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15 pages, 4140 KiB

Open AccessArticle

Voltage and Current Sensor Fault Diagnosis Method for Traction Converter with Two Stator Current Sensors

by Hongwei Tao, Tao Peng, Chao Yang, Jinqiu Gao, Chunhua Yang and Weihua Gui

Sensors 2022, 22(6), 2355; https://doi.org/10.3390/s22062355 - 18 Mar 2022

Cited by 8 | Viewed by 2247

The traction converter is one of the key components of high-speed trains. Current and voltage sensor faults in the converter may lead to feedback values deviation and system degradation, which will bring security risks to the train. This paper proposes a real-time fault [...] Read more.

The traction converter is one of the key components of high-speed trains. Current and voltage sensor faults in the converter may lead to feedback values deviation and system degradation, which will bring security risks to the train. This paper proposes a real-time fault diagnosis method for grid current, DC-link voltage and stator current sensor faults in the traction converter with two stator current sensors, which can not only detect and locate faults but also identify the types of faults. Moreover, the faults considered in this paper are incipient. First, the DC-link model is established, and the fault is detected by the residual of the DC-link voltage. Next, the differential of DC-link voltage residual is calculated, which is applied to fault location. Then, according to the change of the differential values, different fault types are determined. Finally, the hardware-in-the-loop (HIL) platform is built and the effectiveness and accuracy of the proposed method are verified by the HIL tests. Full article

(This article belongs to the Special Issue Fault Diagnosis in Transportation and Industry: Sensors, Methods, and Experimental Applications)

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21 pages, 8649 KiB

Open AccessArticle

Predictive Fault Diagnosis for Ship Photovoltaic Modules Systems Applications

by Emilio García, Eduardo Quiles, Ranko Zotovic-Stanisic and Santiago C. Gutiérrez

Sensors 2022, 22(6), 2175; https://doi.org/10.3390/s22062175 - 10 Mar 2022

Cited by 5 | Viewed by 2415

Abstract

In this paper, an application for the management and supervision by predictive fault diagnosis (PFD) of solar power generation systems is developed through a National Marine Electronics Association (NMEA) 2000 smart sensor network. Here, the NMEA 2000 network sensor devices for measuring and [...] Read more.

In this paper, an application for the management and supervision by predictive fault diagnosis (PFD) of solar power generation systems is developed through a National Marine Electronics Association (NMEA) 2000 smart sensor network. Here, the NMEA 2000 network sensor devices for measuring and supervising the parameters inherent to solar power generation and renewable energy supply are applied. The importance of renewable power generation systems in ships is discussed, as well as the causes of photovoltaic modules (PVMs) aging due to superimposed causes of degradation, which is a natural and inexorable phenomenon that affects photovoltaic installations in a special way. In ships, PVMs are doubly exposed to inclement weather (solar radiation, cold, rain, dust, humidity, snow, wind, electrical storms, etc.), pollution, and a particularly aggressive environment in terms of corrosion. PFD techniques for the real-world installation and safe navigation of PVMs are discussed. A specific method based on the online analysis of the time-series data of random and seasonal I–V parameters is proposed for the comparative trend analyses of solar power generation. The objective is to apply PFD using as predictor symptom parameter (PS) the generated power decrease in affected PVMs. This PFD method allows early fault detection and isolation, whose appearance precedes by an adequate margin of maneuver, from the point of view of maintenance tasks applications. This early detection can stop the cumulative degradation phenomenon that causes the development of the most frequent and dangerous failure modes of solar modules, such as hot-spots. It is concluded that these failure modes can be conveniently diagnosed by performing comparative trend analyses of the measured power parameters by NMEA sensors. Full article

(This article belongs to the Special Issue Fault Diagnosis in Transportation and Industry: Sensors, Methods, and Experimental Applications)

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20 pages, 6719 KiB

Open AccessArticle

Intelligent Diagnosis of Rolling Element Bearing Based on Refined Composite Multiscale Reverse Dispersion Entropy and Random Forest

by Aiqiang Liu, Zuye Yang, Hongkun Li, Chaoge Wang and Xuejun Liu

Sensors 2022, 22(5), 2046; https://doi.org/10.3390/s22052046 - 06 Mar 2022

Cited by 12 | Viewed by 2300

Abstract

Rolling bearings are the vital components of large electromechanical equipment, thus it is of great significance to develop intelligent fault diagnoses for them to improve equipment operation reliability. In this paper, a fault diagnosis method based on refined composite multiscale reverse dispersion entropy [...] Read more.

Rolling bearings are the vital components of large electromechanical equipment, thus it is of great significance to develop intelligent fault diagnoses for them to improve equipment operation reliability. In this paper, a fault diagnosis method based on refined composite multiscale reverse dispersion entropy (RCMRDE) and random forest is developed. Firstly, rolling bearing vibration signals are adaptively decomposed by variational mode decomposition (VMD), and then the RCMRDE values of 25 scales are calculated for original signal and each decomposed component as the initial feature set. Secondly, based on the joint mutual information maximization (JMIM) algorithm, the top 15 sensitive features are selected as a new feature set and feed into random forest model to identify bearing health status. Finally, to verify the effectiveness and superiority of the presented method, actual data acquisition and analysis are performed on the bearing fault diagnosis experimental platform. These results indicate that the presented method can precisely diagnose bearing fault types and damage degree, and the average identification accuracy rate is 97.33%. Compared with the refine composite multiscale dispersion entropy (RCMDE) and multiscale dispersion entropy (MDE), the fault diagnosis accuracy is improved by 2.67% and 8.67%, respectively. Furthermore, compared with the RCMRDE method without VMD decomposition, the fault diagnosis accuracy is improved by 3.67%. Research results prove that a better feature extraction technique is proposed, which can effectively overcome the deficiency of existing entropy and significantly enhance the ability of fault identification. Full article

(This article belongs to the Special Issue Fault Diagnosis in Transportation and Industry: Sensors, Methods, and Experimental Applications)

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12 pages, 4371 KiB

Open AccessArticle

Tool-Condition Diagnosis Model with Shock-Sharpening Algorithm for Drilling Process

by Byeonghui Park, Yoonjae Lee, Myeonghwan Yeo, Haemi Lee, Changbeom Joo and Changwoo Lee

Sensors 2022, 22(5), 1975; https://doi.org/10.3390/s22051975 - 03 Mar 2022

Cited by 3 | Viewed by 1742

Abstract

Fault diagnosis systems are used to improve the productivity and reduce the costs of the manufacturing process. However, the feature variables in existing systems are extracted based on the classification performance of the final model, thereby limiting their applications to models with different [...] Read more.

Fault diagnosis systems are used to improve the productivity and reduce the costs of the manufacturing process. However, the feature variables in existing systems are extracted based on the classification performance of the final model, thereby limiting their applications to models with different conditions. This paper proposes an algorithm to improve the characteristics of feature variables by considering the cutting conditions. Regardless of the frequency band, the noise of the measurement data was reduced through an oversampling method, setting a window length through a cutter sampling frequency, and improving its sensitivity to shock signal. An experiment was subsequently performed to confirm the performance of the model. Using normal and wear tools on AI7075 and SM45C, the diagnosis accuracies were 97.1% and 95.6%, respectively, with a reduction of 85% and 83%, respectively, in the time required to develop a diagnosis model. Therefore, the proposed algorithm reduced the model computation time and developed a model with high accuracy by enhancing the characteristics of the feature variable. The results of this study can contribute significantly to the establishment of a high-precision monitoring system for various processing processes. Full article

(This article belongs to the Special Issue Fault Diagnosis in Transportation and Industry: Sensors, Methods, and Experimental Applications)

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14 pages, 3106 KiB

Open AccessArticle

Bearing Fault Reconstruction Diagnosis Method Based on ResNet-152 with Multi-Scale Stacked Receptive Field

by Hu Yu, Xiaodong Miao and Hua Wang

Sensors 2022, 22(5), 1705; https://doi.org/10.3390/s22051705 - 22 Feb 2022

Cited by 8 | Viewed by 2422

Abstract

The axle box in the bogie system of subway trains is a key component connecting primary damper and the axle. In order to extract deep features and large-scale fault features for rapid diagnosis, a novel fault reconstruction characteristics classification method based on deep [...] Read more.

The axle box in the bogie system of subway trains is a key component connecting primary damper and the axle. In order to extract deep features and large-scale fault features for rapid diagnosis, a novel fault reconstruction characteristics classification method based on deep residual network with a multi-scale stacked receptive field for rolling bearings of a subway train axle box is proposed. Firstly, multi-layer stacked convolutional kernels and methods to insert them into ultra-deep residual networks are developed. Then, the original vibration signals of four fault characteristics acquired are reconstructed with a Gramian angular summation field and trainable large-scale 2D time-series images are obtained. In the end, the experimental results show that ResNet-152-MSRF has a low complexity of network structure, less trainable parameters than general convolutional neural networks, and no significant increase in network parameters and calculation time after embedding multi-layer stacked convolutional kernels. Moreover, there is a significant improvement in accuracy compared to lower depths, and a slight improvement in accuracy compared to networks than unembedded multi-layer stacked convolutional kernels. Full article

(This article belongs to the Special Issue Fault Diagnosis in Transportation and Industry: Sensors, Methods, and Experimental Applications)

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22 pages, 7588 KiB

Open AccessArticle

A Semi-Supervised Approach with Monotonic Constraints for Improved Remaining Useful Life Estimation

by Diego Nieves Avendano, Nathan Vandermoortele, Colin Soete, Pieter Moens, Agusmian Partogi Ompusunggu, Dirk Deschrijver and Sofie Van Hoecke

Sensors 2022, 22(4), 1590; https://doi.org/10.3390/s22041590 - 18 Feb 2022

Cited by 10 | Viewed by 2298

Abstract

Remaining useful life is of great value in the industry and is a key component of Prognostics and Health Management (PHM) in the context of the Predictive Maintenance (PdM) strategy. Accurate estimation of the remaining useful life (RUL) is helpful for optimizing maintenance [...] Read more.

Remaining useful life is of great value in the industry and is a key component of Prognostics and Health Management (PHM) in the context of the Predictive Maintenance (PdM) strategy. Accurate estimation of the remaining useful life (RUL) is helpful for optimizing maintenance schedules, obtaining insights into the component degradation, and avoiding unexpected breakdowns. This paper presents a methodology for creating health index models with monotonicity in a semi-supervised approach. The health indexes are then used for enhancing remaining useful life estimation models. The methodology is evaluated on two bearing datasets. Results demonstrate the advantage of using the monotonic health index for obtaining insights into the bearing degradation and for remaining useful life estimation. Full article

(This article belongs to the Special Issue Fault Diagnosis in Transportation and Industry: Sensors, Methods, and Experimental Applications)

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29 pages, 13366 KiB

Open AccessArticle

Solar Panels String Predictive and Parametric Fault Diagnosis Using Low-Cost Sensors

by Emilio García, Neisser Ponluisa, Eduardo Quiles, Ranko Zotovic-Stanisic and Santiago C. Gutiérrez

Sensors 2022, 22(1), 332; https://doi.org/10.3390/s22010332 - 03 Jan 2022

Cited by 15 | Viewed by 4017

Abstract

This work proposes a method for real-time supervision and predictive fault diagnosis applicable to solar panel strings in real-world installations. It is focused on the detection and parametric isolation of fault symptoms through the analysis of the Voc-Isc curves. The method performs early, [...] Read more.

This work proposes a method for real-time supervision and predictive fault diagnosis applicable to solar panel strings in real-world installations. It is focused on the detection and parametric isolation of fault symptoms through the analysis of the Voc-Isc curves. The method performs early, systematic, online, automatic, permanent predictive supervision, and diagnosis of a high sampling frequency. It is based on the supervision of predictive electrical parameters easily accessible by the design of its architecture, whose detection and isolation precedes with an adequate margin of maneuver, to be able to alert and stop by means of automatic disconnection the degradation phenomenon and its cumulative effect causing the development of a future irrecoverable failure. Its architecture design is scalable and integrable in conventional photovoltaic installations. It emphasizes the use of low-cost technology such as the ESP8266 module, ASC712-5A, and FZ0430 sensors and relay modules. The method is based on data acquisition with the ESP8266 module, which is sent over the internet to the computer where a SCADA system (iFIX V6.5) is installed, using the Modbus TCP/IP and OPC communication protocols. Detection thresholds are initially obtained experimentally by applying inductive shading methods on specific solar panels. Full article

(This article belongs to the Special Issue Fault Diagnosis in Transportation and Industry: Sensors, Methods, and Experimental Applications)

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27 pages, 9955 KiB

Open AccessArticle

The Application of Machine Learning ICA-VMD in an Intelligent Diagnosis System in a Low SNR Environment

by Shih-Lin Lin

Sensors 2021, 21(24), 8344; https://doi.org/10.3390/s21248344 - 14 Dec 2021

Cited by 3 | Viewed by 2423

Abstract

This paper proposes a new method called independent component analysis–variational mode decomposition (ICA-VMD), which combines ICA and VMD. The purpose is to study the application of ICA-VMD in low signal-to-noise ratio (SNR) signal processing and data analysis. ICA is a very important method [...] Read more.

This paper proposes a new method called independent component analysis–variational mode decomposition (ICA-VMD), which combines ICA and VMD. The purpose is to study the application of ICA-VMD in low signal-to-noise ratio (SNR) signal processing and data analysis. ICA is a very important method in the field of machine learning. It is an unsupervised learning algorithm that can dig out the independent factors hidden in the observation signal. The VMD method estimates each signal component by solving the frequency domain variational optimization problem, and it is very suitable for mechanical fault diagnosis. The advantage of ICA-VMD is that it requires two sensory cues to distinguish the original source from the unwanted noise. In the three cases studied here, the original source was first contaminated by white Gaussian noise. The three cases in this study are under different SNR conditions. The SNR in the first case is –6.46 dB, the SNR in the second case is –21.3728, and the SNR in the third case is –46.8177. The simulation results show that the ICA-VMD method can effectively recover the original source from the contaminated data. It is hoped that, in the future, there will be new discoveries and advances in science and technology to solve the noise interference problem through this method. Full article

(This article belongs to the Special Issue Fault Diagnosis in Transportation and Industry: Sensors, Methods, and Experimental Applications)

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22 pages, 7231 KiB

Open AccessArticle

Intelligent Fault Diagnosis and Forecast of Time-Varying Bearing Based on Deep Learning VMD-DenseNet

by Shih-Lin Lin

Sensors 2021, 21(22), 7467; https://doi.org/10.3390/s21227467 - 10 Nov 2021

Cited by 16 | Viewed by 2418

Abstract

Rolling bearings are important in rotating machinery and equipment. This research proposes variational mode decomposition (VMD)-DenseNet to diagnose faults in bearings. The research feature involves analyzing the Hilbert spectrum through VMD whereby the vibration signal is converted into an image. Healthy and various [...] Read more.

Rolling bearings are important in rotating machinery and equipment. This research proposes variational mode decomposition (VMD)-DenseNet to diagnose faults in bearings. The research feature involves analyzing the Hilbert spectrum through VMD whereby the vibration signal is converted into an image. Healthy and various faults show different characteristics on the image, thus there is no need to select features. Coupled with the lightweight network, DenseNet, for image classification and prediction. DenseNet is used to build a model of motor fault diagnosis; its structure is simple, and the calculation speed is fast. The method of using DenseNet for image feature learning can perform feature extraction on each image block of the image, providing full play to the advantages of deep learning to obtain accurate results. This research method is verified by the data of the time-varying bearing experimental device at the University of Ottawa. Through the four links of signal acquisition, feature extraction, fault identification, and prediction, a mechanical intelligent fault diagnosis system has established the state of bearing. The experimental results show that the method can accurately identify four common motor faults, with a VMD-DenseNet prediction accuracy rate of 92%. It provides a more effective method for bearing fault diagnosis and has a wide range of application prospects in fault diagnosis engineering. In the future, online and timely diagnosis can be achieved for intelligent fault diagnosis. Full article

(This article belongs to the Special Issue Fault Diagnosis in Transportation and Industry: Sensors, Methods, and Experimental Applications)

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14 pages, 1619 KiB

Open AccessArticle

A Tutorial on Hardware-Implemented Fault Injection and Online Fault Diagnosis for High-Speed Trains

by Xiaoyue Yang, Xinyu Qiao, Chao Cheng, Kai Zhong and Hongtian Chen

Sensors 2021, 21(17), 5957; https://doi.org/10.3390/s21175957 - 05 Sep 2021

Cited by 3 | Viewed by 2276

Abstract

Electrical drive systems are the core of high-speed trains, providing energy transmission from electric power to traction force. Therefore, their safety and reliability topics are always active in practice. Among the current research, fault injection (FI) and fault diagnosis (FD) are representative techniques, [...] Read more.

Electrical drive systems are the core of high-speed trains, providing energy transmission from electric power to traction force. Therefore, their safety and reliability topics are always active in practice. Among the current research, fault injection (FI) and fault diagnosis (FD) are representative techniques, where FI is an important way to recur faults, and FD ensures the recurring faults can be successfully detected as soon as possible. In this paper, a tutorial on a hardware-implemented (HIL) platform that blends FI and FD techniques is given for electrical drive systems of high-speed trains. The main contributions of this work are fourfold: (1) An HIL platform is elaborated for realistic simulation of faults, which provides the test and verification environment for FD tasks. (2) Basics of both the static and dynamic FD methods are reviewed, whose purpose is to guide the engineers and researchers. (3) Multiple performance indexes are defined for comprehensively evaluating the FD approaches from the application viewpoints. (4) It is an integrated platform making the FI and FD work together. Finally, a summary of FD research based on the HIL platform is made. Full article

(This article belongs to the Special Issue Fault Diagnosis in Transportation and Industry: Sensors, Methods, and Experimental Applications)

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15 pages, 4847 KiB

Open AccessArticle

Open-Circuit Fault Detection and Classification of Modular Multilevel Converters in High Voltage Direct Current Systems (MMC-HVDC) with Long Short-Term Memory (LSTM) Method

by Qinghua Wang, Yuexiao Yu, Hosameldin O. A. Ahmed, Mohamed Darwish and Asoke K. Nandi

Sensors 2021, 21(12), 4159; https://doi.org/10.3390/s21124159 - 17 Jun 2021

Cited by 16 | Viewed by 3524

Abstract

Fault detection and classification are two of the challenging tasks in Modular Multilevel Converters in High Voltage Direct Current (MMC-HVDC) systems. To directly classify the raw sensor data without certain feature extraction and classifier design, a long short-term memory (LSTM) neural network is [...] Read more.

Fault detection and classification are two of the challenging tasks in Modular Multilevel Converters in High Voltage Direct Current (MMC-HVDC) systems. To directly classify the raw sensor data without certain feature extraction and classifier design, a long short-term memory (LSTM) neural network is proposed and used for seven states of the MMC-HVDC transmission power system simulated by Power Systems Computer Aided Design/Electromagnetic Transients including DC (PSCAD/EMTDC). It is observed that the LSTM method can detect faults with 100% accuracy and classify different faults as well as provide promising fault classification performance. Compared with a bidirectional LSTM (BiLSTM), the LSTM can get similar classification accuracy, requiring less training time and testing time. Compared with Convolutional Neural Networks (CNN) and AutoEncoder-based deep neural networks (AE-based DNN), the LSTM method can get better classification accuracy around the middle of the testing data proportion, but it needs more training time. Full article

(This article belongs to the Special Issue Fault Diagnosis in Transportation and Industry: Sensors, Methods, and Experimental Applications)

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24 pages, 10685 KiB

Open AccessArticle

Feature Extraction Using Sparse Kernel Non-Negative Matrix Factorization for Rolling Element Bearing Diagnosis

by Lin Liang, Xingyun Ding, Fei Liu, Yuanming Chen and Haobin Wen

Sensors 2021, 21(11), 3680; https://doi.org/10.3390/s21113680 - 25 May 2021

Cited by 6 | Viewed by 2220

Abstract

For early fault detection of a bearing, the localized defect generally brings a complex vibration signal, so it is difficult to detect the periodic transient characteristics from the signal spectrum using conventional bearing fault diagnosis methods. Therefore, many matrix analysis technologies, such as [...] Read more.

For early fault detection of a bearing, the localized defect generally brings a complex vibration signal, so it is difficult to detect the periodic transient characteristics from the signal spectrum using conventional bearing fault diagnosis methods. Therefore, many matrix analysis technologies, such as singular value decomposition (SVD) and reweighted SVD (RSVD), were proposed recently to solve this problem. However, such technologies also face failure in bearing fault detection due to the poor interpretability of the obtained eigenvector. Non-negative Matrix Factorization (NMF), as a part-based representation algorithm, can extract low-rank basis spaces with natural sparsity from the time–frequency representation. It performs excellent interpretability of the factor matrices due to its non-negative constraints. By this virtue, NMF can extract the fault feature by separating the frequency bands of resonance regions from the amplitude spectrogram automatically. In this paper, a new feature extraction method based on sparse kernel NMF (KNMF) was proposed to extract the fault features from the amplitude spectrogram in greater depth. By decomposing the amplitude spectrogram using the kernel-based NMF model with L1 regularization, sparser spectral bases can be obtained. Using KNMF with the linear kernel function, the time–frequency distribution of the vibration signal can be decomposed into a subspace with different frequency bands. Thus, we can extract the fault features, a series of periodic impulses, from the decomposed subspace according to the sparse frequency bands in the spectral bases. As a result, the proposed method shows a very high performance in extracting fault features, which is verified by experimental investigations and benchmarked by the Fast Kurtogram, SVD and NMF-based methods. Full article

(This article belongs to the Special Issue Fault Diagnosis in Transportation and Industry: Sensors, Methods, and Experimental Applications)

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Review

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33 pages, 2356 KiB

Open AccessEditor’s ChoiceReview

Data-Driven Fault Diagnosis for Electric Drives: A Review

by David Gonzalez-Jimenez, Jon del-Olmo, Javier Poza, Fernando Garramiola and Patxi Madina

Sensors 2021, 21(12), 4024; https://doi.org/10.3390/s21124024 - 10 Jun 2021

Cited by 48 | Viewed by 6275

Abstract

The need to manufacture more competitive equipment, together with the emergence of the digital technologies from the so-called Industry 4.0, have changed many paradigms of the industrial sector. Presently, the trend has shifted to massively acquire operational data, which can be processed to [...] Read more.

The need to manufacture more competitive equipment, together with the emergence of the digital technologies from the so-called Industry 4.0, have changed many paradigms of the industrial sector. Presently, the trend has shifted to massively acquire operational data, which can be processed to extract really valuable information with the help of Machine Learning or Deep Learning techniques. As a result, classical Condition Monitoring methodologies, such as model- and signal-based ones are being overcome by data-driven approaches. Therefore, the current paper provides a review of these data-driven active supervision strategies implemented in electric drives for fault detection and diagnosis (FDD). Hence, first, an overview of the main FDD methods is presented. Then, some basic guidelines to implement the Machine Learning workflow on which most data-driven strategies are based, are explained. In addition, finally, the review of scientific articles related to the topic is provided, together with a discussion which tries to identify the main research gaps and opportunities. Full article

(This article belongs to the Special Issue Fault Diagnosis in Transportation and Industry: Sensors, Methods, and Experimental Applications)

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