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Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems

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

Deadline for manuscript submissions: closed (1 November 2022) | Viewed by 25965

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Special Issue Editors

Dr. Yongbo Li

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Guest Editor

School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China
Interests: information fusion; digital twin technology; structural health monitoring; fault diagnosis and prognosis; system reliability analysis; dynamic modelling of mechanical systems
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Bing Li

E-Mail Website
Guest Editor

School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China
Interests: structural health monitoring; elastic-wave and vibration control; wave mechanics; non-destructive testing; elastic/acoustic metamaterials
Special Issues, Collections and Topics in MDPI journals

Dr. Jinchen Ji

E-Mail Website
Guest Editor

School of Mechanical and Mechatronic Engineering, Faculty of Engineering and IT, University of Technology, Sydney, P.O. Box 123, Broadway, NSW 2007, Australia
Interests: dynamic modelling; vibration analysis; vibration control; fatigue analysis; stability analysis; FEM analysis; cooperative control of multi-agent systems
Special Issues, Collections and Topics in MDPI journals

Dr. Hamed Kalhori

E-Mail Website
Guest Editor

1. School of Mechanical and Mechatronic Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia
2. Department of Mechanical Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan 65167-38695, Iran
Interests: structural health monitoring; inverse problems; sensors and signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last few decades, the field of fault diagnostics and structural health management has been experiencing rapid developments. The reliability, availability, and safety of engineering systems can be significantly improved by implementing multifaceted strategies of in situ diagnostics and prognostics. With the development of intelligence algorithms, smart sensors, and advanced data collection and modeling techniques, this challenging research area has been receiving ever-increasing attention in both fundamental research and engineering applications. This has been strongly supported by the extensive applications ranging from aerospace, automotive, transport, manufacturing, and processing industries to defense and infrastructure industry. In view of the current state of the art and advances in this fast-growing discipline, in this Special Issue, we are calling for papers related to all aspects of fault diagnostics, damage identification, and prognostics-based health management. A wide range of topics are covered, including new theories, methodologies, optimization, and applications in sensing, measurement, modeling, control, and prognostics. Topics include but are not limited to:

Measuring techniques for condition monitoring;
Reliability analysis and design;
Signal processing of measured data;
Feature extraction of measured data;
Fault diagnosis for prognosis and health management (PHM);
Degradation modeling of measured data;;
Measurement error analysis;
RUL prediction method based on intelligent algorithms;
Maintenance strategy optimization;
Structural health monitoring (SHM);
Non-destructive testing (NDT).

Dr. Yongbo Li
Prof. Dr. Bing Li
Dr. Jinchen Ji
Dr. Hamed Kalhori
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.

Published Papers (12 papers)

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Editorial

Jump to: Research

3 pages, 165 KiB

Open AccessEditorial

Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems

by Yongbo Li, Bing Li, Jinchen Ji and Hamed Kalhori

Sensors 2022, 22(24), 10002; https://doi.org/10.3390/s222410002 - 19 Dec 2022

Viewed by 1096

Abstract

Fault diagnosis and health condition monitoring have always been critical issues in the engineering research community [...] Full article

(This article belongs to the Special Issue Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems)

Research

Jump to: Editorial

15 pages, 7774 KiB

Open AccessArticle

Composite Multiscale Transition Permutation Entropy-Based Fault Diagnosis of Bearings

by Jing Guo, Biao Ma, Tiangang Zou, Lin Gui and Yongbo Li

Sensors 2022, 22(20), 7809; https://doi.org/10.3390/s22207809 - 14 Oct 2022

Cited by 4 | Viewed by 1140

Abstract

When considering the transition probability matrix of ordinal patterns, transition permutation entropy (TPE) can effectively extract fault features by quantifying the irregularity and complexity of signals. However, TPE can only characterize the complexity of the vibration signals at a single scale. Therefore, a multiscale transition permutation entropy (MTPE) technique has been proposed. However, the original multiscale method still has some inherent defects in the coarse-grained process, such as considerably shortening the length of time series at large scale, which leads to a low entropy evaluation accuracy. In order to solve these problems, a composite multiscale transition permutation entropy (CMTPE) method was proposed in order to improve the incomplete coarse-grained analysis of MTPE by avoiding the loss of some key information in the original fault signals, and to improve the performance of feature extraction, robustness to noise, and accuracy of entropy estimation. A fault diagnosis strategy based on CMTPE and an extreme learning machine (ELM) was proposed. Both simulation and experimental signals verified the advantages of the proposed CMTPE method. The results show that, compared with other comparison strategies, this strategy has better robustness, and can carry out feature recognition and bearing fault diagnosis more accurately and with improved stability. Full article

(This article belongs to the Special Issue Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems)

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15 pages, 24569 KiB

Open AccessArticle

Vibro-Acoustic Distributed Sensing for Large-Scale Data-Driven Leak Detection on Urban Distribution Mains

by Lili Bykerk and Jaime Valls Miro

Sensors 2022, 22(18), 6897; https://doi.org/10.3390/s22186897 - 13 Sep 2022

Cited by 10 | Viewed by 2186

Abstract

Non-surfacing leaks constitute the dominant source of water losses for utilities worldwide. This paper presents advanced data-driven analysis methods for leak monitoring using commercial field-deployable semi-permanent vibro-acoustic sensors, evaluated on live data collected from extensive multi-sensor deployments across a sprawling metropolitan city. This necessarily includes a wide variety of pipeline sizes, materials and surrounding soils, as well as leak sources and rates brought about by external factors. The novel proposition for structural pipe health monitoring shows that excellent leak/no-leak classification results (>94% accuracy) can be observed using Convolutional Neural Networks (CNNs) trained with Short-Time Fourier Transforms (STFTs) of the raw audio files. Most notably, it is shown how this can be achieved irrespective of the sensor used, with four models from different manufactures being part of the investigation, and over time across extended densely populated areas. Full article

(This article belongs to the Special Issue Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems)

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14 pages, 1517 KiB

Open AccessArticle

Simultaneous Sensor and Actuator Fault Reconstruction by Using a Sliding Mode Observer, Fuzzy Stability Analysis, and a Nonlinear Optimization Tool

by Samira Asadi, Mehrdad Moallem and G. Gary Wang

Sensors 2022, 22(18), 6866; https://doi.org/10.3390/s22186866 - 10 Sep 2022

Cited by 2 | Viewed by 1543

Abstract

This paper proposes a Takagi–Sugeno (TS) fuzzy sliding mode observer (SMO) for simultaneous actuator and sensor fault reconstruction in a class of nonlinear systems subjected to unknown disturbances. First, the nonlinear system is represented by a TS fuzzy model with immeasurable premise variables. By filtering the output of the TS fuzzy model, an augmented system whose actuator fault is a combination of the original actuator and sensor faults is constructed. An

H_{\infty}

performance criteria is considered to minimize the effect of the disturbance on the state estimations. Then, by using two further transformation matrices, a non-quadratic Lyapunov function (NQLF), and fmincon in MATLAB as a nonlinear optimization tool, the gains of the SMO are designed through the stability analysis of the observer. The main advantages of the proposed approach in comparison to the existing methods are using nonlinear optimization tools instead of linear matrix inequalities (LMIs), utilizing NQLF instead of simple quadratic Lyapunov functions (QLF), choosing SMO as the observer, which is robust to the uncertainties, and assuming that the premise variables are immeasurable. Finally, a practical continuous stirred tank reactor (CSTR) is considered as a nonlinear dynamic, and the numerical simulation results illustrate the superiority of the proposed approach compared to the existing methods. Full article

(This article belongs to the Special Issue Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems)

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14 pages, 3460 KiB

Open AccessArticle

Bearing Fault Diagnosis Using Piecewise Aggregate Approximation and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise

by Lei Hu, Ligui Wang, Yanlu Chen, Niaoqing Hu and Yu Jiang

Sensors 2022, 22(17), 6599; https://doi.org/10.3390/s22176599 - 01 Sep 2022

Cited by 16 | Viewed by 1603

Abstract

Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) effectively separates the fault vibration signals of rolling bearings and improves the diagnosis of rolling bearing faults. However, CEEMDAN has high memory requirements and low computational efficiency. In each iteration of CEEMDAN, fault vibration signals are added with noises, both the vibration signals added with noises and the added noises are decomposed with classical empirical mode decomposition (EMD). This paper proposes a rolling bearing fault diagnosis method that combines piecewise aggregate approximation (PAA) with CEEMDAN. PAA enables CEEMDAN to decompose long signals and to achieve enhanced diagnosis. In particular, the method first yields the vibration envelope using bandpass filtering and demodulation, then compresses the envelope using PAA, and finally decomposes the compressed signal with CEEMDAN. Test data verification results show that the proposed method is more effective and more efficient than CEEMDAN. Full article

(This article belongs to the Special Issue Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems)

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13 pages, 2599 KiB

Open AccessArticle

The Robust Multi-Scale Deep-SVDD Model for Anomaly Online Detection of Rolling Bearings

by Linlin Kou, Jiaxian Chen, Yong Qin and Wentao Mao

Sensors 2022, 22(15), 5681; https://doi.org/10.3390/s22155681 - 29 Jul 2022

Cited by 5 | Viewed by 1787

Abstract

Aiming at the online detection problem of rolling bearings, the limited amount of target bearing data leads to insufficient model in training and feature representation. It is difficult for the online detection model to construct an accurate decision boundary. To solve the problem, a multi-scale robust anomaly detection method based on data enhancement technology is proposed in this paper. Firstly, the training data are transformed into multiple subspaces through the data enhancement technology. Then, a prototype clustering method is introduced to enhance the robustness of features representation under the framework of the robust deep auto-encoding algorithm. Finally, the robust multi-scale Deep-SVDD hyper sphere model is constructed to achieve online detection of abnormal state data. Experiments are conducted on the IEEE PHM Challenge 2012 bearing data set and XJTU-TU data set. The proposed method shows much greater susceptibility to incipient faults, and it has fewer false alarms. The robust multi-scale Deep-SVDD hyper sphere model significantly improves the performance of incipient fault detection for rolling bearings. Full article

(This article belongs to the Special Issue Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems)

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15 pages, 3419 KiB

Open AccessArticle

An Oversampling Method of Unbalanced Data for Mechanical Fault Diagnosis Based on MeanRadius-SMOTE

by Feng Duan, Shuai Zhang, Yinze Yan and Zhiqiang Cai

Sensors 2022, 22(14), 5166; https://doi.org/10.3390/s22145166 - 10 Jul 2022

Cited by 18 | Viewed by 1894

Abstract

With the development of machine learning, data-driven mechanical fault diagnosis methods have been widely used in the field of PHM. Due to the limitation of the amount of fault data, it is a difficult problem for fault diagnosis to solve the problem of unbalanced data sets. Under unbalanced data sets, faults with little historical data are always difficult to diagnose and lead to economic losses. In order to improve the prediction accuracy under unbalanced data sets, this paper proposes MeanRadius-SMOTE based on the traditional SMOTE oversampling algorithm, which effectively avoids the generation of useless samples and noise samples. This paper validates the effectiveness of the algorithm on three linear unbalanced data sets and four step unbalanced data sets. Experimental results show that MeanRadius-SMOTE outperforms SMOTE and LR-SMOTE in various evaluation indicators, as well as has better robustness against different imbalance rates. In addition, MeanRadius-SMOTE can take into account the prediction accuracy of the overall and minority class, which is of great significance for engineering applications. Full article

(This article belongs to the Special Issue Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems)

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20 pages, 7697 KiB

Open AccessArticle

Partial Transfer Ensemble Learning Framework: A Method for Intelligent Diagnosis of Rotating Machinery Based on an Incomplete Source Domain

by Gang Mao, Zhongzheng Zhang, Sixiang Jia, Khandaker Noman and Yongbo Li

Sensors 2022, 22(7), 2579; https://doi.org/10.3390/s22072579 - 28 Mar 2022

Cited by 3 | Viewed by 1624

Abstract

Most cross-domain intelligent diagnosis approaches presume that the health states in training datasets are consistent with those in testing. However, it is usually difficult and expensive to collect samples under all failure states during the training stage in actual engineering; this causes the training dataset to be incomplete. These existing methods may not be favorably implemented with an incomplete training dataset. To address this problem, a novel deep-learning-based model called partial transfer ensemble learning framework (PT-ELF) is proposed in this paper. The major procedures of this study consist of three steps. First, the missing health states in the training dataset are supplemented by another dataset. Second, since the training dataset is drawn from two different distributions, a partial transfer mechanism is explored to train a weak global classifier and two partial domain adaptation classifiers. Third, a particular ensemble strategy combines these classifiers with different classification ranges and capabilities to obtain the final diagnosis result. Two case studies are used to validate our method. Results indicate that our method can provide robust diagnosis results based on an incomplete source domain under variable working conditions. Full article

(This article belongs to the Special Issue Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems)

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23 pages, 8040 KiB

Open AccessArticle

Self-Powered Self-Contained Wireless Vibration Synchronous Sensor for Fault Detection

by Ghufran Aldawood and Hamzeh Bardaweel

Sensors 2022, 22(6), 2352; https://doi.org/10.3390/s22062352 - 18 Mar 2022

Cited by 4 | Viewed by 2296

Abstract

Failure in dynamic structures poses a pressing need for fault detection systems. Interconnected sensor nodes of wireless sensor networks (WSN) offer a solution by communicating information about their surroundings. Nonetheless, these battery-powered sensors have an immense labor cost and require periodical battery maintenance and replacement. Batteries pose a significant environmental threat that is expected to cause irreversible damage to the ecosystem. We introduce a fully integrated vibration-powered energy harvester sensor system that is interfaced with a custom-developed fault detection app. Vibrations are used to power a radio frequency (RF) transmitter that is integrated with the vibration sensor subunit. The harvester-sensor unit is comprised of dual moving magnets that are bordered by coil windings for power and signal generation. The power generated from the harvester is used to operate the transmitter while the signal generated from the sensor is transmitted as a vibration signal. Transmitted values are streamed into a high precision fault detection app capable of detecting the frequency of vibrations with an error of 1%. The app employs an FFT algorithm on the transmitted data and notifies the user when a threshold vibration level is reached. The total energy consumed by the transmitter is 0.894 µJ at a 3 V operation. The operable acceleration of the system is 0.7 g [m/s²] at 5–10.6 Hz. Full article

(This article belongs to the Special Issue Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems)

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16 pages, 4147 KiB

Open AccessArticle

Study on a Fault Mitigation Scheme for Rub-Impact of an Aero-Engine Based on NiTi Wires

by Qiang Pan, Tian He, Wendong Liu, Xiaofeng Liu and Haibing Chen

Sensors 2022, 22(5), 1796; https://doi.org/10.3390/s22051796 - 24 Feb 2022

Cited by 1 | Viewed by 1389

Abstract

The aim of this study was to solve the frequently occurring rotor-stator rub-impact fault in aero-engines without causing a significant reduction in efficiency. We proposed a fault mitigation scheme, using shape memory alloy (SMA) wire, whereby the tip clearance between the rotor and the stator is adjusted. In this scheme, an acoustic emission (AE) sensor is utilized to monitor the rub-impact fault. An active control actuator is designed with pre-strained two-way SMA wires, driven by an electric current via an Arduino control board, to mitigate the rub-impact fault once it occurs. In order to investigate the feasibility of the proposed scheme, a series of tests on the material properties of NiTi wires, including heating response rate, ultimate strain, free recovery rate, and restoring force, were carried out. A prototype of the actuator was designed, manufactured, and tested under various conditions. The experimental result verifies that the proposed scheme has the potential to mitigate or eliminate the rotor-stator rub-impact fault in aero-engines. Full article

(This article belongs to the Special Issue Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems)

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19 pages, 11185 KiB

Open AccessArticle

Aircraft Landing Gear Retraction/Extension System Fault Diagnosis with 1-D Dilated Convolutional Neural Network

by Jie Chen, Qingshan Xu, Yingchao Guo and Runfeng Chen

Sensors 2022, 22(4), 1367; https://doi.org/10.3390/s22041367 - 10 Feb 2022

Cited by 4 | Viewed by 5704

Abstract

The faults of the landing gear retraction/extension(R/E) system can result in the deterioration of an aircraft’s maneuvering conditions; how to identify the faults of the landing gear R/E system has become a key issue for ensuring aircraft take-off and landing safety. In this paper, we aim to solve this problem by proposing the 1-D dilated convolutional neural network (1-DDCNN). Aiming at developing the limited feature information extraction and inaccurate diagnosis of the traditional 1-DCNN with a single feature, the 1-DDCNN selects multiple feature parameters to realize feature integration. The performance of the 1-DDCNN in feature extraction is explored. Importantly, using padding dilated convolution to multiply the receptive field of the convolution kernel, the 1-DDCNN can completely retain the feature information in the original signal. Experimental results demonstrated that the proposed method has high accuracy and robustness, which provides a novel idea for feature extraction and fault diagnosis of the landing gear R/E system. Full article

(This article belongs to the Special Issue Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems)

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18 pages, 5829 KiB

Open AccessArticle

Impact of Sensor Data Characterization with Directional Nature of Fault and Statistical Feature Combination for Defect Detection on Roll-to-Roll Printed Electronics

by Yoonjae Lee, Minho Jo, Gyoujin Cho, Changbeom Joo and Changwoo Lee

Sensors 2021, 21(24), 8454; https://doi.org/10.3390/s21248454 - 18 Dec 2021

Cited by 7 | Viewed by 3144

Abstract

Gravure printing, which is a roll-to-roll printed electronics system suitable for high-speed patterning of functional layers have advantages of being applied to flexible webs in large areas. As each of the printing procedure from inking to doctoring followed by ink transferring and setting influences the quality of the pattern geometry, it is necessary to detect and diagnose factors causing the printing defects beforehand. Data acquisition with three triaxial acceleration sensors for fault diagnosis of four major defects such as doctor blade tilting fault was obtained. To improve the diagnosis performances, optimal sensor selection with Sensor Data Efficiency Evaluation, sensitivity evaluation for axis selection with Directional Nature of Fault and feature variable optimization with Feature Combination Matrix method was applied on the raw data to form a Smart Data. Each phase carried out on the raw data progressively enhanced the diagnosis results in contents of accuracy, positive predictive value, diagnosis processing time, and data capacity. In the case of doctor blade tilting fault, the diagnosis accuracy increased from 48% to 97% with decreasing processing time of 3640 s to 16 s and the data capacity of 100 Mb to 5 Mb depending on the input data between raw data and Smart Data. Full article

(This article belongs to the Special Issue Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems)

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