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Sensors for Fault Diagnosis and Prognostics

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

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 46390

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Prof. Dr. Andrew G. Starr

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Guest Editor

School of Aerospace, Transport and Manufacturing, Building 50, Cranfield University, College Road, Cranfield MK43 0AL, UK
Interests: maintenance of machine systems; data fusion; fault diagnostics and prognostics
Special Issues, Collections and Topics in MDPI journals

Dr. Muhammad Khan

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Guest Editor

Centre for Life-Cycle Engineering and Management, Cranfield University, College Road, Cranfield MK43 0AL, UK
Interests: damage mechanics; maintenance engineering; asset management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

The recent advancements in automation, artificial intelligence, digitization, and data communication have widely influenced the sensing fundamentals and methods for fault diagnostics and prognostics. Noncontact-based sensing methods have made fault diagnostics and prognostics possible even for those assets which are operated around very harsh and hazardous surroundings. The challenges with regard to size and resolution of sensing elements have been addressed comprehensively in the last decade due the evolution of smart materials and mechanisms. Challenges with raw data conversion into useful information have been addressed by state-of-the-art algorithms based on statistical tools, signal processing techniques and data fusion. All these developments starting from sensing elements to reliable prediction of fault in a machine have made things possible which were not a few years ago.

This Special Issue welcomes contributions dealing with sensing methods and approaches for fault diagnosis and prognostics, including sensor materials, sensor data generation and acquisition and techniques to use sensor data for diagnosis and prognosis. Special consideration will be given to papers discussing sensor material modelling and measurement, algorithms for converting raw sensor data and experimental studies on fault monitoring.

Prof. Dr. Andrew G Star
Dr. Muhammad Khan
Guest Editors

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Keywords

Diagnosis
Prognostics
Sensor material
Sensor measurement
Fault monitoring
Intelligent algorithms
Raw data analysis
Noncontact sensing

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Published Papers (12 papers)

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Research

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16 pages, 1854 KiB

Open AccessArticle

Predictive Modeling of Surface Wear in Mechanical Contacts under Lubricated and Non-Lubricated Conditions

by Ali Rahman, Muhammad Khan and Aleem Mushtaq

Sensors 2021, 21(4), 1160; https://doi.org/10.3390/s21041160 - 7 Feb 2021

Cited by 2 | Viewed by 2770

Abstract

The surface wear in mechanical contacts under running conditions is always a challenge to quantify. However, the inevitable relationship between the airborne noise and the surface wear can be used to predict the latter with good accuracy. In this paper, a predictive model has been derived to quantify surface wear by using airborne noise signals collected at a microphone. The noise was generated from a pin on disc setup on different dry and lubricated conditions. The collected signals were analyzed, and spectral features estimated from the measurements and regression models implemented in order to achieve an average wear prediction accuracy of within 1

{m m}^{3}

. Full article

(This article belongs to the Special Issue Sensors for Fault Diagnosis and Prognostics)

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14 pages, 6810 KiB

Open AccessArticle

Particle Filtering Based Remaining Useful Life Prediction for Electromagnetic Coil Insulation

by Haifeng Guo, Aidong Xu, Kai Wang, Yue Sun, Xiaojia Han, Seung Ho Hong and Mengmeng Yu

Sensors 2021, 21(2), 473; https://doi.org/10.3390/s21020473 - 11 Jan 2021

Cited by 7 | Viewed by 2798

Abstract

Electromagnetic coils are one of the key components of many systems. Their insulation failure can have severe effects on the systems in which coils are used. This paper focuses on insulation degradation monitoring and remaining useful life (RUL) prediction of electromagnetic coils. First, insulation degradation characteristics are extracted from coil high-frequency electrical parameters. Second, health indicator is defined based on insulation degradation characteristics to indicate the health degree of coil insulation. Finally, an insulation degradation model is constructed, and coil insulation RUL prediction is performed by particle filtering. Thermal accelerated degradation experiments are performed to validate the RUL prediction performance. The proposed method presents opportunities for predictive maintenance of systems that incorporate coils. Full article

(This article belongs to the Special Issue Sensors for Fault Diagnosis and Prognostics)

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17 pages, 6109 KiB

Open AccessArticle

Increase in Fast Response Time of the Resistance-to-Voltage Converter When Monitoring the Cable Products’ Insulation Resistance

by Nikolay I. Yermoshin, Evgeny V. Yakimov, Aleksandr E. Goldshtein and Dmitry A. Sednev

Sensors 2021, 21(2), 368; https://doi.org/10.3390/s21020368 - 7 Jan 2021

Cited by 4 | Viewed by 2182

Abstract

Theoretical and experimental studies were conducted to investigate the impact of the cable capacitance during measurements of insulation resistance on the fast response time of a resistance-to-voltage converter. From a comparison of the results of simulation with the data obtained during the experiments, it was determined that the dependence characteristics of the settling time of resistance under measurement on the capacitance are identical to the analogous characteristics of electronic components of the resistance-to-voltage converter. It was experimentally proven that using T-shaped feedback in the resistance-to-voltage converter during the cable insulation resistance measurements reduces the settling time of the data by 1–3 times in comparison with a classical feedback system. Furthermore, when using the optimal parameters, the settling time of the resistance-to-voltage converter with T-shaped feedback depends to a lesser degree on the capacitance of the object under control. Full article

(This article belongs to the Special Issue Sensors for Fault Diagnosis and Prognostics)

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17 pages, 8593 KiB

Open AccessArticle

A Novel Inspection Technique for Electronic Components Using Thermography (NITECT)

by Haochen Liu, Lawrence Tinsley, Wayne Lam, Sri Addepalli, Xiaochen Liu, Andrew Starr and Yifan Zhao

Sensors 2020, 20(17), 5013; https://doi.org/10.3390/s20175013 - 3 Sep 2020

Cited by 4 | Viewed by 3806

Abstract

Unverified or counterfeited electronic components pose a big threat globally because they could lead to malfunction of safety-critical systems and reduced reliability of high-hazard assets. The current inspection techniques are either expensive or slow, which becomes the bottleneck of large volume inspection. As a complement of the existing inspection capabilities, a pulsed thermography-based screening technique is proposed in this paper using a digital twin methodology. A FEM-based simulation unit is initially developed to simulate the internal structure of electronic components with deviations of multiple physical properties, informed by X-ray data, along with its thermal behaviour under exposure to instantaneous heat. A dedicated physical inspection unit is then integrated to verify the simulation unit and further improve the simulation by taking account of various uncertainties caused by equipment and samples. Principle component analysis is used for feature extraction, and then a set of machine learning-based classifiers are employed for quantitative classification. Evaluation results of 17 chips from different sources successfully demonstrate the effectiveness of the proposed technique. Full article

(This article belongs to the Special Issue Sensors for Fault Diagnosis and Prognostics)

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21 pages, 4302 KiB

Open AccessArticle

Robust Fault Estimation Using the Intermediate Observer: Application to the Quadcopter

by Ngoc Phi Nguyen, Tuan Tu Huynh, Xuan Phu Do, Nguyen Xuan Mung and Sung Kyung Hong

Sensors 2020, 20(17), 4917; https://doi.org/10.3390/s20174917 - 31 Aug 2020

Cited by 13 | Viewed by 3089

Abstract

In this paper, an actuator fault estimation technique is proposed for quadcopters under uncertainties. In previous studies, matching conditions were required for the observer design, but they were found to be complex for solving linear matrix inequalities (LMIs). To overcome these limitations, in this study, an improved intermediate estimator algorithm was applied to the quadcopter model, which can be used to estimate actuator faults and system states. The system stability was validated using Lyapunov theory. It was shown that system errors are uniformly ultimately bounded. To increase the accuracy of the proposed fault estimation algorithm, a magnitude order balance method was applied. Experiments were verified with four scenarios to show the effectiveness of the proposed algorithm. Two first scenarios were compared to show the effectiveness of the magnitude order balance method. The remaining scenarios were described to test the reliability of the presented method in the presence of multiple actuator faults. Different from previous studies on observer-based fault estimation, this proposal not only can estimate the fault magnitude of the roll, pitch, yaw, and thrust channel, but also can estimate the loss of control effectiveness of each actuator under uncertainties. Full article

(This article belongs to the Special Issue Sensors for Fault Diagnosis and Prognostics)

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16 pages, 2473 KiB

Open AccessArticle

Cross-Machine Fault Diagnosis with Semi-Supervised Discriminative Adversarial Domain Adaptation

by Xiaodong Wang, Feng Liu and Dongdong Zhao

Sensors 2020, 20(13), 3753; https://doi.org/10.3390/s20133753 - 4 Jul 2020

Cited by 18 | Viewed by 3814

Abstract

Bearings are ubiquitous in rotating machinery and bearings in good working conditions are essential for the availability and safety of the machine. Various intelligent fault diagnosis models have been widely studied aiming to prevent system failures. These data-driven fault diagnosis models work well when training data and testing data are from the same distribution, which is not easy to sustain in industry since the working environment of rotating machinery is often subject to change. Recently, the domain adaptation methods for fault diagnosis between different working conditions have been extensively researched, which fully utilize the labeled data from the same machine under different working conditions to address this domain shift diploma. However, for a target machine with seldom occurred faulty data under any working conditions, the domain adaptation approaches between working conditions are not applicable. Hence, the cross-machine fault diagnosis tasks are recently proposed to utilize the labeled data from related but not identical machines. The larger domain shift between machines makes the cross-machine fault diagnosis a more challenging task. The large domain shift may cause the well-trained model on source domain deteriorates on target domain, and the ambiguous samples near the decision boundary are prone to be misclassified. In addition, the sparse faulty samples in target domain make a class-imbalanced scenario. To address the two issues, in this paper we propose a semi-supervised adversarial domain adaptation approach for cross-machine fault diagnosis which incorporates the virtual adversarial training and batch nuclear-norm maximization to make the fault diagnosis robust and discriminative. Experiments of transferring between three bearing datasets show that the proposed method is able to effectively learn a discriminative model given only a labeled faulty sample of each class in target domain. The research provides a feasible approach for knowledge transfer in fault diagnosis scenarios. Full article

(This article belongs to the Special Issue Sensors for Fault Diagnosis and Prognostics)

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20 pages, 7110 KiB

Open AccessArticle

Degradation Monitoring of Insulation Systems Used in Low-Voltage Electromagnetic Coils under Thermal Loading Conditions from a Creep Point of View

by Kai Wang, Haifeng Guo, Aidong Xu and Michael Pecht

Sensors 2020, 20(13), 3696; https://doi.org/10.3390/s20133696 - 1 Jul 2020

Cited by 7 | Viewed by 3988

Abstract

Electromagnetic coils are a key component in a variety of systems and are widely used in many industries. Because their insulation usually fails suddenly and can have catastrophic effects, degradation monitoring of coil insulation systems plays a vital role in avoiding unexpected machine shutdown. The existing insulation degradation monitoring methods are based on assessing the change of coil high-frequency electrical parameter response, whereas the effects of the insulation failure mechanisms are not considered, which leads to inconsistency between experimental results. Therefore, this paper investigates degradation monitoring of coil insulation systems under thermal loading conditions from a creep point of view. Inter-turn insulation creep deformation is identified as a quantitative index to manifest insulation degradation changes at the micro level. A method is developed to map coil high-frequency electrical monitoring parameters to inter-turn insulation creep deformation in order to bridge the gap between the micro-level and macro-level changes during the incipient insulation degradation process. Thermally accelerated tests are performed to validate the developed method. The mapping method helps to determine the physical meaning of coil electrical monitoring parameters and presents opportunities for predictive maintenance of machines that incorporate electromagnetic coils. Full article

(This article belongs to the Special Issue Sensors for Fault Diagnosis and Prognostics)

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20 pages, 8090 KiB

Open AccessArticle

A Novel Approach for Acoustic Signal Processing of a Drum Shearer Based on Improved Variational Mode Decomposition and Cluster Analysis

by Changpeng Li, Tianhao Peng and Yanmin Zhu

Sensors 2020, 20(10), 2949; https://doi.org/10.3390/s20102949 - 22 May 2020

Cited by 16 | Viewed by 3106

Abstract

During operation, the acoustic signal of the drum shearer contains a wealth of information. The monitoring or diagnosis system based on acoustic signal has obvious advantages. However, the signal is challenging to extract and recognize. Therefore, this paper proposes an approach for acoustic signal processing of a shearer based on the parameter optimized variational mode decomposition (VMD) method and a clustering algorithm. First, the particle swarm optimization (PSO) algorithm searched for the best parameter combination of the VMD. According to the results, the approach determined the number of modes and penalty parameters for VMD. Then the improved VMD algorithm decomposed the acoustic signal. It selected the ideal component through the minimum envelope entropy. The PSO was designed to optimize the clustering analysis, and the minimum envelope entropy of the acoustic signal was regarded as the feature for classification. We then use a shearer simulation platform to collect the acoustic signal and use the approach proposed in this paper to process and classify the signal. The experimental results show that the approach proposed can effectively extract the features of the acoustic signal of the shearer. The recognition accuracy of the acoustic signal was high, which has practical application value. Full article

(This article belongs to the Special Issue Sensors for Fault Diagnosis and Prognostics)

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17 pages, 6388 KiB

Open AccessArticle

A Novel Fault Diagnosis Scheme for Rolling Bearing Based on Convex Optimization in Synchroextracting Chirplet Transform

by Guanghui You, Yong Lv, Yefeng Jiang and Cancan Yi

Sensors 2020, 20(10), 2813; https://doi.org/10.3390/s20102813 - 15 May 2020

Cited by 8 | Viewed by 2777

Abstract

Synchroextracting transform (SET) developed from synchrosqueezing transform (SST) is a novel time-frequency (TF) analysis method. Its concentrated TF spectrum is obtained by applying a synchroextracting operator into TF transformation co-efficients on the TF plane. For this class of post-processing TF analysis methods, the main research focuses on the accurate estimation of instantaneous frequency (IF). However, the performance of TF analysis is greatly affected by the strong frequency modulation (FM) signal. In particular, the actual measured mechanical vibration signals always contain strong background noise, which decreases the resolution of TF representation, resulting in an inaccurate ridge extraction. To solve this problem, an improved penalty function based on the convex optimization scheme is firstly introduced for signal denoising. Based on the superiority of the linear chirplet transform (LCT) in dealing with modulated signals, the synchroextracting chirplet transform (SECT) is employed to sharpen the TF representation after the convex optimization denoising operation. To verify the effectiveness of the proposed method, the numerical simulation signals and the measured fault signals of rolling bearing are carried out, respectively. The results demonstrate that the proposed method leads to a better solution in rolling bearing fault feature extraction. Full article

(This article belongs to the Special Issue Sensors for Fault Diagnosis and Prognostics)

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16 pages, 5926 KiB

Open AccessArticle

Fault Diagnosis of Planetary Gearbox Based on Adaptive Order Bispectrum Slice and Fault Characteristics Energy Ratio Analysis

by Zhaoyang Shen, Zhanqun Shi, Dong Zhen, Hao Zhang and Fengshou Gu

Sensors 2020, 20(8), 2433; https://doi.org/10.3390/s20082433 - 24 Apr 2020

Cited by 6 | Viewed by 3052

Abstract

The vibration of a planetary gearbox (PG) is complex and mutually modulated, which makes the weak features of incipient fault difficult to detect. To target this problem, a novel method, based on an adaptive order bispectrum slice (AOBS) and the fault characteristics energy ratio (FCER), is proposed. The order bispectrum (OB) method has shown its effectiveness in the feature extraction of bearings and fixed-shaft gearboxes. However, the effectiveness of the PG still needs to be explored. The FCER is developed to sum up the fault information, which is scattered by mutual modulation. In this method, the raw vibration signal is firstly converted to that in the angle domain. Secondly, the characteristic slice of AOBS is extracted. Different from the conventional OB method, the AOBS is extracted by searching for a characteristic carrier frequency adaptively in the sensitive range of signal coupling. Finally, the FCER is summed up and calculated from the fault features that were dispersed in the characteristic slice. Experimental data was processed, using both the AOBS-FCER method, and the method that combines order spectrum analysis with sideband energy ratio (OSA-SER), respectively. Results indicated that the new method is effective in incipient fault feature extraction, compared with the methods of OB and OSA-SER. Full article

(This article belongs to the Special Issue Sensors for Fault Diagnosis and Prognostics)

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Review

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28 pages, 4342 KiB

Open AccessEditor’s ChoiceReview

Review of Current Guided Wave Ultrasonic Testing (GWUT) Limitations and Future Directions

by Samuel Chukwuemeka Olisa, Muhammad A. Khan and Andrew Starr

Sensors 2021, 21(3), 811; https://doi.org/10.3390/s21030811 - 26 Jan 2021

Cited by 77 | Viewed by 10955

Abstract

Damage is an inevitable occurrence in metallic structures and when unchecked could result in a catastrophic breakdown of structural assets. Non-destructive evaluation (NDE) is adopted in industries for assessment and health inspection of structural assets. Prominent among the NDE techniques is guided wave ultrasonic testing (GWUT). This method is cost-effective and possesses an enormous capability for long-range inspection of corroded structures, detection of sundries of crack and other metallic damage structures at low frequency and energy attenuation. However, the parametric features of the GWUT are affected by structural and environmental operating conditions and result in masking damage signal. Most studies focused on identifying individual damage under varying conditions while combined damage phenomena can coexist in structure and hasten its deterioration. Hence, it is an impending task to study the effect of combined damage on a structure under varying conditions and correlate it with GWUT parametric features. In this respect, this work reviewed the literature on UGWs, damage inspection, severity, temperature influence on the guided wave and parametric characteristics of the inspecting wave. The review is limited to the piezoelectric transduction unit. It was keenly observed that no significant work had been done to correlate the parametric feature of GWUT with combined damage effect under varying conditions. It is therefore proposed to investigate this impending task. Full article

(This article belongs to the Special Issue Sensors for Fault Diagnosis and Prognostics)

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Other

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13 pages, 4207 KiB

Open AccessLetter

A Machine Learning Approach to Model Interdependencies between Dynamic Response and Crack Propagation

by Thomas Fleet, Khangamlung Kamei, Feiyang He, Muhammad A. Khan, Kamran A. Khan and Andrew Starr

Sensors 2020, 20(23), 6847; https://doi.org/10.3390/s20236847 - 30 Nov 2020

Cited by 15 | Viewed by 3107

Abstract

Accurate damage detection in engineering structures is a critical part of structural health monitoring. A variety of non-destructive inspection methods has been employed to detect the presence and severity of the damage. In this research, machine learning (ML) algorithms are used to assess the dynamic response of the system. It can predict the damage severity, damage location, and fundamental behaviour of the system. Fatigue damage data of aluminium and ABS under coupled mechanical loads at different temperatures are used to train the model. The model shows that natural frequency and temperature appear to be the most important predictive features for aluminium. It appears to be dominated by natural frequency and tip amplitude for ABS. The results also show that the position of the crack along the specimen appears to be of little importance for either material, allowing simultaneous prediction of location and damage severity. Full article

(This article belongs to the Special Issue Sensors for Fault Diagnosis and Prognostics)

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