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Sensor-Based Frequency, Time-Frequency, and Higher Order Signal Processing for Condition Monitoring, Structural Health Monitoring, and Non-Destructive Testing

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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 34280

Special Issue Editors

Prof. Dr. Len Gelman

E-Mail Website
Guest Editor
School of Computing and Engineering, Department of Engineering and Technology, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK
Interests: digital signal processing; structural health monitoring; condition monitoring; artificial intelligence; vibration analysis; motor current signature analysis; adaptation of diagnosis systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shuncong Zhong

E-Mail Website
Co-Guest Editor
School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350116, PR China
Interests: Optics & Terahertz; Diagnosis; Structural Health Monitoring; NDT&E
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sensor-based technologies for condition monitoring, structural health monitoring, and non-destructive testing have become very important for most industrial sectors and academic research.

The main challenges for these technologies are as follows:

  • Most industrial assets/machineries are working in non-stationary operations;
  • Most excitations of engineering structures and materials and, therefore, sensor outputs are non-stationary;
  • One of the most important industrial requirements to these technologies is an effective diagnosis at early stage of damage development.

Addressing these challenges requires novel signal processing developments, related to intelligent sensors, frequency, time–frequency, and non-linear higher-order spectral analysis of sensor data and adaptation of sensor-based technologies to non-stationary conditions, related to machineries, structures, and materials.

Therefore, this SI focuses on sensor-based technologies and systems for machineries, structures, and materials, with the main attention on novel signal processing developments, related to intelligent sensors, signal processing of sensor data, artificial intelligence for decision making, and adaptation of sensor-based technologies to non-stationary conditions, related to machineries, structures, and materials.

This Special Issue will not cover non-novel case study papers. Potential authors need to make clear statements of paper novelties that should be based on comprehensive state-of-the art reviews.

The following keywords describe this SI:

  • Frequency, time–frequency, and higher-order signal processing for sensor-based technologies and systems for condition monitoring, structural health monitoring, and non-destructive testing;
  • Artificial intelligence for sensor-based technologies and systems for condition monitoring, structural health monitoring, and non-destructive testing;
  • Sensor-based structural health monitoring technologies and systems for engineering structures;
  • Sensor-based non-destructive testing technologies and systems for materials;
  • Sensor-based condition monitoring technologies and systems for machinery and complex electromechanical assets;
  • Adaptive sensor-based technologies and systems for condition monitoring, structural health monitoring/non-destructive testing;
  • Sensor-based technologies and systems for linear and non-linear assets, structures, and materials;
  • Diagnostic feature extraction for sensor-based technologies and systems for condition monitoring, structural health monitoring, and non-destructive testing.

Prof. Dr. Len Gelman
Prof. Dr. Shuncong Zhong
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 submissions that pass pre-check are 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 2600 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.

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Published Papers (18 papers)

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Research

18 pages, 7695 KiB  
Article
Effect of Additional Mass on Natural Frequencies of Weight-Sensing Structures
by Guiyong Guo, Shuncong Zhong, Qiukun Zhang, Jianfeng Zhong and Dongming Liu
Sensors 2023, 23(17), 7585; https://doi.org/10.3390/s23177585 - 01 Sep 2023
Viewed by 744
Abstract
The phenomena of variability and interference in the natural frequencies of weight-sensing structures applied in complex working conditions must solve the problem of reducing or eliminating resonance under low-frequency vibrations to maximize stability, accuracy and reliability. The influence laws of the additional mass [...] Read more.
The phenomena of variability and interference in the natural frequencies of weight-sensing structures applied in complex working conditions must solve the problem of reducing or eliminating resonance under low-frequency vibrations to maximize stability, accuracy and reliability. The influence laws of the additional mass with relevant characteristics on the natural frequencies, which include the components of mass, stiffness and center-of-mass distribution, etc. Firstly, the theoretical formulas of the mathematical model are given based on different characteristics of the weight-sensing structure, and various combinations of additional masses on the weight-sensing structures are adjusted in the X-, Y-, and Z-directions. The key factors to be specifically considered in the theoretical formulas are discussed through simulation analysis and experimental validation. Secondly, the locking strength of the fastening screws of some components was changed, and another component was placed on the experimental platform in the experiment. The results show that the mass, center-of-mass, stiffness distribution and other factors of the additional mass have different effects on the natural frequencies, which are important for the demand for high-precision, high-stability weighing measurement. The results of this research can provide an effective scientific evaluation basis for the reliable prediction of natural frequencies. Full article
(This article belongs to the Special Issue Sensor-Based Frequency, Time-Frequency, and Higher Order Signal Processing for Condition Monitoring, Structural Health Monitoring, and Non-Destructive Testing)
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22 pages, 7781 KiB  
Article
Detection and Classification of Uniform and Concentrated Wall-Thinning Defects Using High-Order Circumferential Guided Waves and Artificial Neural Networks
by Donatas Cirtautas, Vykintas Samaitis, Liudas Mažeika and Renaldas Raišutis
Sensors 2023, 23(14), 6505; https://doi.org/10.3390/s23146505 - 18 Jul 2023
Viewed by 911
Abstract
Pipeline structures are susceptible to corrosion, leading to significant safety, environmental, and economic implications. Existing long range guided wave inspection systems often fail to detect footprints of the concentrated defects, which can lead to leakage. One way to tackle this issue is the [...] Read more.
Pipeline structures are susceptible to corrosion, leading to significant safety, environmental, and economic implications. Existing long range guided wave inspection systems often fail to detect footprints of the concentrated defects, which can lead to leakage. One way to tackle this issue is the utilization of circumferential guided waves that inspect the pipe’s cross section. However, achieving the necessary detection resolution typically necessitates the use of high-order modes hindering the inspection data interpretation. This study presents the implementation of an ultrasonic technique capable of detecting and classifying wall thinning and concentrated defects using high-order guided wave modes. The technique is based on a proposed phase velocity mapping approach, which generates a set of isolated wave modes within a specified phase velocity range. By referencing phase velocity maps obtained from defect-free stages of the pipe, it becomes possible to observe changes resulting from the presence of defects and assign those changes to the specific type of damage using artificial neural networks (ANN). The paper outlines the fundamental principles of the proposed phase velocity mapping technique and the ANN models employed for classification tasks that use synthetic data as an input. The presented results are meticulously verified using samples with artificial defects and appropriate numerical models. Through numerical modeling, experimental verification, and analysis using ANN, the proposed method demonstrates promising outcomes in defect detection and classification, providing a more comprehensive assessment of wall thinning and concentrated defects. The model achieved an average prediction accuracy of 92% for localized defects, 99% for defect-free cases, and 98% for uniform defects. Full article
(This article belongs to the Special Issue Sensor-Based Frequency, Time-Frequency, and Higher Order Signal Processing for Condition Monitoring, Structural Health Monitoring, and Non-Destructive Testing)
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45 pages, 24207 KiB  
Article
Novel Investigation of Higher Order Spectral Technologies for Fault Diagnosis of Motor-Based Rotating Machinery
by Tomasz Ciszewski, Len Gelman, Andrew Ball, Abdulmumeen Onimisi Abdullahi, Biebele Jamabo and Michal Ziolko
Sensors 2023, 23(7), 3731; https://doi.org/10.3390/s23073731 - 04 Apr 2023
Cited by 1 | Viewed by 1100
Abstract
In the last decade, research centered around the fault diagnosis of rotating machinery using non-contact techniques has been significantly on the rise. For the first time worldwide, innovative techniques for the diagnosis of rotating machinery, based on electrical motors, including generic, nonlinear, higher-order [...] Read more.
In the last decade, research centered around the fault diagnosis of rotating machinery using non-contact techniques has been significantly on the rise. For the first time worldwide, innovative techniques for the diagnosis of rotating machinery, based on electrical motors, including generic, nonlinear, higher-order cross-correlations of spectral moduli of the third and fourth order (CCSM3 and CCSM4, respectively), have been comprehensively validated by modeling and experiments. The existing higher-order cross-correlations of complex spectra are not sufficiently effective for the fault diagnosis of rotating machinery. The novel technology CCSM3 was comprehensively experimentally validated for induction motor bearing diagnosis via motor current signals. Experimental results, provided by the validated technology, confirmed high overall probabilities of correct diagnosis for bearings at early stages of damage development. The novel diagnosis technologies were compared with existing diagnosis technologies, based on triple and fourth cross-correlations of the complex spectra. The comprehensive validation and comparison of the novel cross-correlation technologies confirmed an important non-traditional novel outcome: the technologies based on cross-correlations of spectral moduli were more effective for damage diagnosis than the technologies based on cross-correlations of the complex spectra. Experimental and simulation validations confirmed a high probability of correct diagnosis via the CCSM at the early stage of fault development. The average total probability of incorrect diagnosis for the CCSM3 for all experimental results of 8 tested bearings, estimated via 6528 diagnostic features, was 1.475%. The effectiveness gains in the total probability of incorrect diagnosis for the CCSM3 in comparison with the CCCS3 were 26.8 for the experimental validation and 18.9 for the simulation validation. The effectiveness gains in the Fisher criterion for the CCSM3 in comparison with the CCCS3 were 50.7 for the simulation validation and 104.7 for the experimental validation. Full article
(This article belongs to the Special Issue Sensor-Based Frequency, Time-Frequency, and Higher Order Signal Processing for Condition Monitoring, Structural Health Monitoring, and Non-Destructive Testing)
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17 pages, 4802 KiB  
Communication
Novel Fault Diagnosis of a Conveyor Belt Mis-Tracking via Motor Current Signature Analysis
by Mohamed Habib Farhat, Len Gelman, Abdulmumeen Onimisi Abdullahi, Andrew Ball, Gerard Conaghan and Winston Kluis
Sensors 2023, 23(7), 3652; https://doi.org/10.3390/s23073652 - 31 Mar 2023
Cited by 1 | Viewed by 1584
Abstract
For the first time ever worldwide, this paper proposes, investigates, and validates, by multiple experiments, a new online automatic diagnostic technology for the belt mis-tracking of belt conveyor systems based on motor current signature analysis (MCSA). Three diagnostic technologies were investigated, experimentally evaluated, [...] Read more.
For the first time ever worldwide, this paper proposes, investigates, and validates, by multiple experiments, a new online automatic diagnostic technology for the belt mis-tracking of belt conveyor systems based on motor current signature analysis (MCSA). Three diagnostic technologies were investigated, experimentally evaluated, and compared for conveyor belt mis-tracking diagnosis. The proposed technologies are based on three higher-order spectral diagnostic features: bicoherence, tricoherence, and the cross-correlation of spectral moduli of order 3 (CCSM3). The investigation of the proposed technologies via comprehensive experiments has shown that technology based on the CCSM3 is highly effective for diagnosing a conveyor belt mis-tracking via MCSA. Full article
(This article belongs to the Special Issue Sensor-Based Frequency, Time-Frequency, and Higher Order Signal Processing for Condition Monitoring, Structural Health Monitoring, and Non-Destructive Testing)
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24 pages, 829 KiB  
Article
Pseudo-Bayesian Approach for Robust Mode Detection and Extraction Based on the STFT
by Quentin Legros and Dominique Fourer
Sensors 2023, 23(1), 85; https://doi.org/10.3390/s23010085 - 22 Dec 2022
Cited by 2 | Viewed by 1203
Abstract
This paper addresses the problem of disentangling nonoverlapping multicomponent signals from their observation being possibly contaminated by external additive noise. We aim to extract and to retrieve the elementary components (also called modes) present in an observed nonstationary mixture signal. To this end, [...] Read more.
This paper addresses the problem of disentangling nonoverlapping multicomponent signals from their observation being possibly contaminated by external additive noise. We aim to extract and to retrieve the elementary components (also called modes) present in an observed nonstationary mixture signal. To this end, we propose a new pseudo-Bayesian algorithm to perform the estimation of the instantaneous frequency of the signal modes from their time-frequency representation. In a second time, a detection algorithm is developed to restrict the time region where each signal component behaves, to enhance quality of the reconstructed signal. We finally deal with the presence of noise in the vicinity of the estimated instantaneous frequency by introducing a new reconstruction approach relying on nonbinary band-pass synthesis filters. We validate our methods by comparing their reconstruction performance to state-of-the-art approaches through several experiments involving both synthetic and real-world data under different experimental conditions. Full article
(This article belongs to the Special Issue Sensor-Based Frequency, Time-Frequency, and Higher Order Signal Processing for Condition Monitoring, Structural Health Monitoring, and Non-Destructive Testing)
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28 pages, 7495 KiB  
Article
Novel Diagnosis Technologies for a Lack of Oil Lubrication in Gearmotor Systems, Based on Motor Current Signature Analysis
by Mohamed Habib Farhat, Len Gelman, Gerard Conaghan, Winston Kluis and Andrew Ball
Sensors 2022, 22(23), 9507; https://doi.org/10.3390/s22239507 - 05 Dec 2022
Cited by 4 | Viewed by 1568
Abstract
Due to the wide use of gearmotor systems in industry, many diagnostic techniques have been developed/employed to prevent their failures. An insufficient lubrication of gearboxes of these machines could shorten their life and lead to catastrophic failures and losses, making it important to [...] Read more.
Due to the wide use of gearmotor systems in industry, many diagnostic techniques have been developed/employed to prevent their failures. An insufficient lubrication of gearboxes of these machines could shorten their life and lead to catastrophic failures and losses, making it important to ensure a required lubrication level. For the first time in worldwide terms, this paper proposed to diagnose a lack of gearbox oil lubrication using motor current signature analysis (MCSA). This study proposed, investigated, and experimentally validated two new technologies to diagnose a lack of lubrication of gear motor systems based on MCSA. Two new diagnostic features were extracted from the current signals of a three-phase induction motor. The effectiveness of the proposed technologies was evaluated for different gear lubrication levels and was compared for three phases of motor current signals and for a case of averaging the proposed diagnostic features over three phases. The results confirmed a high effectiveness of the proposed technologies for diagnosing a lack of oil lubrication in gearmotor systems. Other contributions were as follows: (i) it was shown for the first time in worldwide terms, that the motor current nonlinearity level increases with the reduction of the sgearbox oil level; (ii) novel experimental validations of the proposed two diagnostic technologies via comprehensive experimental trials (iii) novel experimental comparisons of the diagnosis effectiveness of the proposed two diagnostic technologies. Full article
(This article belongs to the Special Issue Sensor-Based Frequency, Time-Frequency, and Higher Order Signal Processing for Condition Monitoring, Structural Health Monitoring, and Non-Destructive Testing)
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18 pages, 8338 KiB  
Article
Novel Vision Monitoring Method Based on Multi Light Points for Space-Time Analysis of Overhead Contact Line Displacements
by Andrzej Wilk, Len Gelman, Jacek Skibicki, Slawomir Judek, Krzysztof Karwowski, Aleksander Jakubowski and Paweł Kaczmarek
Sensors 2022, 22(23), 9281; https://doi.org/10.3390/s22239281 - 29 Nov 2022
Cited by 3 | Viewed by 1225
Abstract
The article presents an innovative vision monitoring method of overhead contact line (OCL) displacement, which utilizes a set of LED light points installed along it. A light point is an, LED fed from a battery. Displacements of the LED points, recorded by a [...] Read more.
The article presents an innovative vision monitoring method of overhead contact line (OCL) displacement, which utilizes a set of LED light points installed along it. A light point is an, LED fed from a battery. Displacements of the LED points, recorded by a camera, are interpreted as a change of OCL shape in time and space. The vision system comprises a camera, properly situated with respect to the OCL, which is capable of capturing a dozen light points in its field of view. The monitoring system can be scaled by increasing the number of LED points and video cameras; thus, this method can be used for monitoring the motion of other large-size objects (e.g., several hundred meters). The applied method has made it possible to obtain the following novel results: vibration damping in a contact wire is nonlinear by nature and its intensity depends on the wire vibration amplitude; the natural frequency of contact wire vibration varies, and it is a function of vibration amplitude; the natural frequency of contact wire vibration also depends on the wire temperature. The proposed method can be used to monitor the uplift of contact and messenger wires in laboratory conditions, or for experimental OCL testing, as well as for verifying simulation models of OCL. Full article
(This article belongs to the Special Issue Sensor-Based Frequency, Time-Frequency, and Higher Order Signal Processing for Condition Monitoring, Structural Health Monitoring, and Non-Destructive Testing)
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19 pages, 10177 KiB  
Article
Quantitative Investigation of Acoustic Emission Waveform Parameters from Crack Opening in a Rail Section Using Clustering Algorithms and Advanced Signal Processing
by Harsh Mahajan and Sauvik Banerjee
Sensors 2022, 22(22), 8643; https://doi.org/10.3390/s22228643 - 09 Nov 2022
Cited by 2 | Viewed by 1411
Abstract
Acoustic emission (AE) is an emerging technology for real-time non-destructive testing of structures. While research on a simulated AE source in rail and testing on rail material using small beam samples have been conducted, a study is required in lab environment to investigate [...] Read more.
Acoustic emission (AE) is an emerging technology for real-time non-destructive testing of structures. While research on a simulated AE source in rail and testing on rail material using small beam samples have been conducted, a study is required in lab environment to investigate AE waveform characteristics generated by crack in rail. In this paper, a three-point bending test is conducted on an actual rail section of 1500 mm with transverse damage of 38% head area to simulate AE source due to crack opening in the rail. AE signals are recorded for three different loads. For data analysis, unsupervised machine learning algorithms such as k-means, fuzzy-C mean and gaussian mixture model are used to cluster and filter out usable signals from the whole dataset corrupted by noisy signals from various sources. k-mean with principal component was observed to be best technique based on silhouette score. The frequency and amplitude of waveform have been discussed in relation to load and crack opening displacement. This study establishes a baseline for linking load, crack opening, and AE wave characteristics. This work can ultimately aid in the development of robust denoising, and damage analysis algorithms based on the frequency content and dispersion of the AE waveform. Full article
(This article belongs to the Special Issue Sensor-Based Frequency, Time-Frequency, and Higher Order Signal Processing for Condition Monitoring, Structural Health Monitoring, and Non-Destructive Testing)
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16 pages, 2821 KiB  
Article
Application of Adaptive Filtering Based on Variational Mode Decomposition for High-Temperature Electromagnetic Acoustic Transducer Denoising
by Shuaijie Zhao, Jinjie Zhou, Yao Liu, Jitang Zhang and Jie Cui
Sensors 2022, 22(18), 7042; https://doi.org/10.3390/s22187042 - 17 Sep 2022
Cited by 5 | Viewed by 1801
Abstract
In high-temperature environments, the signal-to-noise ratio (SNR) of the signal measured by electromagnetic acoustic transducers (EMAT) is low, and the signal characteristics are difficult to extract, which greatly affects their application in practical industry. Aiming at this problem, this paper proposes the least [...] Read more.
In high-temperature environments, the signal-to-noise ratio (SNR) of the signal measured by electromagnetic acoustic transducers (EMAT) is low, and the signal characteristics are difficult to extract, which greatly affects their application in practical industry. Aiming at this problem, this paper proposes the least mean square adaptive filtering interpolation denoising method based on variational modal decomposition (AFIV). Firstly, the high-temperature EMAT signal was decomposed by variational modal decomposition (VMD). Then the high-frequency and low-frequency noises in the signal were filtered according to the excitation center frequency. Following the wavelet threshold denoising (WTD) for the noise component after VMD decomposition was carried out. Afterward, the noise component and signal component were connected by an adaptive filtering process to achieve further noise reduction. Finally, cubic spline interpolation was used to smooth the noise reduction curve and obtain the time information. To verify the effectiveness of the proposed method, it was applied to two kinds of ultrasonic signals from 25 to 700 °C. Compared with VMD, WTD, and empirical mode decomposition denoising, the SNR was increased by 2 times. The results show that this method can better extract the effective information of echo signals and realize the online thickness measurement at high temperature. Full article
(This article belongs to the Special Issue Sensor-Based Frequency, Time-Frequency, and Higher Order Signal Processing for Condition Monitoring, Structural Health Monitoring, and Non-Destructive Testing)
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28 pages, 8698 KiB  
Article
Empirical Variational Mode Decomposition Based on Binary Tree Algorithm
by Huipeng Li, Bo Xu, Fengxing Zhou, Baokang Yan and Fengqi Zhou
Sensors 2022, 22(13), 4961; https://doi.org/10.3390/s22134961 - 30 Jun 2022
Cited by 3 | Viewed by 1745
Abstract
Aiming at non-stationary signals with complex components, the performance of a variational mode decomposition (VMD) algorithm is seriously affected by the key parameters such as the number of modes K, the quadratic penalty parameter α and the update step τ. In [...] Read more.
Aiming at non-stationary signals with complex components, the performance of a variational mode decomposition (VMD) algorithm is seriously affected by the key parameters such as the number of modes K, the quadratic penalty parameter α and the update step τ. In order to solve this problem, an adaptive empirical variational mode decomposition (EVMD) method based on a binary tree model is proposed in this paper, which can not only effectively solve the problem of VMD parameter selection, but also effectively reduce the computational complexity of searching the optimal VMD parameters using intelligent optimization algorithm. Firstly, the signal noise ratio (SNR) and refined composite multi-scale dispersion entropy (RCMDE) of the decomposed signal are calculated. The RCMDE is used as the setting basis of the α, and the SNR is used as the parameter value of the τ. Then, the signal is decomposed into two components based on the binary tree mode. Before decomposing, the α and τ need to be reset according to the SNR and MDE of the new signal. Finally, the cycle iteration termination condition composed of the least squares mutual information and reconstruction error of the components determines whether to continue the decomposition. The components with large least squares mutual information (LSMI) are combined, and the LSMI threshold is set as 0.8. The simulation and experimental results indicate that the proposed empirical VMD algorithm can decompose the non-stationary signals adaptively, with lower complexity, which is O(n2), good decomposition effect and strong robustness. Full article
(This article belongs to the Special Issue Sensor-Based Frequency, Time-Frequency, and Higher Order Signal Processing for Condition Monitoring, Structural Health Monitoring, and Non-Destructive Testing)
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20 pages, 14099 KiB  
Article
Research on Fuel Offset Control of High-Pressure Common-Rail Diesel Engine Based on Crankshaft Segment Signals
by Yuhua Wang, Guiyong Wang, Guozhong Yao and Lizhong Shen
Sensors 2022, 22(9), 3355; https://doi.org/10.3390/s22093355 - 27 Apr 2022
Cited by 1 | Viewed by 1890
Abstract
This paper studies the fuel supply offset of diesel engines based on the crankshaft segment signal. Engine nonuniformity refers to the crankshaft torque fluctuation caused by cylinder-to-cylinder differences caused by misfiring or differences in the fuel supply or air supply. Fuel injection offset [...] Read more.
This paper studies the fuel supply offset of diesel engines based on the crankshaft segment signal. Engine nonuniformity refers to the crankshaft torque fluctuation caused by cylinder-to-cylinder differences caused by misfiring or differences in the fuel supply or air supply. Fuel injection offset can reduce the nonuniformity between cylinders to realize high-efficiency and low pollution combustion. Based on crankshaft segment signal characteristics, an individual cylinder fuel offset control (FOC) strategy was built. The high-pressure common-rail diesel engine operating nonuniform control strategy was put forward. Based on crankshaft segment signal characteristics at different operating conditions, the reasonable operating condition of detecting the engine individual cylinder nonuniform degree was put forward. The open-loop and closed-loop control mode based on the condition was set up. The proportional-integral (PI) control algorithm is proposed to quantify engine individual cylinder nonuniform degree, and the fuel amount offset value was obtained. According to the principle of FOC, based on the automotive electronics development ASCET software platform, the FOC strategy module of the electronic control unit (ECU) was designed, and the simulation experiment was carried out. Research shows that for Z cylinder engine, just the first Z/2 harmonic components below fire frequency can fully reflect the state of the engine’s nonuniform operation. The control target to individual cylinder FOC is zero for the synthetic waveform amplitude of the first Z/2 harmonic components. Compared with the traditional quantization method, the fuel offset information extracted from the crankshaft segment signal has stronger anti-interference and more accurate parameters. FOC algorithm can accurately reflect the engine’s operating nonuniformity. The control of the nonuniformity is reasonable. The offset fuel amount calculated by FOC is very consistent with the fuel supply state of each cylinder set by the experiment, which meets the requirement of accurate fuel injection control of the diesel engine. Full article
(This article belongs to the Special Issue Sensor-Based Frequency, Time-Frequency, and Higher Order Signal Processing for Condition Monitoring, Structural Health Monitoring, and Non-Destructive Testing)
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10 pages, 2578 KiB  
Communication
Device for Torsional Fatigue Strength Assessment Adapted for Pulsating Testing Machines
by Viorel Goanta
Sensors 2022, 22(7), 2667; https://doi.org/10.3390/s22072667 - 30 Mar 2022
Cited by 2 | Viewed by 1729
Abstract
The torsional fatigue test determines the fatigue limit for a certain asymmetry coefficient of the cycle. The assessment of fatigue tests is performed on specialized machines. There are two types of torsion testing machines: universal machines that have the torsion component and specialized [...] Read more.
The torsional fatigue test determines the fatigue limit for a certain asymmetry coefficient of the cycle. The assessment of fatigue tests is performed on specialized machines. There are two types of torsion testing machines: universal machines that have the torsion component and specialized machines only for torsion testing. Nevertheless, no matter which proposed option we choose, the purchase prices for these testing machines or the values spent for self-management are quite high. This paper presented a device used for torsion fatigue testing, adaptable to a universal pulsating testing machine, designed to determine the torsion fatigue limit for different materials. The built device is simple and reliable, and therefore inexpensive. By using this device, we can determine the limit of the torsional fatigue after any stress cycle and we can use the parameters obtained from the universal machine to which it was attached. The torque and twisting angle of the test specimen during the test can be determined by calculation. The paper also presented an experimental method for determining shear strains based on calibration experiment, using a specimen on which strain gauges were mounted. The values taken from this calibration experiment were compared with those obtained from the theoretical calculation. Full article
(This article belongs to the Special Issue Sensor-Based Frequency, Time-Frequency, and Higher Order Signal Processing for Condition Monitoring, Structural Health Monitoring, and Non-Destructive Testing)
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23 pages, 10132 KiB  
Article
Online Surface Roughness Prediction for Assembly Interfaces of Vertical Tail Integrating Tool Wear under Variable Cutting Parameters
by Yahui Wang, Yiwei Wang, Lianyu Zheng and Jian Zhou
Sensors 2022, 22(5), 1991; https://doi.org/10.3390/s22051991 - 03 Mar 2022
Cited by 14 | Viewed by 2514
Abstract
Monitoring surface quality during machining has considerable practical significance for the performance of high-value products, particularly for their assembly interfaces. Surface roughness is the most important metric of surface quality. Currently, the research on online surface roughness prediction has several limitations. The effect [...] Read more.
Monitoring surface quality during machining has considerable practical significance for the performance of high-value products, particularly for their assembly interfaces. Surface roughness is the most important metric of surface quality. Currently, the research on online surface roughness prediction has several limitations. The effect of tool wear variation on surface roughness is seldom considered in machining. In addition, the deterioration trend of surface roughness and tool wear differs under variable cutting parameters. The prediction models trained under one set of cutting parameters fail when cutting parameters change. Accordingly, to timely monitor the surface quality of assembly interfaces of high-value products, this paper proposes a surface roughness prediction method that considers the tool wear variation under variable cutting parameters. In this method, a stacked autoencoder and long short-term memory network (SAE–LSTM) is designed as the fundamental surface roughness prediction model using tool wear conditions and sensor signals as inputs. The transfer learning strategy is applied to the SAE–LSTM such that the surface roughness online prediction under variable cutting parameters can be realized. Machining experiments for the assembly interface (using Ti6Al4V as material) of an aircraft’s vertical tail are conducted, and monitoring data are used to validate the proposed method. Ablation studies are implemented to evaluate the key modules of the proposed model. The experimental results show that the proposed method outperforms other models and is capable of tracking the true surface roughness with time. Specifically, the minimum values of the root mean square error and mean absolute percentage error of the prediction results after transfer learning are 0.027 μm and 1.56%, respectively. Full article
(This article belongs to the Special Issue Sensor-Based Frequency, Time-Frequency, and Higher Order Signal Processing for Condition Monitoring, Structural Health Monitoring, and Non-Destructive Testing)
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17 pages, 2147 KiB  
Article
A Bearing Fault Diagnosis Method Based on Wavelet Packet Transform and Convolutional Neural Network Optimized by Simulated Annealing Algorithm
by Feng He and Qing Ye
Sensors 2022, 22(4), 1410; https://doi.org/10.3390/s22041410 - 12 Feb 2022
Cited by 69 | Viewed by 3794
Abstract
Bearings are widely used in various electrical and mechanical equipment. As their core components, failures often have serious consequences. At present, most parameter adjustment methods are still manual adjustments of parameters. This adjustment method is easily affected by prior knowledge, easily falls into [...] Read more.
Bearings are widely used in various electrical and mechanical equipment. As their core components, failures often have serious consequences. At present, most parameter adjustment methods are still manual adjustments of parameters. This adjustment method is easily affected by prior knowledge, easily falls into the local optimal solution, cannot obtain the global optimal solution, and requires a lot of resources. Therefore, this paper proposes a new method for bearing fault diagnosis based on wavelet packet transform and convolutional neural network optimized by a simulated annealing algorithm. Firstly, the original bearing vibration signal is extracted by wavelet packet transform to obtain the spectrogram, and then the obtained spectrogram is sent to the convolutional neural network for parameter adjustment, and finally the simulated annealing algorithm is used to adjust the parameters. To verify the effectiveness of the method, the bearing database of Case Western Reserve University is used for testing, and the traditional intelligent bearing fault diagnosis methods are compared. The results show that the new method for bearing fault diagnosis proposed in this paper has a better and more reliable diagnosis effect than the existing machine learning and deep learning methods. Full article
(This article belongs to the Special Issue Sensor-Based Frequency, Time-Frequency, and Higher Order Signal Processing for Condition Monitoring, Structural Health Monitoring, and Non-Destructive Testing)
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20 pages, 10140 KiB  
Article
Multilevel Fine Fault Diagnosis Method for Motors Based on Feature Extraction of Fractional Fourier Transform
by Hao Wu, Xue Ma and Chenglin Wen
Sensors 2022, 22(4), 1310; https://doi.org/10.3390/s22041310 - 09 Feb 2022
Cited by 8 | Viewed by 1749
Abstract
Motors are the main driving power for equipment operation, and they are also a major factor to promote the development of the motor and the load it drives and its motor control system toward a low-carbon future, reduce carbon emissions, and improve the [...] Read more.
Motors are the main driving power for equipment operation, and they are also a major factor to promote the development of the motor and the load it drives and its motor control system toward a low-carbon future, reduce carbon emissions, and improve the industrial economy and social economic efficiency. Due to high-speed, long-period, and heavy-load operation, various faults occur; since the existing integer-order Fourier transform methods have not enough able to detect fractional-order faults and lack robustness, it is difficult to realize the fine diagnosis of motor faults, which reduces the safety and reliability of the motor control system. For this reason, on the basis of the powerful extraction ability of the fractional Fourier transform (FRFT) for micro fault features, especially the extraction ability to fit fractional frequency domain faults, this paper intends to establish a multilevel fine fault diagnosis method for fractional-order or integer-order faults. Firstly, this is accomplished by performing the fractional Fourier transform on the acquired data with faults and feature extraction in the multilevel fractional frequency domain and then optimizing the feature extraction model. Secondly, one further step search method is established to determine the projection direction with the largest fault feature. Thirdly, taking the extracted multilevel fault features as input, a multilevel fine fault diagnosis method based on the SVM model is established. Finally, three typical digital simulation examples and actual operating data collected by the ZHS-2 multifunctional motor test bench with a flexible rotor are employed to verify the effectiveness, robustness, and accuracy of this new method. The main contribution and innovation of this paper are that the fractional Fourier transform method based on time domain and frequency domains is introduced. This method can extract the small fault features in the maximum projection direction of the signal in the fractional domain, but detection with other time–frequency methods is difficult; the extracted multilevel fault features are used as input, and the corresponding fault diagnosis model is established, which can improve the accuracy of fault detection and ensure the safe and reliable operation of industrial equipment. Full article
(This article belongs to the Special Issue Sensor-Based Frequency, Time-Frequency, and Higher Order Signal Processing for Condition Monitoring, Structural Health Monitoring, and Non-Destructive Testing)
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14 pages, 4505 KiB  
Article
Vibration-Based Loosening Detection of a Multi-Bolt Structure Using Machine Learning Algorithms
by Oybek Eraliev, Kwang-Hee Lee and Chul-Hee Lee
Sensors 2022, 22(3), 1210; https://doi.org/10.3390/s22031210 - 05 Feb 2022
Cited by 13 | Viewed by 3167
Abstract
Since artificial intelligence (AI) was introduced into engineering fields, it has made many breakthroughs. Machine learning (ML) algorithms have been very commonly used in structural health monitoring (SHM) systems in the last decade. In this study, a vibration-based early stage of bolt loosening [...] Read more.
Since artificial intelligence (AI) was introduced into engineering fields, it has made many breakthroughs. Machine learning (ML) algorithms have been very commonly used in structural health monitoring (SHM) systems in the last decade. In this study, a vibration-based early stage of bolt loosening detection and identification technique is proposed using ML algorithms, for a motor fastened with four bolts (M8 × 1.5) to a stationary support. First, several cases with fastened and loosened bolts were established, and the motor was operated in three different types of working condition (800 rpm, 1000 rpm, and 1200 rpm), in order to obtain enough vibration data. Second, for feature extraction of the dataset, the short-time Fourier transform (STFT) method was performed. Third, different types of classifier of ML were trained, and a new test dataset was applied to evaluate the performance of the classifiers. Finally, the classifier with the greatest accuracy was identified. The test results showed that the capability of the classifier was satisfactory for detecting bolt loosening and identifying which bolt or bolts started to lose their preload in each working condition. The identified classifier will be implemented for online monitoring of the early stage of bolt loosening of a multi-bolt structure in future works. Full article
(This article belongs to the Special Issue Sensor-Based Frequency, Time-Frequency, and Higher Order Signal Processing for Condition Monitoring, Structural Health Monitoring, and Non-Destructive Testing)
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17 pages, 44743 KiB  
Article
A Fuzzy Fusion Rotating Machinery Fault Diagnosis Framework Based on the Enhancement Deep Convolutional Neural Networks
by Daoguang Yang, Hamid Reza Karimi and Len Gelman
Sensors 2022, 22(2), 671; https://doi.org/10.3390/s22020671 - 16 Jan 2022
Cited by 19 | Viewed by 2785
Abstract
Some artificial intelligence algorithms have gained much attention in the rotating machinery fault diagnosis due to their robust nonlinear regression properties. In addition, existing deep learning algorithms are usually dependent on single signal features, which would lead to the loss of some information [...] Read more.
Some artificial intelligence algorithms have gained much attention in the rotating machinery fault diagnosis due to their robust nonlinear regression properties. In addition, existing deep learning algorithms are usually dependent on single signal features, which would lead to the loss of some information or incomplete use of the information in the signal. To address this problem, three kinds of popular signal processing methods, including Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT) and directly slicing one-dimensional data into the two-dimensional matrix, are used to create four different datasets from raw vibration signal as the input data of four enhancement Convolutional Neural Networks (CNN) models. Then, a fuzzy fusion strategy is used to fuse the output of four CNN models that could analyze the importance of each classifier and explore the interaction index between each classifier, which is different from conventional fusion strategies. To show the performance of the proposed model, an artificial fault bearing dataset and a real-world bearing dataset are used to test the feature extraction capability of the model. The good anti-noise and interpretation characteristics of the proposed method are demonstrated as well. Full article
(This article belongs to the Special Issue Sensor-Based Frequency, Time-Frequency, and Higher Order Signal Processing for Condition Monitoring, Structural Health Monitoring, and Non-Destructive Testing)
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12 pages, 2200 KiB  
Communication
Diagnosing Extrusion Process Based on Displacement Signal and Simple Decision Tree Classifier
by Grzegorz Piecuch and Rafał Żyła
Sensors 2022, 22(1), 379; https://doi.org/10.3390/s22010379 - 05 Jan 2022
Cited by 1 | Viewed by 1798
Abstract
The article presents an extensive analysis of the literature related to the diagnosis of the extrusion process and proposes a new, unique method. This method is based on the observation of the punch displacement signal in relation to the die, and then approximation [...] Read more.
The article presents an extensive analysis of the literature related to the diagnosis of the extrusion process and proposes a new, unique method. This method is based on the observation of the punch displacement signal in relation to the die, and then approximation of this signal using a polynomial. It is difficult to find in the literature even an attempt to solve the problem of diagnosing the extrusion process by means of a simple distance measurement. The dominant feature is the use of strain gauges, force sensors or even accelerometers. However, the authors managed to use the displacement signal, and it was considered a key element of the method presented in the article. The aim of the authors was to propose an effective method, simple to implement and not requiring high computing power, with the possibility of acting and making decisions in real time. At the input of the classifier, authors provided the determined polynomial coefficients and the SSE (Sum of Squared Errors) value. Based on the SSE values only, the decision tree algorithm performed anomaly detection with an accuracy of 98.36%. With regard to the duration of the experiment (single extrusion process), the decision was made after 0.44 s, which is on average 26.7% of the extrusion experiment duration. The article describes in detail the method and the results achieved. Full article
(This article belongs to the Special Issue Sensor-Based Frequency, Time-Frequency, and Higher Order Signal Processing for Condition Monitoring, Structural Health Monitoring, and Non-Destructive Testing)
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