radosław Zimroz
Dr. Chaari Fakher
Dr. Łukasz Konieczny
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine diagnosis
  • systems condition monitoring
  • sensors
  • sensing technology
  • smart city
  • IoT

Published Papers (27 papers)

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Article
Controlling an Industrial Robot Using a Graphic Tablet in Offline and Online Mode
Sensors 2021, 21(7), 2439; https://doi.org/10.3390/s21072439 - 01 Apr 2021
Viewed by 514
Abstract
The article presents the possibility of using a graphics tablet to control an industrial robot. The paper presents elements of software development for offline and online control of a robot. The program for the graphic tablet and the operator interface was developed in [...] Read more.
The article presents the possibility of using a graphics tablet to control an industrial robot. The paper presents elements of software development for offline and online control of a robot. The program for the graphic tablet and the operator interface was developed in C# language in Visual Studio environment, while the program controlling the industrial robot was developed in RAPID language in the RobotStudio environment. Thanks to the development of a digital twin of the real robotic workstation, tests were carried out on the correct functioning of the application in offline mode (without using the real robot). The obtained results were verified in online mode (on a real production station). The developed computer programmes have a modular structure, which makes it possible to easily adapt them to one’s needs. The application allows for changing the parameters of the robot and the parameters of the path drawing. Tests were carried out on the influence of the sampling frequency and the tool diameter on the quality of the reconstructed trajectory of the industrial robot. The results confirmed the correctness of the application. Thanks to the new method of robot programming, it is possible to quickly modify the path by the operator, without the knowledge of robot programming languages. Further research will focus on analyzing the influence of screen resolution and layout scale on the accuracy of trajectory generation. Full article
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Article
Module for Monitoring the Probe-Skin Contact Force in the Study of Vibration Perception on the Wrist
Sensors 2021, 21(6), 2128; https://doi.org/10.3390/s21062128 - 18 Mar 2021
Viewed by 368
Abstract
This paper presents a module for monitoring the contact force between a probe for measuring vibration perception on the wrist and the skin. The module was designed for an original measuring stand for the automatic testing of the vibrotactile discrimination thresholds using the [...] Read more.
This paper presents a module for monitoring the contact force between a probe for measuring vibration perception on the wrist and the skin. The module was designed for an original measuring stand for the automatic testing of the vibrotactile discrimination thresholds using the psychophysical adaptive method of 1 up–2 down with two or three interval forced choices (2IFC, 3IFC). Measurement methods were implemented in LabVIEW software. The inspiration for the project was the need to check the possibility of building a vibrating interface for transmitting information through vibrations delivered to the wrist via a bracelet. The test procedure on the wrist is not standardized; however, during its development, the recommendations of the Polish Norm–International Organization for Standardization PN-ISO 13091-1, 2006 were adopted. This standard contains methods for measuring vibration sensation thresholds on the fingertips for the assessment of neural dysfunction. The key to the repeatability of measurements seems to be the ability to continuously control the pressure of the measuring probe on the skin. This article compares two solutions for measuring the contact force along with an analysis of their accuracy and the impact of vibrations on the measured values. Moreover, the results of measurements of vibrotactile amplitude and frequency discrimination thresholds obtained on the ventral wrist at five frequencies (25, 32, 63, 125 and 250 Hz) are presented. Full article
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Article
Issues of Data Acquisition and Interpretation of Paraseismic Measuring Signals Triggered by the Detonation of Explosive Charges
Sensors 2021, 21(4), 1290; https://doi.org/10.3390/s21041290 - 11 Feb 2021
Viewed by 433
Abstract
The paper tackles the issues of data acquisition during the measuring of vibrations caused by the detonation of explosive charges in various types of works (blasting in mines, demolition works, tunneling). Depending on the placement of an explosive charge (a charge detonated on [...] Read more.
The paper tackles the issues of data acquisition during the measuring of vibrations caused by the detonation of explosive charges in various types of works (blasting in mines, demolition works, tunneling). Depending on the placement of an explosive charge (a charge detonated on the surface or a charge placed in a hole), it triggers side effects in the form of mechanical vibrations, which are propagated in the environment and may pose a hazard to buildings. In the case of propagation in the air, there is an acoustic wave and an airblast wave. For the assessment analysis on the impact of vibrations on buildings, a ground-propagated signal is used, while what is propagated by air is a disturbance. Selected examples in the paper demonstrate how an acoustic wave and an airblast wave interferes with the signal recorded by geophones. Afterwards, the paper presents the results of the tests conducted at a training area, during which various masses of explosive charges placed in different ways were detonated. The examples demonstrate that this interference may lead to the misinterpretation of recorded measurements. This paper is the first of two papers that will present the results of research into this matter and the suggested resolutions in order to eliminate this interference. Full article
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Article
Use of Bispectrum Analysis to Inspect the Non-Linear Dynamic Characteristics of Beam-Type Structures Containing a Breathing Crack
Sensors 2021, 21(4), 1177; https://doi.org/10.3390/s21041177 - 07 Feb 2021
Cited by 1 | Viewed by 488
Abstract
A breathing crack is a typical form of structural damage attributed to long-term dynamic loads acting on engineering structures. Traditional linear damage identification methods suffer from the loss of valuable information when structural responses are essentially non-linear. To deal with this issue, bispectrum [...] Read more.
A breathing crack is a typical form of structural damage attributed to long-term dynamic loads acting on engineering structures. Traditional linear damage identification methods suffer from the loss of valuable information when structural responses are essentially non-linear. To deal with this issue, bispectrum analysis is employed to study the non-linear dynamic characteristics of a beam structure containing a breathing crack, from the perspective of numerical simulation and experimental validation. A finite element model of a cantilever beam is built with contact elements to simulate a breathing crack. The effects of crack depth and location, excitation frequency and magnitude, and measurement noise on the non-linear behavior of the beam are studied systematically. The result demonstrates that bispectral analysis can effectively identify non-linear damage in different states with strong noise immunity. Compared with existing methods, the bispectral non-linear analysis can efficiently extract non-linear features of a breathing crack, and it can overcome the limitations of existing linear damage detection methods used for non-linear damage detection. This study’s outcome provides a theoretical basis and a paradigm for damage identification in cracked structures. Full article
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Article
Structural Damage Identification Based on Integrated Utilization of Inverse Finite Element Method and Pseudo-Excitation Approach
Sensors 2021, 21(2), 606; https://doi.org/10.3390/s21020606 - 16 Jan 2021
Viewed by 721
Abstract
The attempt to integrate the applications of conventional structural deformation reconstruction strategies and vibration-based damage identification methods is made in this study, where, more specifically, the inverse finite element method (iFEM) and pseudo-excitation approach (PE) are combined for the first time, to give [...] Read more.
The attempt to integrate the applications of conventional structural deformation reconstruction strategies and vibration-based damage identification methods is made in this study, where, more specifically, the inverse finite element method (iFEM) and pseudo-excitation approach (PE) are combined for the first time, to give rise to a novel structural health monitoring (SHM) framework showing various advantages, particularly in aspects of enhanced adaptability and robustness. As the key component of the method, the inverse finite element method (iFEM) enables precise reconstruction of vibration displacements based on measured dynamic strains, which, as compared to displacement measurement, is much more adaptable to existing on-board SHM systems in engineering practice. The PE, on the other hand, is applied subsequently, relying on the reconstructed displacements for the identification of structural damage. Delamination zones in a carbon fibre reinforced plastic (CFRP) laminate are identified using the developed method. As demonstrated by the damage detection results, the iFEM-PE method possesses apparently improved accuracy and significantly enhanced noise immunity compared to the original PE approach depending on displacement measurement. Extensive parametric study is conducted to discuss the influence of a variety of factors on the effectiveness and accuracy of damage identification, including the influence of damage size and position, measurement density, sensor layout, vibration frequency and noise level. It is found that different factors are highly correlated and thus should be considered comprehensively to achieve optimal detection results. The application of the iFEM-PE method is extended to better adapt to the structural operational state, where multiple groups of vibration responses within a wide frequency band are used. Hybrid data fusion is applied to process the damage index (DI) constructed based on the multiple responses, leading to detection results capable of indicating delamination positions precisely. Full article
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Article
Model of the Vibration Signal of the Vibrating Sieving Screen Suspension for Condition Monitoring Purposes
Sensors 2021, 21(1), 213; https://doi.org/10.3390/s21010213 - 31 Dec 2020
Cited by 1 | Viewed by 613
Abstract
Diagnostics of industrial machinery is a topic related to the need for damage detection, but it also allows to understand the process itself. Proper knowledge about the operational process of the machine, as well as identification of the underlying components, is critical for [...] Read more.
Diagnostics of industrial machinery is a topic related to the need for damage detection, but it also allows to understand the process itself. Proper knowledge about the operational process of the machine, as well as identification of the underlying components, is critical for its diagnostics. In this paper, we present a model of the signal, which describes vibrations of the sieving screen. This particular type is used in the mining industry for the classification of ore pieces in the material stream by size. The model describes the real vibration signal measured on the spring set being the suspension of this machine. This way, it is expected to help in better understanding how the overall motion of the machine can impact the efforts of diagnostics. The analysis of real vibration signals measured on the screen allowed to identify and parameterize the key signal components, which carry valuable information for the following stages of diagnostic process of that machine. In the proposed model we take into consideration deterministic components related to shaft rotation, stochastic Gaussian component related to external noise, stochastic α-stable component as a model of excitations caused by falling rocks pieces, and identified machine response to unitary excitations. Full article
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Article
Local Defect Detection in Bearings in the Presence of Heavy-Tailed Noise and Spectral Overlapping of Informative and Non-Informative Impulses
Sensors 2020, 20(22), 6444; https://doi.org/10.3390/s20226444 - 11 Nov 2020
Cited by 1 | Viewed by 676
Abstract
The problem of the informative frequency band (IFB) selection for local fault detection is considered in the paper. There are various approaches that are very effective in this issue. Most of the techniques are vibration-based and they are related to the cyclic impulses [...] Read more.
The problem of the informative frequency band (IFB) selection for local fault detection is considered in the paper. There are various approaches that are very effective in this issue. Most of the techniques are vibration-based and they are related to the cyclic impulses detection (associated with the local fault) in the background noise. However, when the background noise in the vibration signal has non-Gaussian impulsive behavior, the classical methods seem to be insufficient. Recently, new techniques were proposed by several authors and interesting approaches were tested for different non-Gaussian signals. We demonstrate the comparative analysis related to the results for three selected techniques proposed in recent years, namely the Alpha selector, Conditional Variance-based selector, and Spearman selector. The techniques seem to be effective for the IFB selection for the non-Gaussian distributed vibration signals. The main purpose of this article is to investigate how spectral overlapping of informative and non-informative impulsive components will affect diagnostic procedures. According to our knowledge, this problem was not considered in the literature for the non-Gaussian signals. Nevertheless, as we demonstrated by the simulations, the level of overlapping and the location of a center frequency of the mentioned frequency bands have a significant influence on the behavior of the considered selectors. The discussion about the effectiveness of each analyzed method is conducted. The considered problem is supported by real-world examples. Full article
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Article
Analysis on the Possibility of Eliminating Interference from Paraseismic Vibration Signals Induced by the Detonation of Explosive Materials
Sensors 2020, 20(21), 6401; https://doi.org/10.3390/s20216401 - 09 Nov 2020
Cited by 1 | Viewed by 614
Abstract
This article presents the results of studies on the impact of acoustic waves on geophones and microphones used to measure airblasts carried out in a reverberation chamber. During the tests, a number of test signals were generated, of which two are presented in [...] Read more.
This article presents the results of studies on the impact of acoustic waves on geophones and microphones used to measure airblasts carried out in a reverberation chamber. During the tests, a number of test signals were generated, of which two are presented in this article: frequency-modulated sine (sine sweep) waves in the 30–300 Hz range, and the result of detonating 3 g of pyrotechnic material inside the chamber. Then, based on the short-time Fourier transform, the spectral subtraction method was used to remove unwanted disruption interfering with the recorded signal. Using MATLAB software, a program was written that was calibrated and adapted to the specifics of the measuring equipment based on the collected test results. As a result, it was possible to clean the signals of interference and obtain a vibration signal propagated by the substrate. The results are based on signals registered in the laboratory and made in field conditions during the detonation of explosive materials. Full article
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Article
Industrial Robot Control by Means of Gestures and Voice Commands in Off-Line and On-Line Mode
Sensors 2020, 20(21), 6358; https://doi.org/10.3390/s20216358 - 07 Nov 2020
Cited by 3 | Viewed by 907
Abstract
The paper presents the possibility of using the Kinect v2 module to control an industrial robot by means of gestures and voice commands. It describes the elements of creating software for off-line and on-line robot control. The application for the Kinect module was [...] Read more.
The paper presents the possibility of using the Kinect v2 module to control an industrial robot by means of gestures and voice commands. It describes the elements of creating software for off-line and on-line robot control. The application for the Kinect module was developed in the C# language in the Visual Studio environment, while the industrial robot control program was developed in the RAPID language in the RobotStudio environment. The development of a two-threaded application in the RAPID language allowed separating two independent tasks for the IRB120 robot. The main task of the robot is performed in Thread No. 1 (responsible for movement). Simultaneously, Thread No. 2 ensures continuous communication with the Kinect system and provides information about the gesture and voice commands in real time without any interference in Thread No. 1. The applied solution allows the robot to work in industrial conditions without the negative impact of the communication task on the time of the robot’s work cycles. Thanks to the development of a digital twin of the real robot station, tests of proper application functioning in off-line mode (without using a real robot) were conducted. The obtained results were verified on-line (on the real test station). Tests of the correctness of gesture recognition were carried out, and the robot recognized all programmed gestures. Another test carried out was the recognition and execution of voice commands. A difference in the time of task completion between the actual and virtual station was noticed; the average difference was 0.67 s. The last test carried out was to examine the impact of interference on the recognition of voice commands. With a 10 dB difference between the command and noise, the recognition of voice commands was equal to 91.43%. The developed computer programs have a modular structure, which enables easy adaptation to process requirements. Full article
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Article
Application of Wireless Accelerometer Mounted on Wheel Rim for Parked Car Monitoring
Sensors 2020, 20(21), 6088; https://doi.org/10.3390/s20216088 - 26 Oct 2020
Cited by 1 | Viewed by 672
Abstract
Damages of different kinds that can be inflicted to a parked car. Among them, loosening of the car wheel bolts is difficult to detect during normal use of the car and is at the same time very dangerous to the health and life [...] Read more.
Damages of different kinds that can be inflicted to a parked car. Among them, loosening of the car wheel bolts is difficult to detect during normal use of the car and is at the same time very dangerous to the health and life of the driver. Moreover, in patents and publications, only little information is presented about electronic sensors available for activation from inside of the car to inform the driver about the mentioned dangerous situation. Thus, the main aim of this work is the proposition and examination of a sensing device using of a wireless accelerometer head to detect loosening of wheel fixing bolts before ride has been started. The proposed sensing device consists of a wireless accelerometer head, an assembly interface and a receiver unit. The assembly interface between the head and the inner part of the rim enables the correct operation of the system. The data processing algorithm developed for the receiver unit enables the proper detection of the unscrewing of bolts. Moreover, the tested algorithm is resistant to the interference signals generated in the accelerometer head by cars and men passing in close distance. Full article
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Article
Effect of Loading Rate on Tensile and Failure Behavior of Concrete
Sensors 2020, 20(21), 5994; https://doi.org/10.3390/s20215994 - 22 Oct 2020
Viewed by 515
Abstract
Three-point bending experiments of concrete beams were conducted under the strain rate range of 10−6 s−1 and 1.5 × 10−3 s−1. A novel 3D laser scanner, Handy SCAN, was employed to detect the areas of interface, mortar and [...] Read more.
Three-point bending experiments of concrete beams were conducted under the strain rate range of 10−6 s−1 and 1.5 × 10−3 s−1. A novel 3D laser scanner, Handy SCAN, was employed to detect the areas of interface, mortar and aggregate on the crack surface after the experiment. In this paper, the inhomogeneity of materials and the inertial effect were considered as the main factors in the strength enhancement of concrete together with a proposed dynamic model. With the obtained experimental results, the initial elastic modulus and tensile strength of concrete showed obvious rate sensitivity. Moreover, an empirical relationship of dynamic increase factor and strain rate was established for the strain rate range of 10−6 s−1 and 1.5 × 10−3 s−1. The contributions of aggregate and inertia effect to the dynamic enhancement of concrete strength were quantified with respect to the loading rate. The rate effect of concrete obtained by the experiments was verified by the finite element analysis on the mesoscopic scale with the model built by the three-dimensional random aggregate software. Full article
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Article
Assessment of the Road Surface Condition with Longitudinal Acceleration Signal of the Car Body
Sensors 2020, 20(21), 5987; https://doi.org/10.3390/s20215987 - 22 Oct 2020
Viewed by 452
Abstract
On the basis of road tests, the authors assessed the feasibility of the vehicle body acceleration values for the purposes of assessing road surface characteristics in terms of its roughness. Short-term Fourier Transform (STFT) was used for the analysis of the recorded signal. [...] Read more.
On the basis of road tests, the authors assessed the feasibility of the vehicle body acceleration values for the purposes of assessing road surface characteristics in terms of its roughness. Short-term Fourier Transform (STFT) was used for the analysis of the recorded signal. The spectra obtained in successive frequency bands demonstrate the amplitudes originating from the natural vibrations of the rolling wheel and forces resulting from the interaction with the road roughness. The article focuses on the relationships between the road roughness and the ratios of individual amplitudes in a specific frequency band of the vehicle body acceleration values. Amplitude values derived on the basis of successive windows were averaged for analogous, arbitrarily assumed local frequency bands. The value characterizing the road surface condition provided the information regarding the mean amplitude value in specific frequency ranges depending on the instantaneous velocity of the car body and the condition of the road surface on which it was moving. In cases where the road was free of any visible roughness, the obtained mean amplitude value in the analyzed spectrum window, for the adopted vehicle velocity range from 50 km h to 100 km/h, did not exceed 0.02 m/s2. It was also demonstrated that the road surface roughness leads to an increase in the mean amplitude value from 0.07 m/s2 to 0.16 m/s2. Full article
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Article
Decision Tree-Based Classification for Planetary Gearboxes’ Condition Monitoring with the Use of Vibration Data in Multidimensional Symptom Space
Sensors 2020, 20(21), 5979; https://doi.org/10.3390/s20215979 - 22 Oct 2020
Cited by 3 | Viewed by 937
Abstract
Monitoring the condition of rotating machinery, especially planetary gearboxes, is a challenging problem. In most of the available approaches, diagnostic procedures are related to advanced signal pre-processing/feature extraction methods or advanced data (features) analysis by using artificial intelligence. In this paper, the second [...] Read more.
Monitoring the condition of rotating machinery, especially planetary gearboxes, is a challenging problem. In most of the available approaches, diagnostic procedures are related to advanced signal pre-processing/feature extraction methods or advanced data (features) analysis by using artificial intelligence. In this paper, the second approach is explored, so an application of decision trees for the classification of spectral-based 15D vectors of diagnostic data is proposed. The novelty of this paper is that by a combination of spectral analysis and the application of decision trees to a set of spectral features, we are able to take advantage of the multidimensionality of diagnostic data and classify/recognize the gearbox condition almost faultlessly even in non-stationary operating conditions. The diagnostics of time-varying systems are a complicated issue due to time-varying probability densities estimated for features. Using multidimensional data instead of an aggregated 1D feature, it is possible to improve the efficiency of diagnostics. It can be underlined that in comparison to previous work related to the same data, where the aggregated 1D variable was used, the efficiency of the proposed approach is around 99% (ca. 19% better). We tested several algorithms: classification and regression trees with the Gini index and entropy, as well as the random tree. We compare the obtained results with the K-nearest neighbors classification algorithm and meta-classifiers, namely: random forest and AdaBoost. As a result, we created the decision tree model with 99.74% classification accuracy on the test dataset. Full article
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Article
Development of Insert Condition Classification System for CNC Lathes Using Power Spectral Density Distribution of Accelerometer Vibration Signals
Sensors 2020, 20(20), 5907; https://doi.org/10.3390/s20205907 - 19 Oct 2020
Viewed by 532
Abstract
Insert conditions significantly influence the product quality and manufacturing efficiency of lathe machining. This study used the power spectral density distribution of the vibration signals of a lathe machining accelerometer to design an insert condition classification system applicable to different machining conditions. For [...] Read more.
Insert conditions significantly influence the product quality and manufacturing efficiency of lathe machining. This study used the power spectral density distribution of the vibration signals of a lathe machining accelerometer to design an insert condition classification system applicable to different machining conditions. For four common lathe machining insert conditions (i.e., built-up edge, flank wear, normal, and fracture), herein, the insert condition classification system was established with two stages—insert condition modeling and machining model fusion. In the insert condition modeling stage, the magnitude features of the segmented frequencies were captured according to the power spectral density distributions of the accelerometer vibration signals. Principal component analysis and backpropagation neural networks were used to develop insert condition models for different machining conditions. In the machining model fusion stage, a backpropagation neural network was employed to establish the weight function between the machining conditions and insert condition models. Subsequently, the insert conditions were classified based on the calculated weight values of all the insert condition models. Cutting tests were performed on a computer numerical control (CNC) lathe and utilized to validate the feasibility of the designed insert condition classification system. The results of the cutting tests showed that the designed system could perform insert condition classification under different machining conditions, with a classification rate exceeding 80%. Using a triaxial accelerometer, the designed insert condition classification system could perform identification and classification online for four common insert conditions under different machining conditions, ensuring that CNC lathes could further improve manufacturing quality and efficiency in practice. Full article
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Article
Identification, Decomposition and Segmentation of Impulsive Vibration Signals with Deterministic Components—A Sieving Screen Case Study
Sensors 2020, 20(19), 5648; https://doi.org/10.3390/s20195648 - 02 Oct 2020
Cited by 4 | Viewed by 765
Abstract
Condition monitoring is a well-established field of research; however, for industrial applications, one may find some challenges. They are mostly related to complex design, a specific process performed by the machine, time-varying load/speed conditions, and the presence of non-Gaussian noise. A procedure for [...] Read more.
Condition monitoring is a well-established field of research; however, for industrial applications, one may find some challenges. They are mostly related to complex design, a specific process performed by the machine, time-varying load/speed conditions, and the presence of non-Gaussian noise. A procedure for vibration analysis from the sieving screen used in the raw material industry is proposed in the paper. It is more for pre-processing than the damage detection procedure. The idea presented here is related to identification and extraction of two main types of components: (i) deterministic (D)—related to the unbalanced shaft(s) and (ii) high amplitude, impulsive component randomly (R) appeared in the vibration due to pieces of ore falling down of moving along the deck. If we could identify these components, then we will be able to perform classical diagnostic procedures for local damage detection in rolling element bearing. As deterministic component may be AM/FM modulated and each impulse may appear with different amplitude and damping, there is a need for an automatic procedure. We propose a method for signal processing that covers two main steps: (a) related to R/D decomposition and including signal segmentation to neglect AM/FM modulations, iterative sine wave fitting using the least square method (for each segment), signal filtering technique by subtraction fitted sine from the raw signal, the definition of the criterion to stop iteration by residuals analysis, (b) impulse segmentation and description (beginning, end, max amplitude) that contains: detection of the number of impulses in a decomposed random part of the raw signal, detection of the max value of each impulse, statistical analysis (probability density function) of max value to find regime-switching), modeling of the envelope of each impulse for samples that protrude from the signal, extrapolation (forecasting) envelope shape for samples hidden in the signal. The procedure is explained using simulated and real data. Each step is very easy to implement and interpret thus the method may be used in practice in a commercial system. Full article
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Article
Deep Quality Assessment of a Solar Reflector Based on Synthetic Data: Detecting Surficial Defects from Manufacturing and Use Phase
Sensors 2020, 20(19), 5481; https://doi.org/10.3390/s20195481 - 24 Sep 2020
Viewed by 639
Abstract
Vision technologies are used in both industrial and smart city applications in order to provide advanced value products due to embedded self-monitoring and assessment services. In addition, for the full utilization of the obtained data, deep learning is now suggested for use. To [...] Read more.
Vision technologies are used in both industrial and smart city applications in order to provide advanced value products due to embedded self-monitoring and assessment services. In addition, for the full utilization of the obtained data, deep learning is now suggested for use. To this end, the current work presents the implementation of image recognition techniques alongside the original the quality assessment of a Parabolic Trough Collector (PTC) reflector surface to locate and identify surface irregularities by classifying images as either acceptable or non-acceptable. The method consists of a three-step solution that promotes an affordable implementation in a relatively small time period. More specifically, a 3D Computer Aided Design (CAD) of the PTC was used for the pre-training of neural networks, while an aluminum reflector surface was used to verify algorithm performance. The results are promising, as this method proved applicable in cases where the actual part was manufactured in small batches or under the concept of customized manufacturing. Consequently, the algorithm is capable of being trained with a limited number of data. Full article
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Article
Selection and Optimization of the Parameters of the Robotized Packaging Process of One Type of Product
Sensors 2020, 20(18), 5378; https://doi.org/10.3390/s20185378 - 19 Sep 2020
Cited by 4 | Viewed by 888
Abstract
The article presents the results of computer simulations related to the selection and optimization of the parameters of robotic packing process of one type of product. Taking the required performance of the robotic production line as a basis, we proposed its configuration using [...] Read more.
The article presents the results of computer simulations related to the selection and optimization of the parameters of robotic packing process of one type of product. Taking the required performance of the robotic production line as a basis, we proposed its configuration using the RobotStudio environment for offline robot programming and virtual controller technology. Next, a methodology for the validation of the adopted assumptions was developed, based on a wide range of input data and a precise representation of the applicable conditions in the packaging process of one type of product. This methodology included test scenarios repeated an appropriate number of times in order to obtain the result data with the desired reliability and repeatability. The main element of the research was a computer simulation of the station based on the Picking PowerPac package. It was assumed that the products on the technological line are generated pseudo-randomly, thus reflecting the real working conditions. The result of the conducted works is the optimal operating speed of industrial robots and conveyors. The developed methodology allows for multifaceted analyses of the key parameters of the technological process (e.g., the number of active robots and their load, speed of conveyors, and station efficiency). We paid special attention to the occurrence of anomalies, i.e., emergency situations in the form of “halting” the operation of chosen robots and their impact on the obtained quality of the industrial process. As a result of the simulations, numerical values were obtained, maximum efficiency, with regard to maximum overflow of items of 5%, for LB algorithm was equal to 1188 completed containers per hour, with conveyors speeds of 270 mm/s and 165 mm/s. This efficiency was possible at robot speeds R1 = 6450 mm/s, R2 = 7500 mm/s, R3 = 6500 mm/s, R4 = 6375 mm/s, R5 = 5500 mm/s, R6 = 7200 mm/s. The ATC algorithm reached efficiency of 1332 containers per hour with less than 5% overflown items, with conveyor speeds of 310 mm/s and 185 mm/s. This efficiency was possible at robot speeds R1 = 7500 mm/s, R2 = 7500 mm/s, R3 = 7200 mm/s, R4 = 7000 mm/s, R5 = 6450 mm/s, R6 = 6300 mm/s. Tests carried out for emergency situations showed that the LB algorithm does not allow for automatic continuation of the process, while the ATC algorithm assured production efficiency of 94% to 98% of the maximum station efficiency. Full article
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Article
Electric Current Waveform of the Injector as a Source of Diagnostic Information
Sensors 2020, 20(15), 4151; https://doi.org/10.3390/s20154151 - 26 Jul 2020
Viewed by 574
Abstract
The article discusses the method of evaluation of the fuel injector operation based on the observation of the electric current parameters, which were measured with a current transducer using the Hall effect, during the dosing process. This method relies on comparison of the [...] Read more.
The article discusses the method of evaluation of the fuel injector operation based on the observation of the electric current parameters, which were measured with a current transducer using the Hall effect, during the dosing process. This method relies on comparison of the electric current-related values of the examined injector with the model characteristics, which are representing the properly functioning injector. A model of the fuel injector in the form of the electric current waveform that describes the changes in the electric current and voltage during its work is presented in this article. Complex equations describing the fuel injector model under discussion account for the characteristics of the current variations, with no damage-induced modifications. Due to these, the modeled electric current/voltage waveform mirrors the real conditions. The use of a mathematical model describing the voltage–current phenomena occurring during the injector operation allows determining the actual beginning and duration of the injection. The model can also be used to develop new injector diagnostic methods that can be implemented in the engine controller (ECU). Full article
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Article
Automated Calibration System for Digital Multimeters Not Equipped with a Communication Interface
Sensors 2020, 20(13), 3650; https://doi.org/10.3390/s20133650 - 29 Jun 2020
Viewed by 605
Abstract
This article is focused on the calibration of digital multimeters, in which the concept and practical solutions for stations with software for automatic calibration are presented. This paper also presents the general structure of the measuring system, the application scheme, and the technical [...] Read more.
This article is focused on the calibration of digital multimeters, in which the concept and practical solutions for stations with software for automatic calibration are presented. This paper also presents the general structure of the measuring system, the application scheme, and the technical implementation of measuring stations, together with the software. Full article
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Article
Research on the Single-Value Indicators for Centrifugal Pump Based on Vibration Signals
Sensors 2020, 20(11), 3283; https://doi.org/10.3390/s20113283 - 09 Jun 2020
Viewed by 671
Abstract
Off-design operation conditions might not only seriously affect the internal flow status of a centrifugal pump, but also result in additional energy loss and potential mechanical damage. Therefore, early-stage monitoring and predication on off-design operation conditions for centrifugal pumps have become essential. Single-value [...] Read more.
Off-design operation conditions might not only seriously affect the internal flow status of a centrifugal pump, but also result in additional energy loss and potential mechanical damage. Therefore, early-stage monitoring and predication on off-design operation conditions for centrifugal pumps have become essential. Single-value indicators have favorable factors such as a smaller amount of calculation and easier identification. As a result, industries prefer the more straightforward approach: obtaining single-value indicators directly from the signals which could be easier compared with accepted standards. The possibility of applying the single-value indicators of vibration into operation condition monitoring for a centrifugal pump is studied theoretically and experimentally, which shows that the statistical features of vibration might be suitable for hydraulic instability detection for a centrifugal pump. Full article
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Article
A Simple Condition Monitoring Method for Gearboxes Operating in Impulsive Environments
Sensors 2020, 20(7), 2115; https://doi.org/10.3390/s20072115 - 09 Apr 2020
Cited by 5 | Viewed by 877
Abstract
Reliable condition indicators are necessary to perform effective diagnosis and prognosis. However, the vibration signals are often corrupted with non-Gaussian noise and rotating machines may operate under time-varying operating conditions. This impedes the application of conventional condition indicators. The synchronous average of the [...] Read more.
Reliable condition indicators are necessary to perform effective diagnosis and prognosis. However, the vibration signals are often corrupted with non-Gaussian noise and rotating machines may operate under time-varying operating conditions. This impedes the application of conventional condition indicators. The synchronous average of the squared envelope is a relatively simple yet effective method to perform fault detection, fault identification and fault trending under constant and time-varying operating conditions. However, its performance is impeded by the presence of impulsive signal components attributed to impulsive noise or the presence of other damage modes in the machine. In this work, it is proposed that the synchronous median of the squared envelope should be used instead of the synchronous average of the squared envelope for gearbox fault diagnosis. It is shown on numerical and experimental datasets that the synchronous median is more robust to the presence of impulsive signal components and is therefore more reliable for estimating the condition of specific machine components. Full article
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Article
Multi-objective Informative Frequency Band Selection Based on Negentropy-induced Grey Wolf Optimizer for Fault Diagnosis of Rolling Element Bearings
Sensors 2020, 20(7), 1845; https://doi.org/10.3390/s20071845 - 26 Mar 2020
Cited by 5 | Viewed by 923
Abstract
Informative frequency band (IFB) selection is a challenging task in envelope analysis for the localized fault detection of rolling element bearings. In previous studies, it was often conducted with a single indicator, such as kurtosis, etc., to guide the automatic selection. However, in [...] Read more.
Informative frequency band (IFB) selection is a challenging task in envelope analysis for the localized fault detection of rolling element bearings. In previous studies, it was often conducted with a single indicator, such as kurtosis, etc., to guide the automatic selection. However, in some cases, it is difficult for that to fully depict and balance the fault characters from impulsiveness and cyclostationarity of the repetitive transients. To solve this problem, a novel negentropy-induced multi-objective optimized wavelet filter is proposed in this paper. The wavelet parameters are determined by a grey wolf optimizer with two independent objective functions i.e., maximizing the negentropy of squared envelope and squared envelope spectrum to capture impulsiveness and cyclostationarity, respectively. Subsequently, the average negentropy is utilized in identifying the IFB from the obtained Pareto set, which are non-dominated by other solutions to balance the impulsive and cyclostationary features and eliminate the background noise. Two cases of real vibration signals with slight bearing faults are applied in order to evaluate the performance of the proposed methodology, and the results demonstrate its effectiveness over some fast and optimal filtering methods. In addition, its stability in tracking the IFB is also tested by a case of condition monitoring data sets. Full article
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Review

Jump to: Research, Other

Review
A Critical Review of Nonlinear Damping Identification in Structural Dynamics: Methods, Applications, and Challenges
Sensors 2020, 20(24), 7303; https://doi.org/10.3390/s20247303 - 19 Dec 2020
Cited by 1 | Viewed by 641
Abstract
In recent decades, nonlinear damping identification (NDI) in structural dynamics has attracted wide research interests and intensive studies. Different NDI strategies, from conventional to more advanced, have been developed for a variety of structural types. With apparent advantages over classical linear methods, these [...] Read more.
In recent decades, nonlinear damping identification (NDI) in structural dynamics has attracted wide research interests and intensive studies. Different NDI strategies, from conventional to more advanced, have been developed for a variety of structural types. With apparent advantages over classical linear methods, these strategies are able to quantify the nonlinear damping characteristics, providing powerful tools for the analysis and design of complex engineering structures. Since the current trend in many applications tends to more advanced and sophisticated applications, it is of great necessity to work on developing these methods to keep pace with this progress. Moreover, NDI can provide an effective and promising tool for structural damage detection purposes, where the changes in the dynamic features of structures can be correlated with damage levels. This review paper provides an overview of NDI methods by explaining the fundamental challenges and potentials of these methods based on the available literature. Furthermore, this research offers a comprehensive survey of different applications and future research trends of NDI. For potential development and application work for nonlinear damping methods, the anticipated results and recommendations of the current paper can assist researchers and developers worldwide to find out the gaps and unsolved issues in the field of NDI. Full article
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Other

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Letter
Mechanical Fault Diagnostic in PMSM from Only One Current Measurement: A Tacholess Order Tracking Approach
Sensors 2020, 20(17), 5011; https://doi.org/10.3390/s20175011 - 03 Sep 2020
Viewed by 770
Abstract
This article presents a mechanical fault diagnosis methodology in synchronous machines using only a single current measurement in variable speed conditions. The proposed methodology uses order tracking in order to sample the analysis signal as a function of the rotor angle. The spectrum [...] Read more.
This article presents a mechanical fault diagnosis methodology in synchronous machines using only a single current measurement in variable speed conditions. The proposed methodology uses order tracking in order to sample the analysis signal as a function of the rotor angle. The spectrum of the signal is then independent of speed and it could be employed in frequency analysis. Order tracking is usually applied using rotor position measurement. In this work, the proposed method uses one current measurement to estimate the position as well as the analysis signal (rotation speed). Furthermore, a statistical approach is used to create a complete diagnosis protocol. At variable speed and with only one current measurement the diagnosis is challenging. However, order tracking will allow simpler analysis. The method is proved in simulations and experimental set-up. Full article
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Letter
Chaotic Ensemble of Online Recurrent Extreme Learning Machine for Temperature Prediction of Control Moment Gyroscopes
Sensors 2020, 20(17), 4786; https://doi.org/10.3390/s20174786 - 25 Aug 2020
Cited by 1 | Viewed by 550
Abstract
Control moment gyroscopes (CMG) are crucial components in spacecrafts. Since the anomaly of bearing temperature of the CMG shows apparent correlation with nearly all critical fault modes, temperature prediction is of great importance for health management of CMGs. However, due to the complicity [...] Read more.
Control moment gyroscopes (CMG) are crucial components in spacecrafts. Since the anomaly of bearing temperature of the CMG shows apparent correlation with nearly all critical fault modes, temperature prediction is of great importance for health management of CMGs. However, due to the complicity of thermal environment on orbit, the temperature signal of the CMG has strong intrinsic nonlinearity and chaotic characteristics. Therefore, it is crucial to study temperature prediction under the framework of chaos time series theory. There are also several other challenges including poor data quality, large individual differences and difficulty in processing streaming data. To overcome these issues, we propose a new method named Chaotic Ensemble of Online Recurrent Extreme Learning Machine (CE-ORELM) for temperature prediction of control moment gyroscopes. By means of the CE-ORELM model, this proposed method is capable of dynamic prediction of temperature. The performance of the method was tested by real temperature data acquired from actual CMGs. Experimental results show that this method has high prediction accuracy and strong adaptability to the on-orbital temperature data with sudden variations. These superiorities indicate that the proposed method can be used for temperature prediction of control moment gyroscopes. Full article
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Letter
A Novel Approach to Condition Monitoring of the Cutting Process Using Recurrent Neural Networks
Sensors 2020, 20(16), 4493; https://doi.org/10.3390/s20164493 - 11 Aug 2020
Viewed by 693
Abstract
Condition monitoring is a fundamental part of machining, as well as other manufacturing processes where, generally, there are parts that wear out and have to be replaced. Devising proper condition monitoring has been a concern of many researchers, but there is still a [...] Read more.
Condition monitoring is a fundamental part of machining, as well as other manufacturing processes where, generally, there are parts that wear out and have to be replaced. Devising proper condition monitoring has been a concern of many researchers, but there is still a lack of robustness and efficiency, most often hindered by the system’s complexity or otherwise limited by the inherent noisy signals, a characteristic of industrial processes. The vast majority of condition monitoring approaches do not take into account the temporal sequence when modelling and hence lose an intrinsic part of the context of an actual time-dependent process, fundamental to processes such as cutting. The proposed system uses a multisensory approach to gather information from the cutting process, which is then modelled by a recurrent neural network, capturing the evolutive pattern of wear over time. The system was tested with realistic cutting conditions, and the results show great effectiveness and accuracy with just a few cutting tests. The use of recurrent neural networks demonstrates the potential of such an approach for other time-dependent industrial processes under noisy conditions. Full article
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Letter
Object-Based Thermal Image Segmentation for Fault Diagnosis of Reciprocating Compressors
Sensors 2020, 20(12), 3436; https://doi.org/10.3390/s20123436 - 18 Jun 2020
Cited by 3 | Viewed by 714
Abstract
As an essential mechanical device in many industrial applications, reciprocating compressors have a high demand for operating efficiency and availability. Because the temperature of each part of a reciprocating compressor depends considerably on operating conditions, faults in any parts will cause the variation [...] Read more.
As an essential mechanical device in many industrial applications, reciprocating compressors have a high demand for operating efficiency and availability. Because the temperature of each part of a reciprocating compressor depends considerably on operating conditions, faults in any parts will cause the variation of the temperature distribution, which provides the possibility to distinguish the fault type of reciprocating compressors by differentiating the distribution using infrared thermal imaging. In this paper, three types of common fault are laboratory experimented in an uncontrolled temperature environment. The temperature distribution signals of a reciprocating compressor are captured by a non-contact infrared camera remotely in the form of heat maps during the experimental process. Based on the temperature distribution under baseline condition, temperature fields of six main components were selected via Hue-Saturation-Value (HSV) image as diagnostic features. During the experiment, the average grayscale values of each component were calculated to form 6-dimension vectors to represent the variation of the temperature distribution. A computational efficient multiclass support vector machine (SVM) model is then used for classifying the differences of the distributions, and the classification results demonstrate that the average temperatures of six main components aided by SVM is a promising technique to diagnose the faults of reciprocating compressors under various operating conditions with a classification accuracy of more than 99%. Full article
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