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Intelligent Diagnostic and Prognostic Methods for Electronic Systems and Mechanical Systems

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (25 July 2022) | Viewed by 19279

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Special Issue Editors

Prof. Dr. Bing Long

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Guest Editor

School of Automation Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China
Interests: automatic test; diagnosis; prognosis; testability methods for circuits and systems
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Zhen Liu

E-Mail Website
Guest Editor

School of Automation Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China
Interests: fault diagnosis; prognostics and health management (PHM); reliability and life assessment

Special Issue Information

Dear Colleagues,

The scale of modern electronic systems or mechanical systems is becoming more complex, but the testable parameters are becoming less, which makes it difficult to locate the fault, and thus, the diagnosis cost is high. This Special Issue calls for papers on the fault diagnosis and prediction technology of complex electronic and mechanical systems such as analog circuits, lithium batteries, and gears, including but not limited to fault feature extraction, diagnostic reasoning methods, performance degradation, life prediction, etc.

Prof. Dr. Bing Long
Prof. Dr. Zhen Liu
Guest Editors

Manuscript Submission Information

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Keywords

fault diagnosis
diagnostics
diagnostic reasoning
fault feature extraction
prognostics
performance degradation
life prediction
residual useful life (RUL)
analog circuits
lithium-ion battery
gears system
data-driven methods
model-based methods
machine learning
neural network

Published Papers (12 papers)

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Editorial

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2 pages, 160 KiB

Open AccessEditorial

Special Issue on Intelligent Diagnostic and Prognostic Methods for Electronic Systems and Mechanical Systems

by Bing Long and Zhen Liu

Appl. Sci. 2022, 12(19), 10106; https://doi.org/10.3390/app121910106 - 08 Oct 2022

Viewed by 628

Abstract

Fault diagnoses and prognostics are important tools to improve system reliability [...] Full article

(This article belongs to the Special Issue Intelligent Diagnostic and Prognostic Methods for Electronic Systems and Mechanical Systems)

Research

Jump to: Editorial

20 pages, 3309 KiB

Open AccessArticle

Uncertainty-Controlled Remaining Useful Life Prediction of Bearings with a New Data-Augmentation Strategy

by Ran Wang, Fucheng Yan, Ruyu Shi, Liang Yu and Yingjun Deng

Appl. Sci. 2022, 12(21), 11086; https://doi.org/10.3390/app122111086 - 01 Nov 2022

Cited by 4 | Viewed by 1523

Abstract

The remaining useful life (RUL) of bearings based on deep learning methods has been increasingly used. However, there are still two obstacles in deep learning RUL prediction: (1) the training process of the deep learning model requires enough data, but run-to-failure data are limited in the actual industry; (2) the mutual dependence between RUL predictions at different time instants are commonly ignored in existing RUL prediction methods. To overcome these problems, a RUL prediction method combining the data augmentation strategy and Wiener–LSTM network is proposed. First, the Sobol sampling strategy is implemented to augment run-to-failure data based on the degradation model. Then, the Wiener–LSTM model is developed for the RUL prediction of bearings. Different from the existing LSTM-based bearing RUL methods, the Wiener–LSTM model utilizes the Wiener process to represent the mutual dependence between the predicted RUL results at different time instants and embeds the Wiener process into the LSTM to control the uncertainty of the result. A joint optimization strategy is applied in the construction of the loss function. The efficacy and superiority of the proposed method are verified on a rolling bearing dataset obtained from the PRONOSTIA platform. Compared with the conventional bearing RUL prediction methods, the proposed method can effectively augment the bearing run-to-failure data and, thus, improve the prediction results. Meanwhile, fluctuations of the bearing RUL prediction result are significantly suppressed by the proposed method, and the prediction errors of the proposed method are much lower than other comparative methods. Full article

(This article belongs to the Special Issue Intelligent Diagnostic and Prognostic Methods for Electronic Systems and Mechanical Systems)

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17 pages, 5022 KiB

Open AccessArticle

Impact Load Sparse Recognition Method Based on Mc Penalty Function

by Hongjun Wang, Xiang Zhang, Zhengbo Wang and Shucong Liu

Appl. Sci. 2022, 12(16), 8147; https://doi.org/10.3390/app12168147 - 15 Aug 2022

Cited by 1 | Viewed by 1003

Abstract

The rotor system is an important part of large-scale rotating machinery. Bearings, as a key component of the rotor system, play a vital role in the healthy operation of the rotor system. The bearings operate under harsh conditions such as high temperature, high pressure, and high speed. They are complex and extremely prone to failure, especially when the bearing is affected by impact load, which seriously affects the remaining service life of the bearing. Uneven bearing friction, caused by the impact, is one of the main factors that cause premature failure of the bearing. The early identification of shock loads and reasonable measures are extremely important for the safe operation of equipment. This paper proposes an impact load identification method based on the sparse decomposition of the Mini-max concave penalty function (Mini-max concave penalty function, MC). The method uses the MC penalty function to reconstruct the regularized sparse recognition model, and then uses the improved original dual interior point method to solve the problem. This model realizes the identification of vibration and shock loads. Relevant experimental verification was carried out, and the results show that the sparse decomposition result based on the MC penalty function is better than the L1-regularized sparse decomposition result, and the noise is well suppressed in the non-loaded area of the impact load. This method can be applied to the early fault diagnosis of the vibration signal of the gas turbine rotor. Full article

(This article belongs to the Special Issue Intelligent Diagnostic and Prognostic Methods for Electronic Systems and Mechanical Systems)

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18 pages, 1097 KiB

Open AccessArticle

A Software Digital Lock-In Amplifier Method with Automatic Frequency Estimation for Low SNR Multi-Frequency Signal

by Yifan Wang, Yuhua Cheng, Kai Chen, Li Wang and Hongrong Wang

Appl. Sci. 2022, 12(13), 6431; https://doi.org/10.3390/app12136431 - 24 Jun 2022

Cited by 6 | Viewed by 1765

Abstract

In the fault diagnosis field, the fault feature signal is weak and contaminated by the noise. The lock-in amplifier is a useful tool for weak signal detection. Aiming to the amplitude error of the lock-in amplifier caused by frequency deviation between the measured signal and the reference signal, a DFT-based automatic signal frequency estimation method is studied to improve the frequency accuracy of the reference signal. Based on this frequency estimation method, a software digital lock-in amplifier method is proposed to detect the multiple frequencies signals. This proposed method can automatically measure the frequency value of the measured signal without prior frequency information. Then, the reference signals are generated through this frequency value to make the digital lock-in amplifier estimate the amplitude of the measured signal. Moreover, an iterative structure is used to implement the multiple frequencies signal measurement. The frequencies and amplitudes measurement accuracies are tested. Under different SNR conditions, the frequency relative error is less than

0.1 %

. In addition, the amplitude relative error with different signal frequencies is less than

1.7 %

when the SNR is −1 dB. This proposed software digital lock-in amplifier method has a higher signal frequency tracking ability and amplitude measurement accuracy. Full article

(This article belongs to the Special Issue Intelligent Diagnostic and Prognostic Methods for Electronic Systems and Mechanical Systems)

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13 pages, 2997 KiB

Open AccessArticle

A Current Sharing State Estimation Method of Redundant Switched-Mode Power Supply Based on LSTM Neural Network

by Peng He, Quan Zhou, Libing Bai, Songlin Xie and Weijing Zhang

Appl. Sci. 2022, 12(7), 3303; https://doi.org/10.3390/app12073303 - 24 Mar 2022

Cited by 1 | Viewed by 1452

Abstract

Redundant Switched-mode Power supplies (SMPSs) are commonly used to improve electronic systems’ reliability, and accurate estimation of the current sharing state is significant for evaluating the system’s health. Currently, the current sharing state estimation is mainly realized by using current sensors to detect each branch’s current, and the deployment and maintenance costs are high. In this paper, a method for power supply current sharing state estimation based on LSTM recurrent neural network is proposed. By taking advantage of subtle differences in the inherent spectral characteristics of SMPSs, this method only needs to detect the voltage ripple at the switching frequency of the load terminal to estimate the output current of each power supply branch. The verification experiment on the three-power redundant experimental platform shows that the estimation error is less than 10%. The method has the characteristics of simple structure, non-invasion, convenient deployment and maintenance, so it has high application and promotion value. Full article

(This article belongs to the Special Issue Intelligent Diagnostic and Prognostic Methods for Electronic Systems and Mechanical Systems)

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17 pages, 4713 KiB

Open AccessArticle

A Robust Accuracy Weighted Random Forests Algorithm for IGBTs Fault Diagnosis in PWM Converters without Additional Sensors

by Gen Qiu, Fan Wu, Kai Chen and Li Wang

Appl. Sci. 2022, 12(4), 2121; https://doi.org/10.3390/app12042121 - 17 Feb 2022

Cited by 6 | Viewed by 1403

Abstract

When an insulated-gate bipolar transistor (IGBT) open-circuit fault occurs, a three-phase pulse-width modulated (PWM) converter can usually keep working, which will lead to system instability and more serious secondary faults. The fault detection and diagnosis of the converter is extremely necessary to improve the reliability of the power supply system. In order to solve the problem of fault misdiagnosis caused by parameters disturbance, this paper proposes a robust accuracy weighted random forests online fault diagnosis model to accurately locate various IGBTs open-circuit faults. Firstly, the fault signal features are preprocessed by using the three-phase current signal and normalization method. Based on the test accuracy of the perturbed out-of-bag data and the multiple converters test data, a robust accuracy weighted random forests algorithm is proposed for extracting a mapping relationship between fault modes and current signal. In order to further improve the fault diagnosis performance, a parameter optimization model is built to optimize hyper-parameters of the proposed method. Finally, comparative simulation and online fault diagnosis experiments are carried out, and the results demonstrate the effectiveness and superiority of the method. Full article

(This article belongs to the Special Issue Intelligent Diagnostic and Prognostic Methods for Electronic Systems and Mechanical Systems)

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13 pages, 2157 KiB

Open AccessArticle

The Research on the Signal Generation Method and Digital Pre-Processing Based on Time-Interleaved Digital-to-Analog Converter for Analog-to-Digital Converter Testing

by Li Wang, Wenli Chen, Kai Chen, Renjun He and Wenjian Zhou

Appl. Sci. 2022, 12(3), 1704; https://doi.org/10.3390/app12031704 - 07 Feb 2022

Cited by 4 | Viewed by 1621

Abstract

In the high-resolution analog circuit, the performance of chips is an important part. The performance of the chips needs to be determined by testing. According to the test requirements, stimulus signal with better quality and performance is necessary. The main research direction is how to generate high-resolution and high-speed analog signal when there is no suitable high-resolution and high-speed digital-to-analog converter (DAC) chip available. In this paper, we take the high-resolution analog-to-digital converter (ADC) chips test as an example; this article uses high-resolution DAC chips and multiplexers to generate high-resolution high-speed signals that can be used for testing high-resolution ADC chips based on the principle of time-alternating sampling. This article explains its method, analyzes its error and proposes a digital pre-processing method to reduce the error. Finally, the actual circuit is designed, and the method is verified on the circuit. The test results prove the effectiveness of this method for generating high-resolution ADC test signals. Full article

(This article belongs to the Special Issue Intelligent Diagnostic and Prognostic Methods for Electronic Systems and Mechanical Systems)

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19 pages, 4338 KiB

Open AccessFeature PaperArticle

A Novel Fault Diagnosis Method for Analog Circuits Based on Multi-Input Deep Residual Networks with an Improved Empirical Wavelet Transform

by Zhen Liu, Xuemei Liu, Songlin Xie, Junhai Wang and Xiuyun Zhou

Appl. Sci. 2022, 12(3), 1675; https://doi.org/10.3390/app12031675 - 06 Feb 2022

Cited by 11 | Viewed by 2033

Abstract

Analog circuits play an essential role in electronic systems. To strengthen the reliability of sophisticated electronic circuits, this paper proposes a novel analog circuit fault diagnosis method. Compared with traditional fault diagnosis, the fault diagnosis process in this paper uses a square wave as the stimulus of the circuit under test (CUT), which is beneficial for obtaining the response of the CUT with rich time and frequency domain information. The improved empirical wavelet transform (EWT), which can more accurately extract the amplitude modulated–frequency modulated (AM-FM) components, is used to preprocess the original response. Finally, based on the preprocessed data, a multi-input deep residual network (ResNet) is constructed for fault feature extraction and fault classification. The multi-input ResNet is a powerful approach for learning the fault characteristics of the CUT under different faults by learning the characteristics of the AM-FM components. The effectiveness of the method proposed in this paper is verified by comparing different fault diagnosis methods. Full article

(This article belongs to the Special Issue Intelligent Diagnostic and Prognostic Methods for Electronic Systems and Mechanical Systems)

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11 pages, 1849 KiB

Open AccessFeature PaperArticle

A Novel Remaining Useful Life Prediction Method for Hydrogen Fuel Cells Based on the Gated Recurrent Unit Neural Network

by Bing Long, Kunping Wu, Pengcheng Li and Meng Li

Appl. Sci. 2022, 12(1), 432; https://doi.org/10.3390/app12010432 - 03 Jan 2022

Cited by 24 | Viewed by 2260

Abstract

The remaining useful life (RUL) prediction for hydrogen fuel cells is an important part of its prognostics and health management (PHM). Artificial neural networks (ANNs) are proven to be very effective in RUL prediction, as they do not need to understand the failure mechanisms behind hydrogen fuel cells. A novel RUL prediction method for hydrogen fuel cells based on the gated recurrent unit ANN is proposed in this paper. Firstly, the data were preprocessed to remove outliers and noises. Secondly, the performance of different neural networks is compared, including the back propagation neural network (BPNN), the long short-term memory (LSTM) network and the gated recurrent unit (GRU) network. According to our proposed method based on GRU, the root mean square error was 0.0026, the mean absolute percentage error was 0.0038 and the coefficient of determination was 0.9891 for the data from the challenge datasets provided by FCLAB Research Federation, when the prediction starting point was 650 h. Compared with the other RUL prediction methods based on the BPNN and the LSTM, our prediction method is better in both prediction accuracy and convergence rate. Full article

(This article belongs to the Special Issue Intelligent Diagnostic and Prognostic Methods for Electronic Systems and Mechanical Systems)

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13 pages, 917 KiB

Open AccessArticle

Landslide Displacement Prediction Method Based on GA-Elman Model

by Chenhui Wang, Yijiu Zhao, Libing Bai, Wei Guo and Qingjia Meng

Appl. Sci. 2021, 11(22), 11030; https://doi.org/10.3390/app112211030 - 21 Nov 2021

Cited by 16 | Viewed by 1434

Abstract

The deformation process of landslide displacement has complex nonlinear characteristics. In view of the problems of large error, slow convergence and poor stability of the traditional neural network prediction model, in order to better realize the accurate and effective prediction of landslide displacement, this research proposes a landslide displacement prediction model based on Genetic Algorithm (GA) optimized Elman neural network. This model combines the GA with the Elman neural network to optimize the weights, thresholds and the number of hidden neurons of the Elman neural network. It gives full play to the dynamic memory function of the Elman neural network, overcomes the problems that a single Elman neural network can easily fall into local minimums and the neuron data is difficult to determine, thereby effectively improving the prediction performance of the neural network prediction model. The displacement monitoring data of a slow-varying landslide in the Guizhou karst mountainous area are selected to predict and verify the landslide displacement, and the results are compared with the traditional Elman neural network prediction results. The results show that the prediction results of GA-Elman model are in good agreement with the actual monitoring data of landslide. The average error of the model is low and the prediction accuracy is high, which proves that the GA-Elman model can play a role in the prediction of landslide displacement and can provide reference for the early warning of landslide displacement deformation. Full article

(This article belongs to the Special Issue Intelligent Diagnostic and Prognostic Methods for Electronic Systems and Mechanical Systems)

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20 pages, 2938 KiB

Open AccessArticle

Data-Driven Health Assessment in a Flight Control System under Uncertain Conditions

by Jie Chen, Yuyang Zhao, Xiaofeng Xue, Runfeng Chen and Yingjian Wu

Appl. Sci. 2021, 11(21), 10107; https://doi.org/10.3390/app112110107 - 28 Oct 2021

Cited by 7 | Viewed by 1685

Abstract

PHM technology plays an increasingly significant role in modern aviation condition-based maintenance. As an important part of prognostics and health management (PHM), a health assessment can effectively estimate the health status of a system and provide support for maintenance decision making. However, in actual conditions, various uncertain factors will amplify assessment errors and cause large fluctuations in assessment results. In this paper, uncertain factors are incorporated into flight control system health assessment modeling. First, four uncertain factors of health assessment characteristic parameters are quantified and described by the extended λ-PDF method to acquire their probability distribution function. Secondly, a Monte Carlo simulation (MCS) is used to simulate a flight control system health assessment process with uncertain factors. Thirdly, the probability distribution of the output health index is solved by the maximum entropy principle. Finally, the proposed model was verified with actual flight data. The comparison between assessment results with and without uncertain factors shows that a health assessment conducted under uncertain conditions can reduce the impact of the uncertainty of outliers on the assessment results and make the assessment results more stable; therefore, the false alarm rate can be reduced. Full article

(This article belongs to the Special Issue Intelligent Diagnostic and Prognostic Methods for Electronic Systems and Mechanical Systems)

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15 pages, 6334 KiB

Open AccessArticle

Intelligent Detection Methods of Electrical Connection Faults in RF Circuits

by Ziren Wang, Jiaqi Li, George T. Flowers, Jinchun Gao, Kaixuan Song, Wei Yi and Zhongyang Cheng

Appl. Sci. 2021, 11(21), 9973; https://doi.org/10.3390/app11219973 - 25 Oct 2021

Cited by 2 | Viewed by 1527

Abstract

Printed circuit boards (PCBs) have a large number of electrical connection nodes. Exposure to harsh environments may lead to connection faults in these nodes. In the present work, intelligent detection methods for electrical connection faults were studied. Specifically, the fault characteristics of connectors, bonding wires and solder balls in the frequency domain were analyzed. The reflection and transmission parameters of an example filter circuit with electrical connection faults were calculated using the Simulation Program with Integrated Circuit Emphasis (SPICE). With these obtained electrical parameters, three machine learning algorithms were used to detect example electrical connection faults for the example circuit. Based upon the performance evaluations of the three algorithms, one can conclude that machine-learning-based intelligent fault detection is a promising technique in diagnosing circuit faults due to electrical connection issues with high accuracy and lower time cost as compared to current manual processes. Full article

(This article belongs to the Special Issue Intelligent Diagnostic and Prognostic Methods for Electronic Systems and Mechanical Systems)

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