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Special Issue "Data Acquisition and Processing for Fault Diagnosis"

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

Deadline for manuscript submissions: 31 July 2021.

Special Issue Editors

Prof. Dr. Gilbert-Rainer Gillich
E-Mail Website
Guest Editor
Department of Engineering Science, Babeș-Bolyai University Cluj-Napoca, Resita 320085, Romania
Interests: structural dynamics; damage detection; signal processing
Special Issues and Collections in MDPI journals
Prof. Dr. Ruqiang Yan
E-Mail Website
Guest Editor
School of Mechanical Engineering, Xi'an Jiaotong University, 28 Xianning West Road, Xi'an, Shaanxi 710049, China
Interests: condition monitoring; fault diagnosis; prognosis; condition-based maintenance; machine learning; deep learning; transfer learning; signal processing; nonlinear time series analysis; wavelet transform
Dr. Abdollah Malekjafarian
E-Mail Website
Guest Editor
School of Civil Engineering, University College Dublin, Dublin, Ireland
Interests: structural dynamics and vibration; structural health monitoring; modal analysis; bridge health monitoring

Special Issue Information

Dear Colleagues,

Monitoring engineering systems to identify when a failure has occurred and determine its nature, location, and severity is a current approach designed to increase the operational safety of machines and structures. In the age of Industry 4.0, this approach is more topical than ever. Cyberphysical systems must allow for self-assessment, which involves physical measurements, their transformation into digital information, and autonomous decision-making. Global control methods, based on vibration analysis, are most suitable for this purpose, because sensors occupy fixed positions and can be placed where humans themselves find it difficult to reach.

In recent decades, research has been connected to various fields such as advanced sensor technologies, measurement techniques, signal processing methods, and statistical decision-making algorithms to design procedures to assess the condition of machines and structures.

This issue will include papers that address all aspects related to fault detection and identification, considering sensors, measurement techniques, signal processing, and classification algorithms. Original contributions that address both theoretical and experimental issues are welcome, but also review articles on specific topics within the scope of this issue are welcome.

Prof. Dr. Gilbert-Rainer Gillich
Prof. Dr. Ruqiang Yan
Dr. Abdollah Malekjafarian
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

  • Structural health monitoring
  • Condition monitoring
  • Data acquisition, normalization and cleansing
  • Advanced signal processing
  • System identification
  • Artificial inteligence
  • Machine learning and deep learning techniques
  • Pattern recognition
  • Algorithms

Published Papers (12 papers)

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Research

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Open AccessArticle
Guaranteed State Estimation Using a Bundle of Interval Observers with Adaptive Gains Applied to the Induction Machine
Sensors 2021, 21(8), 2584; https://doi.org/10.3390/s21082584 - 07 Apr 2021
Viewed by 143
Abstract
The scope of this paper is the design of an interval observer bundle for the guaranteed state estimation of an uncertain induction machine with linear, time-varying dynamics. These guarantees are of particular interest in the case of safety-critical systems. In many cases, interval [...] Read more.
The scope of this paper is the design of an interval observer bundle for the guaranteed state estimation of an uncertain induction machine with linear, time-varying dynamics. These guarantees are of particular interest in the case of safety-critical systems. In many cases, interval observers provide large intervals for which the usability becomes impractical. Hence, based on a reduced-order hybrid interval observer structure, the guaranteed enclosure within intervals of the magnetizing current’s estimates is improved using a bundle of interval observers. One advantage of such an interval observer bundle is the possibility to reinitialize the interval observers at specified timesteps during runtime with smaller initial intervals, based on previously observed system states, resulting in decreasing interval widths. Thus, unstable observer dynamics are considered so as to take advantage of their transient behavior, whereby the overall stability of the interval estimation is maintained. An algorithm is presented to determine the parametrization of reduced-order interval observers. To this, an adaptive observer gain is introduced with which the system states are observed optimally by considering a minimal interval width at variable operating points. Furthermore, real-time capability and validation of the proposed methods are shown. The results are discussed with simulations as well as experimental data obtained with a test bench. Full article
(This article belongs to the Special Issue Data Acquisition and Processing for Fault Diagnosis)
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Open AccessArticle
Visual Measurement System for Wheel–Rail Lateral Position Evaluation
Sensors 2021, 21(4), 1297; https://doi.org/10.3390/s21041297 - 11 Feb 2021
Viewed by 545
Abstract
Railway infrastructure must meet safety requirements concerning its construction and operation. Track geometry monitoring is one of the most important activities in maintaining the steady technical conditions of rail infrastructure. Commonly, it is performed using complex measurement equipment installed on track-recording coaches. Existing [...] Read more.
Railway infrastructure must meet safety requirements concerning its construction and operation. Track geometry monitoring is one of the most important activities in maintaining the steady technical conditions of rail infrastructure. Commonly, it is performed using complex measurement equipment installed on track-recording coaches. Existing low-cost inertial sensor-based measurement systems provide reliable measurements of track geometry in vertical directions. However, solutions are needed for track geometry parameter measurement in the lateral direction. In this research, the authors developed a visual measurement system for track gauge evaluation. It involves the detection of measurement points and the visual measurement of the distance between them. The accuracy of the visual measurement system was evaluated in the laboratory and showed promising results. The initial field test was performed in the Vilnius railway station yard, driving at low velocity on the straight track section. The results show that the image point selection method developed for selecting the wheel and rail points to measure distance is stable enough for TG measurement. Recommendations for the further improvement of the developed system are presented. Full article
(This article belongs to the Special Issue Data Acquisition and Processing for Fault Diagnosis)
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Open AccessArticle
Multi-Dimensional Uniform Initialization Gaussian Mixture Model for Spar Crack Quantification under Uncertainty
Sensors 2021, 21(4), 1283; https://doi.org/10.3390/s21041283 - 11 Feb 2021
Viewed by 348
Abstract
Guided Wave (GW)-based crack monitoring method as a promising method has been widely studied, as this method is sensitive to small cracks and can cover a wide monitoring range. Online crack quantification is difficult as the initiation and growth of crack are affected [...] Read more.
Guided Wave (GW)-based crack monitoring method as a promising method has been widely studied, as this method is sensitive to small cracks and can cover a wide monitoring range. Online crack quantification is difficult as the initiation and growth of crack are affected by various uncertainties. In addition, crack-sensitive GW features are influenced by time-varying conditions which further increase the difficulty in crack quantification. Considering these uncertainties, the Gaussian mixture model (GMM) is studied to model the probability distribution of GW features. To further improve the accuracy and stability of crack quantification under uncertainties, this paper proposes a multi-dimensional uniform initialization GMM. First, the multi-channel GW features are integrated to increase the accuracy of crack quantification, as GW features from different channels have different sensitivity to cracks. Then, the uniform initialization method is adopted to provide more stable initial parameters in the expectation-maximization algorithm. In addition, the relationship between the probability migration index of GMMs and crack length is calibrated with fatigue tests on prior specimens. Finally, the proposed method is applied for online crack quantification on the notched specimen of an aircraft spar with complex fan-shaped cracks under uncertainty. Full article
(This article belongs to the Special Issue Data Acquisition and Processing for Fault Diagnosis)
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Open AccessArticle
Data-Driven Method for Predicting Remaining Useful Life of Bearing Based on Bayesian Theory
Sensors 2021, 21(1), 182; https://doi.org/10.3390/s21010182 - 29 Dec 2020
Viewed by 596
Abstract
Bearings are some of the most critical industrial parts and are widely used in various types of mechanical equipment. Bearing health status can have a significant impact on the overall equipment performance, and bearing failures often cause serious economic losses and even casualties. [...] Read more.
Bearings are some of the most critical industrial parts and are widely used in various types of mechanical equipment. Bearing health status can have a significant impact on the overall equipment performance, and bearing failures often cause serious economic losses and even casualties. Thus, estimating the remaining useful life (RUL) of bearings in real time is of utmost importance. This paper proposes a data-driven RUL prediction method for bearings based on Bayesian theory. First, time-domain features are extracted from the bearing vibration signal and data are fused to build a health indicator (HI) and a state model of bearing degradation. Then, according to Bayesian theory, a Bayesian model of state parameters and bearing life is established. The parameters of the Bayesian model are updated and bearing RUL is predicted by the Metropolis–Hastings algorithm. The method was validated by the XJTU-SY bearing open datasets and the prediction results are compared with the existing methods. Accuracy of the proposed method was demonstrated. Full article
(This article belongs to the Special Issue Data Acquisition and Processing for Fault Diagnosis)
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Open AccessArticle
New Arc Stability Index for Industrial AC Three-Phase Electric Arc Furnaces Based on Acoustic Signals
Sensors 2020, 20(23), 6840; https://doi.org/10.3390/s20236840 - 30 Nov 2020
Viewed by 461
Abstract
This research proposes a new index to evaluate the stability of the melting process, in three-phase electric arc furnaces (EAFs), based on the acoustic signals generated during the different stages of the casting. The proposed stability index is obtained by characterizing the time [...] Read more.
This research proposes a new index to evaluate the stability of the melting process, in three-phase electric arc furnaces (EAFs), based on the acoustic signals generated during the different stages of the casting. The proposed stability index is obtained by characterizing the time and frequency domain of the acoustic signals. During EAF monitoring, acoustic signals were acquired using a microphone coupled to an NI USB-9234 acquisition system. To validate the results, the voltage and current signals were measured with the aid of a Circutor AR6 power analyzer for three-phase electrical networks. The results showed that the acoustic signal energy in the frequency range of 1 to 12 kHz can be used as an indicator of the process stability in the EAF. Finally, the validity of the proposed stability index is evaluated from the process characterization using the harmonic distortion analysis methods and the dynamic U-I characteristics of the arc voltage and current signals. The results obtained demonstrated the effectiveness of the proposal and constitute a starting point for advances in the automatic control of the process in the EAF, from the acoustic signals. Full article
(This article belongs to the Special Issue Data Acquisition and Processing for Fault Diagnosis)
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Open AccessArticle
Tool Wear Condition Monitoring by Combining Variational Mode Decomposition and Ensemble Learning
Sensors 2020, 20(21), 6113; https://doi.org/10.3390/s20216113 - 27 Oct 2020
Viewed by 470
Abstract
Most online tool condition monitoring (TCM) methods easily cause machining interference. To solve this problem, we propose a method based on the analysis of the spindle motor current signal of a machine tool. Firstly, cutting experiments under multi-conditions were carried out at a [...] Read more.
Most online tool condition monitoring (TCM) methods easily cause machining interference. To solve this problem, we propose a method based on the analysis of the spindle motor current signal of a machine tool. Firstly, cutting experiments under multi-conditions were carried out at a Fanuc vertical machining center, using the Fanuc Servo Guide software to obtain the spindle motor current data of the built-in current sensor of the machine tool, which can not only apply to the actual processing conditions but, also, save costs. Secondly, we propose the variational mode decomposition (VMD) algorithm for feature extraction, which can describe the tool conditions under different cutting conditions due to its excellent performance in processing the nonstationary current signal. In contrast with the popular wavelet packet decomposition (WPD) method, the VMD method was verified as a more effective signal-processing technique according to the experimental results. Thirdly, the most indicative features that relate to the tool condition were fed into the ensemble learning (EL) classifier to establish a nonlinear mapping relationship between the features and the tool wear level. Compared with existing TCM methods based on current sensor signals, the operation process and experimental results show that using the proposed method for the monitoring signal acquisition is suitable for the actual processing conditions, and the established tool wear prediction model has better performance in both accuracy and robustness due to its good generalization capability. Full article
(This article belongs to the Special Issue Data Acquisition and Processing for Fault Diagnosis)
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Open AccessArticle
Supervised Health Stage Prediction Using Convolutional Neural Networks for Bearing Wear
Sensors 2020, 20(20), 5846; https://doi.org/10.3390/s20205846 - 16 Oct 2020
Cited by 1 | Viewed by 595
Abstract
Early detection of faults in rotating machinery systems is crucial in preventing system failure, increasing safety, and reducing maintenance costs. Current methods of fault detection suffer from the lack of efficient feature extraction method, the need for designating a threshold producing minimal false [...] Read more.
Early detection of faults in rotating machinery systems is crucial in preventing system failure, increasing safety, and reducing maintenance costs. Current methods of fault detection suffer from the lack of efficient feature extraction method, the need for designating a threshold producing minimal false alarm rates, and the need for expert domain knowledge, which is costly. In this paper, we propose a novel data-driven health division method based on convolutional neural networks using a graphical representation of time series data, called a nested scatter plot. The proposed method trains the model with a small amount of labeled data and does not require a threshold value to predict the health state of rotary machines. Notwithstanding the lack of datasets that show the ground truth of health stages, our experiments with two open datasets of run-to-failure bearing demonstrated that our method is able to detect the early symptoms of bearing wear earlier and more efficiently than other threshold-based health indicator methods. Full article
(This article belongs to the Special Issue Data Acquisition and Processing for Fault Diagnosis)
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Open AccessArticle
Simultaneously Low Rank and Group Sparse Decomposition for Rolling Bearing Fault Diagnosis
Sensors 2020, 20(19), 5541; https://doi.org/10.3390/s20195541 - 27 Sep 2020
Viewed by 645
Abstract
Singular value decomposition (SVD) methods have aroused wide concern to extract the periodic impulses for bearing fault diagnosis. The state-of-the-art SVD methods mainly focus on the low rank property of the Hankel matrix for the fault feature, which cannot achieve satisfied performance when [...] Read more.
Singular value decomposition (SVD) methods have aroused wide concern to extract the periodic impulses for bearing fault diagnosis. The state-of-the-art SVD methods mainly focus on the low rank property of the Hankel matrix for the fault feature, which cannot achieve satisfied performance when the background noise is strong. Different to the existing low rank-based approaches, we proposed a simultaneously low rank and group sparse decomposition (SLRGSD) method for bearing fault diagnosis. The major contribution is that the simultaneously low rank and group sparse (SLRGS) property of the Hankel matrix for fault feature is first revealed to improve performance of the proposed method. Firstly, we exploit the SLRGS property of the Hankel matrix for the fault feature. On this basis, a regularization model is formulated to construct the new diagnostic framework. Furthermore, the incremental proximal algorithm is adopted to achieve a stationary solution. Finally, the effectiveness of the SLRGSD method for enhancing the fault feature are profoundly validated by the numerical analysis, the artificial bearing fault experiment and the wind turbine bearing fault experiment. Simulation and experimental results indicate that the SLRGSD method can obtain superior results of extracting the incipient fault feature in both performance and visual quality as compared with the state-of-the-art methods. Full article
(This article belongs to the Special Issue Data Acquisition and Processing for Fault Diagnosis)
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Open AccessArticle
Multi-Sensor Data Fusion for Remaining Useful Life Prediction of Machining Tools by IABC-BPNN in Dry Milling Operations
Sensors 2020, 20(17), 4657; https://doi.org/10.3390/s20174657 - 19 Aug 2020
Cited by 1 | Viewed by 751
Abstract
Inefficient remaining useful life (RUL) estimation may cause unpredictable failures and unscheduled maintenance of machining tools. Multi-sensor data fusion will improve the RUL prediction reliability by fusing more sensor information related to the machining process of tools. In this paper, a multi-sensor data [...] Read more.
Inefficient remaining useful life (RUL) estimation may cause unpredictable failures and unscheduled maintenance of machining tools. Multi-sensor data fusion will improve the RUL prediction reliability by fusing more sensor information related to the machining process of tools. In this paper, a multi-sensor data fusion system for online RUL prediction of machining tools is proposed. The system integrates multi-sensor signal collection, signal preprocess by a complementary ensemble empirical mode decomposition, feature extraction in time domain, frequency domain and time-frequency domain by such methods as statistical analysis, power spectrum density analysis and Hilbert-Huang transform, feature selection by a Light Gradient Boosting Machine method, feature fusion by a tool wear prediction model based on back propagation neural network optimized by improved artificial bee colony (IABC-BPNN) algorithm, and the online RUL prediction model by a polynomial curve fitting method. An example is used to verify whether if the prediction performance of the proposed system is stable and reliable, and the results show that it is superior to its rivals. Full article
(This article belongs to the Special Issue Data Acquisition and Processing for Fault Diagnosis)
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Open AccessArticle
Process Parameters for FFF 3D-Printed Conductors for Applications in Sensors
Sensors 2020, 20(16), 4542; https://doi.org/10.3390/s20164542 - 13 Aug 2020
Cited by 4 | Viewed by 837
Abstract
With recent developments in additive manufacturing (AM), new possibilities for fabricating smart structures have emerged. Recently, single-process fused-filament fabrication (FFF) sensors for dynamic mechanical quantities have been presented. Sensors measuring dynamic mechanical quantities, like strain, force, and acceleration, typically require conductive filaments with [...] Read more.
With recent developments in additive manufacturing (AM), new possibilities for fabricating smart structures have emerged. Recently, single-process fused-filament fabrication (FFF) sensors for dynamic mechanical quantities have been presented. Sensors measuring dynamic mechanical quantities, like strain, force, and acceleration, typically require conductive filaments with a relatively high electrical resistivity. For fully embedded sensors in single-process FFF dynamic structures, the connecting electrical wires also need to be printed. In contrast to the sensors, the connecting electrical wires have to have a relatively low resistivity, which is limited by the availability of highly conductive FFF materials and FFF process conditions. This study looks at the Electrifi filament for applications in printed electrical conductors. The effect of the printing-process parameters on the electrical performance is thoroughly investigated (six parameters, >40 parameter values, >200 conductive samples) to find the highest conductivity of the printed conductors. In addition, conductor embedding and post-printing heating of the conductive material are researched. The experimental results helped us to understand the mechanisms of the conductive network’s formation and its degradation. With the insight gained, the optimal printing strategy resulted in a resistivity that was approx. 40% lower than the nominal value of the filament. With a new insight into the electrical behavior of the conductive material, process optimizations and new design strategies can be implemented for the single-process FFF of functional smart structures. Full article
(This article belongs to the Special Issue Data Acquisition and Processing for Fault Diagnosis)
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Review

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Open AccessReview
A Comparative Analysis of Signal Decomposition Techniques for Structural Health Monitoring on an Experimental Benchmark
Sensors 2021, 21(5), 1825; https://doi.org/10.3390/s21051825 - 05 Mar 2021
Viewed by 336
Abstract
Signal Processing is, arguably, the fundamental enabling technology for vibration-based Structural Health Monitoring (SHM), which includes damage detection and more advanced tasks. However, the investigation of real-life vibration measurements is quite compelling. For a better understanding of its dynamic behaviour, a multi-degree-of-freedom system [...] Read more.
Signal Processing is, arguably, the fundamental enabling technology for vibration-based Structural Health Monitoring (SHM), which includes damage detection and more advanced tasks. However, the investigation of real-life vibration measurements is quite compelling. For a better understanding of its dynamic behaviour, a multi-degree-of-freedom system should be efficiently decomposed into its independent components. However, the target structure may be affected by (damage-related or not) nonlinearities, which appear as noise-like distortions in its vibrational response. This response can be nonstationary as well and thus requires a time-frequency analysis. Adaptive mode decomposition methods are the most apt strategy under these circumstances. Here, a shortlist of three well-established algorithms has been selected for an in-depth analysis. These signal decomposition approaches—namely, the Empirical Mode Decomposition (EMD), the Hilbert Vibration Decomposition (HVD), and the Variational Mode Decomposition (VMD)—are deemed to be the most representative ones because of their extensive use and favourable reception from the research community. The main aspects and properties of these data-adaptive methods, as well as their advantages, limitations, and drawbacks, are discussed and compared. Then, the potentialities of the three algorithms are assessed firstly on a numerical case study and then on a well-known experimental benchmark, including nonlinear cases and nonstationary signals. Full article
(This article belongs to the Special Issue Data Acquisition and Processing for Fault Diagnosis)
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Other

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Open AccessLetter
Estimating System State through Similarity Analysis of Signal Patterns
Sensors 2020, 20(23), 6839; https://doi.org/10.3390/s20236839 - 30 Nov 2020
Cited by 1 | Viewed by 470
Abstract
State prediction is not straightforward, particularly for complex systems that cannot provide sufficient amounts of training data. In particular, it is usually difficult to analyze some signal patterns for state prediction if they were observed in both normal and fault-states with a similar [...] Read more.
State prediction is not straightforward, particularly for complex systems that cannot provide sufficient amounts of training data. In particular, it is usually difficult to analyze some signal patterns for state prediction if they were observed in both normal and fault-states with a similar frequency or if they were rarely observed in any system state. In order to estimate the system status with imbalanced state data characterized insufficient fault occurrences, this paper proposes a state prediction method that employs discrete state vectors (DSVs) for pattern extraction and then applies a naïve Bayes classifier and Brier scores to interpolate untrained pattern information by using the trained ones probabilistically. Each Brier score is transformed into a more intuitive one, termed state prediction power (SPP). The SPP values represent the reliability of the system state prediction. A state prediction power map, which visualizes the DSVs and corresponding SPP values, is provided a more intuitive way of state prediction analysis. A case study using a car engine fault simulator was conducted to generate artificial engine knocking. The proposed method was evaluated using holdout cross-validation, defining specificity and sensitivity as indicators to represent state prediction success rates for no-fault and fault states, respectively. The results show that specificity and sensitivity are very high (equal to 1) for high limit values of SPP, but drop off dramatically for lower limit values. Full article
(This article belongs to the Special Issue Data Acquisition and Processing for Fault Diagnosis)
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