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Fault Diagnosis and Prognosis of Mechatronic Systems Using Artificial Intelligence...
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Fault Diagnosis and Prognosis of Mechatronic Systems Using Artificial Intelligence and Estimation Theory

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 42843

Special Issue Editors

Prof. Dr. Teresa Orlowska-Kowalska

E-Mail Website
Guest Editor
Department of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw, Poland
Interests: electric drives; sensorless motor drives; state observers and Kalman filters; MRAS estimators, high-performance motion control; AC machines and drive fault detection; fault-tolerant control of AC motor drives; shallow and deep neural network applications in control and diagnostics
Dr. Marcin Wolkiewicz

E-Mail Website
Guest Editor
Department of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw, Poland
Interests: condition monitoring and fault diagnosis of electrical machines and drives; application of artificial intelligence methods in the diagnostics of electrical machines and drives; mathematical modeling of converter-fed drive systems; control methods of induction motors; application of industrial robots; programming in LabVIEW; MATLAB–Simulink environment

Special Issue Information

Dear Colleagues,

Industry 4.0 is the fourth industrial revolution in human history. Industry 4.0 standards assume, among others, an increase in efficiency and reduction of operating costs associated with the intensive automation of industrial processes. It is obvious that such a massive paradigm shift in the industrial sector will mean that various fields of technology must also change and adapt to be used in a new industry concept.

Industrial processes, production systems, transportation systems, and related mechatronic systems are becoming more and more complex and may fail, affecting their reliability, safety, and quality of industrial production. Therefore, monitoring, diagnostics, and prognostics of the condition of machines and devices are one of the critical areas that must be taken into account during all changes related to the implementation of the Industry 4.0 idea.

In particular, obtaining objective diagnostic decisions through the use of artificial intelligence and estimation theory methods and techniques in fault detectors and damage classifiers, as well as in failure prognostic models for mechatronic systems, can facilitate the planning of maintenance and repair inspections of production lines and other devices in industrial plants. It will allow diagnosing and classifying damages of individual elements of these systems in real time at the initial stage of their development, as well as predicting failure development and remaining useful life-time of the failing component/system. Such actions will increase the reliability and efficiency of various industrial processes.

Therefore, the purpose of this Special Issue is to present current trends, advanced methods, and innovative technical solutions (in the field of hardware and software) used in the diagnosis and prognosis of mechatronic systems and their components (electronic, electrical, and mechanical), with particular regard to artificial intelligence methods: shallow and deep neural networks, fuzzy inference, as well as estimation theory.

We encourage you to send articles containing the results of original research, as well as review articles on topics including, among others:

  • General paradigms in fault diagnostics and prognostics of industrial systems and processes
  • Mathematical modeling of faulted mechatronic systems for symptoms generation
  • Initial fault detection of mechatronic systems, single and multiple fault diagnosis
  • Failure prognostic models for industrial systems and their elements, including robots
  • Neural network application for diagnostic/prognostic procedures and unsupervised and supervised training with an emphasis on deep learning networks
  • Fuzzy reasoning and neuro-fuzzy structures for diagnostic/prognostic applications
  • Estimation theory and stochastic methods for diagnostics/prognostics
  • Integrated diagnostic/prognostic methodologies
  • Diagnostic/prognostic system architectures

Prof. Dr. Teresa Orlowska-Kowalska
Dr. Marcin Wolkiewicz
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. Electronics 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 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fault diagnostics and prognostics
  • initial fault detection
  • single and multiple fault diagnosis
  • failure diagnostic and prognostic models
  • diagnostic/prognostic system architectures
  • neural and neuro-fuzzy networks
  • convolutional networks
  • deep learning
  • fuzzy reasoning
  • estimation theory
  • stochastic methods
  • electronic and electrical systems
  • power electronics including power semiconductor devices
  • electrical motors
  • electrical drives
  • robots

Published Papers (15 papers)

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Editorial

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7 pages, 221 KiB  
Editorial
Fault Diagnosis and Prognosis of Mechatronic Systems Using Artificial Intelligence and Estimation Theory
by Teresa Orlowska-Kowalska and Marcin Wolkiewicz
Electronics 2022, 11(21), 3528; https://doi.org/10.3390/electronics11213528 - 29 Oct 2022
Viewed by 1387
Abstract
In the original article [...] Full article
(This article belongs to the Special Issue Fault Diagnosis and Prognosis of Mechatronic Systems Using Artificial Intelligence and Estimation Theory)

Research

Jump to: Editorial

18 pages, 6873 KiB  
Article
Comparison of the Effectiveness of Selected Vibration Signal Analysis Methods in the Rotor Unbalance Detection of PMSM Drive System
by Pawel Ewert, Czeslaw T. Kowalski and Michal Jaworski
Electronics 2022, 11(11), 1748; https://doi.org/10.3390/electronics11111748 - 31 May 2022
Cited by 9 | Viewed by 1665
Abstract
Mechanical unbalance is a phenomenon that concerns rotating elements, including rotors in electrical machines. An unbalanced rotor generates vibration, which is transferred to the machine body. The vibration contributes to reducing drive system reliability and, as a consequence, leads to frequent downtime. Therefore, [...] Read more.
Mechanical unbalance is a phenomenon that concerns rotating elements, including rotors in electrical machines. An unbalanced rotor generates vibration, which is transferred to the machine body. The vibration contributes to reducing drive system reliability and, as a consequence, leads to frequent downtime. Therefore, from an economic point of view, monitoring the unbalance of rotating elements is justified. In this paper, the rotor unbalance of a drive system with a permanent magnet synchronous motor (PMSM) was physically modelled using a specially developed shield, with five test masses fixed at the motor shaft. The analysed diagnostic signal was mechanical vibration. Unbalance was detected using selected signal analysis methods, such as frequency-domain methods (classical spectrum analysis FFT and a higher-order bispectrum method) and two methods applied in technical diagnostics (order analysis and orbit method). The efficiency of unbalance symptom detection using these four methods was compared for the frequency controlled PMSM. The properties of the analysed diagnostic methods were assessed and compared in terms of their usefulness in rotor unbalance diagnosis, and the basic features characterizing the usefulness of these methods were determined depending on the operating conditions of the drive. This work could have a significant impact on the process of designing diagnostic systems for PMSM drives. Full article
(This article belongs to the Special Issue Fault Diagnosis and Prognosis of Mechatronic Systems Using Artificial Intelligence and Estimation Theory)
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17 pages, 2909 KiB  
Article
Permanent Magnet Synchronous Motor Driving Mechanical Transmission Fault Detection and Identification: A Model-Based Diagnosis Approach
by Widagdo Purbowaskito, Po-Yan Wu and Chen-Yang Lan
Electronics 2022, 11(9), 1356; https://doi.org/10.3390/electronics11091356 - 24 Apr 2022
Cited by 9 | Viewed by 2112
Abstract
This paper presents a model-based scheme for permanent magnet synchronous motor (PMSM) driving transmission fault detection and identification (FDI) in a steady-state condition. The proposed framework utilizes a PMSM state-space model and an approximated transmission model to construct the regression models for parameter [...] Read more.
This paper presents a model-based scheme for permanent magnet synchronous motor (PMSM) driving transmission fault detection and identification (FDI) in a steady-state condition. The proposed framework utilizes a PMSM state-space model and an approximated transmission model to construct the regression models for parameter estimation using the Recursive Least-Square (RLS) algorithm. The FDI are accomplished by the residual current spectrum thresholding method to assess the fault characteristic frequency magnitude and also by parameter clustering. Two types of mechanical transmission with three different fault conditions are tested in the experiments. As a preliminary effort in the condition monitoring of PMSM driving transmission, the study results demonstrate a promising approach by considering both residual current spectrum and parameter cluster, which achieved a satisfactory decision making in detecting and identifying the faulty condition. Full article
(This article belongs to the Special Issue Fault Diagnosis and Prognosis of Mechatronic Systems Using Artificial Intelligence and Estimation Theory)
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17 pages, 5698 KiB  
Article
Protection and Control Standards with Auto Diagnosis for the Motor in Low-Voltage Switchgear According to Industry 4.0
by Łukasz Sołtysek, Jerzy Szczepanik, Radosław Dudzik, Maciej Sułowicz and Andreas Schwung
Electronics 2021, 10(23), 2993; https://doi.org/10.3390/electronics10232993 - 01 Dec 2021
Cited by 2 | Viewed by 2228
Abstract
The article is a review of the latest available technologies on the market which are part of “Industry 4.0”, in the field of protection, control, and power supply of equipment. The authors focus on the development of the protection devices (PLC controllers), which [...] Read more.
The article is a review of the latest available technologies on the market which are part of “Industry 4.0”, in the field of protection, control, and power supply of equipment. The authors focus on the development of the protection devices (PLC controllers), which can be used not only for protection purposes but also for the diagnosis and monitoring of the entire system. The key element is the communication structure involving protection, main PLC controller, and DCS, which has an impact on the reliability of the whole system. The authors compare different solutions that allow increasing the reliability of the system (ethernet connection), compared to the classic system (wire connection). Universal protection devices are more flexible devices compared to classic control equipment, but also allow us to make modifications to the structure after commissioning, during normal operation of the system without stopping the technological process. Full article
(This article belongs to the Special Issue Fault Diagnosis and Prognosis of Mechatronic Systems Using Artificial Intelligence and Estimation Theory)
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12 pages, 14737 KiB  
Article
Stray Flux Multi-Sensor for Stator Fault Detection in Synchronous Machines
by Miftah Irhoumah, Remus Pusca, Eric Lefèvre, David Mercier and Raphael Romary
Electronics 2021, 10(18), 2313; https://doi.org/10.3390/electronics10182313 - 20 Sep 2021
Cited by 7 | Viewed by 2143
Abstract
The aim of this paper is to detect a stator inter-turn short circuit in a synchronous machine through the analysis of the external magnetic field measured by external flux sensors. The paper exploits a methodology previously developed, based on the analysis of the [...] Read more.
The aim of this paper is to detect a stator inter-turn short circuit in a synchronous machine through the analysis of the external magnetic field measured by external flux sensors. The paper exploits a methodology previously developed, based on the analysis of the behavior with load variation of sensitive spectral lines issued from two flux sensors positioned at 180° from each other around the machine. Further developments to improve this method were made, in which more than two flux sensors were used to keep a good sensitivity for stator fault detection. The method is based on the Pearson correlation coefficient calculated from sensitive spectral lines at different load operating conditions. Fusion information with belief function is then applied to the correlation coefficients, which enable the detection of an incipient fault in any phase of the machine. The method has the advantage to be fully non-invasive and does not require knowledge of the healthy state. Full article
(This article belongs to the Special Issue Fault Diagnosis and Prognosis of Mechatronic Systems Using Artificial Intelligence and Estimation Theory)
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22 pages, 6973 KiB  
Article
Application of Machine Learning to a Medium Gaussian Support Vector Machine in the Diagnosis of Motor Bearing Faults
by Shih-Lin Lin
Electronics 2021, 10(18), 2266; https://doi.org/10.3390/electronics10182266 - 15 Sep 2021
Cited by 27 | Viewed by 3491
Abstract
In recent years, artificial intelligence technology has been widely used in fault prediction and health management (PHM). The machine learning algorithm is widely used in the condition monitoring of rotating machines, and normal and fault data can be obtained through the data acquisition [...] Read more.
In recent years, artificial intelligence technology has been widely used in fault prediction and health management (PHM). The machine learning algorithm is widely used in the condition monitoring of rotating machines, and normal and fault data can be obtained through the data acquisition and monitoring system. After analyzing the data and establishing a model, the system can automatically learn the features from the input data to predict the failure of the maintenance and diagnosis equipment, which is important for motor maintenance. This research proposes a medium Gaussian support vector machine (SVM) method for the application of machine learning and constructs a feature space by extracting the characteristics of the vibration signal collected on the spot based on experience. Different methods were used to cluster and classify features to classify motor health. The influence of different Gaussian kernel functions, such as fine, medium, and coarse, on the performance of the SVM algorithm was analyzed. The experimental data verify the performance of various models through the data set released by the Case Western Reserve University Motor Bearing Data Center. As the motor often has noise interference in the actual application environment, a simulated Gaussian white noise was added to the original vibration data in order to verify the performance of the research method in a noisy environment. The results summarize the classification results of related motor data sets derived recently from the use of motor fault detection and diagnosis using different machine learning algorithms. The results show that the medium Gaussian SVM method improves the reliability and accuracy of motor bearing fault estimation, detection, and identification under variable crack-size and load conditions. This paper also provides a detailed discussion of the predictive analytical capabilities of machine learning algorithms, which can be used as a reference for the future motor predictive maintenance analysis of electric vehicles. Full article
(This article belongs to the Special Issue Fault Diagnosis and Prognosis of Mechatronic Systems Using Artificial Intelligence and Estimation Theory)
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21 pages, 13081 KiB  
Article
On-line Detection and Classification of PMSM Stator Winding Faults Based on Stator Current Symmetrical Components Analysis and the KNN Algorithm
by Przemyslaw Pietrzak and Marcin Wolkiewicz
Electronics 2021, 10(15), 1786; https://doi.org/10.3390/electronics10151786 - 26 Jul 2021
Cited by 27 | Viewed by 3789
Abstract
The significant advantages of permanent magnet synchronous motors, such as very good dynamic properties, high efficiency and power density, have led to their frequent use in many drive systems today. However, like other types of electric motors, they are exposed to various types [...] Read more.
The significant advantages of permanent magnet synchronous motors, such as very good dynamic properties, high efficiency and power density, have led to their frequent use in many drive systems today. However, like other types of electric motors, they are exposed to various types of faults, including stator winding faults. Stator winding faults are mainly inter-turn short circuits and are among the most common faults in electric motors. In this paper, the possibility of using the spectral analysis of symmetrical current components to extract fault symptoms and the machine-learning-based K-Nearest Neighbors (KNN) algorithm for the detection and classification of the PMSM stator winding fault is presented. The impact of the key parameters of this classifier on the effectiveness of stator winding fault detection and classification is presented and discussed in detail, which has not been researched in the literature so far. The proposed solution was verified experimentally using a 2.5 kW PMSM, the construction of which was specially prepared for carrying out controlled inter-turn short circuits. Full article
(This article belongs to the Special Issue Fault Diagnosis and Prognosis of Mechatronic Systems Using Artificial Intelligence and Estimation Theory)
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22 pages, 21204 KiB  
Article
Gradual Wear Diagnosis of Outer-Race Rolling Bearing Faults through Artificial Intelligence Methods and Stray Flux Signals
by Israel Zamudio-Ramirez, Roque A. Osornio-Rios, Jose A. Antonino-Daviu, Jonathan Cureño-Osornio and Juan-Jose Saucedo-Dorantes
Electronics 2021, 10(12), 1486; https://doi.org/10.3390/electronics10121486 - 20 Jun 2021
Cited by 11 | Viewed by 2208
Abstract
Electric motors have been widely used as fundamental elements for driving kinematic chains on mechatronic systems, which are very important components for the proper operation of several industrial applications. Although electric motors are very robust and efficient machines, they are prone to suffer [...] Read more.
Electric motors have been widely used as fundamental elements for driving kinematic chains on mechatronic systems, which are very important components for the proper operation of several industrial applications. Although electric motors are very robust and efficient machines, they are prone to suffer from different faults. One of the most frequent causes of failure is due to a degradation on the bearings. This fault has commonly been diagnosed at advanced stages by means of vibration and current signals. Since low-amplitude fault-related signals are typically obtained, the diagnosis of faults at incipient stages turns out to be a challenging task. In this context, it is desired to develop non-invasive techniques able to diagnose bearing faults at early stages, enabling to achieve adequate maintenance actions. This paper presents a non-invasive gradual wear diagnosis method for bearing outer-race faults. The proposal relies on the application of a linear discriminant analysis (LDA) to statistical and Katz’s fractal dimension features obtained from stray flux signals, and then an automatic classification is performed by means of a feed-forward neural network (FFNN). The results obtained demonstrates the effectiveness of the proposed method, which is validated on a kinematic chain (composed by a 0.746 KW induction motor, a belt and pulleys transmission system and an alternator as a load) under several operation conditions: healthy condition, 1 mm, 2 mm, 3 mm, 4 mm, and 5 mm hole diameter on the bearing outer race, and 60 Hz, 50 Hz, 15 Hz and 5 Hz power supply frequencies Full article
(This article belongs to the Special Issue Fault Diagnosis and Prognosis of Mechatronic Systems Using Artificial Intelligence and Estimation Theory)
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19 pages, 3285 KiB  
Article
An Adaptive Prediction Model for the Remaining Life of an Li-Ion Battery Based on the Fusion of the Two-Phase Wiener Process and an Extreme Learning Machine
by Xiaowu Chen, Zhen Liu, Jingyuan Wang, Chenglin Yang, Bing Long and Xiuyun Zhou
Electronics 2021, 10(5), 540; https://doi.org/10.3390/electronics10050540 - 25 Feb 2021
Cited by 18 | Viewed by 1894
Abstract
Lithium-ion batteries (LiBs) are the most important part of electric vehicle (EV) systems. Because there are two different degradation rates during LiB degradation, there are many two-phase models for LiBs. However, most of these methods do not consider the randomness of the changing [...] Read more.
Lithium-ion batteries (LiBs) are the most important part of electric vehicle (EV) systems. Because there are two different degradation rates during LiB degradation, there are many two-phase models for LiBs. However, most of these methods do not consider the randomness of the changing point in the two-phase model and cannot update the change time in real time. Therefore, this paper proposes a method based on the combination of the two-phase Wiener model and an extreme learning machine (ELM). The two-phase Wiener model is used to derive the mathematical expression of the remaining useful life (RUL), and the ELM is implemented to adaptively detect the changing point. Based on the Poisson distribution, the distribution of the changing time is derived as a gamma distribution. To evaluate the theoretical results and practicality of the proposed method, we perform both numerical and practical simulations. The results of the simulations show that due to the precise and adaptive detection of changing points, the proposed method produces a more accurate RUL prediction than existing methods. The error of our method for detecting the changing point is about 4% and the mean prediction error of RUL in the second phase is improved from 4.39 cycles to 1.61 cycles. Full article
(This article belongs to the Special Issue Fault Diagnosis and Prognosis of Mechatronic Systems Using Artificial Intelligence and Estimation Theory)
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20 pages, 3758 KiB  
Article
Low-Frequency Signal Sampling Method Implemented in a PLC Controller Dedicated to Applications in the Monitoring of Selected Electrical Devices
by Marcin Jaraczewski, Ryszard Mielnik, Tomasz Gębarowski and Maciej Sułowicz
Electronics 2021, 10(4), 442; https://doi.org/10.3390/electronics10040442 - 10 Feb 2021
Cited by 2 | Viewed by 2490
Abstract
High requirements for power systems, and hence for electrical devices used in industrial processes, make it necessary to ensure adequate power quality. The main parameters of the power system include the rms-values of the current, voltage, and active and reactive power consumed by [...] Read more.
High requirements for power systems, and hence for electrical devices used in industrial processes, make it necessary to ensure adequate power quality. The main parameters of the power system include the rms-values of the current, voltage, and active and reactive power consumed by the loads. In previous articles, the authors investigated the use of low-frequency sampling to measure these parameters of the power system, showing that the method can be easily implemented in simple microcontrollers and PLCs. This article discusses the methods of measuring electrical quantities by devices with low computational efficiency and low sampling frequency up to 1 kHz. It is not obvious that the signal of 50–500 Hz can be processed using the sampling frequency of fs = 47.619 Hz because it defies the Nyquist–Shannon sampling theorem. This theorem states that a reconstruction of a sampled signal is only guaranteed possible for a bandlimit fmax < fs, where fmax is the maximum frequency of a sampled signal. Therefore, theoretically, neither 50 nor 500 Hz can be identified by such a low-frequency sampling. Although, it turns out that if we have a longer period of a stable multi-harmonic signal, which is band-limited (from the bottom and top), it allows us to map this band to the lower frequencies, thus it is possible to use the lower sampling ratio and still get enough precise information of its harmonics and rms value. The use of aliasing for measurement purposes is not often used because it is considered a harmful phenomenon. In our work, it has been used for measurement purposes with good results. The main advantage of this new method is that it achieves a balance between PLC processing power (which is moderate or low) and accuracy in calculating the most important electrical signal indicators such as power, RMS value and sinusoidal-signal distortion factor (e.g., THD). It can be achieved despite an aliasing effect that causes different frequencies to become indistinguishable. The result of the research is a proposal of error reduction in the low-frequency measurement method implemented on compact PLCs. Laboratory tests carried out on a Mitsubishi FX5 compact PLC controller confirmed the correctness of the proposed method of reducing the measurement error. Full article
(This article belongs to the Special Issue Fault Diagnosis and Prognosis of Mechatronic Systems Using Artificial Intelligence and Estimation Theory)
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15 pages, 1179 KiB  
Article
A Genetic Algorithm Optimized RNN-LSTM Model for Remaining Useful Life Prediction of Turbofan Engine
by Kwok Tai Chui, Brij B. Gupta and Pandian Vasant
Electronics 2021, 10(3), 285; https://doi.org/10.3390/electronics10030285 - 25 Jan 2021
Cited by 55 | Viewed by 5015
Abstract
Understanding the remaining useful life (RUL) of equipment is crucial for optimal predictive maintenance (PdM). This addresses the issues of equipment downtime and unnecessary maintenance checks in run-to-failure maintenance and preventive maintenance. Both feature extraction and prediction algorithm have played crucial roles on [...] Read more.
Understanding the remaining useful life (RUL) of equipment is crucial for optimal predictive maintenance (PdM). This addresses the issues of equipment downtime and unnecessary maintenance checks in run-to-failure maintenance and preventive maintenance. Both feature extraction and prediction algorithm have played crucial roles on the performance of RUL prediction models. A benchmark dataset, namely Turbofan Engine Degradation Simulation Dataset, was selected for performance analysis and evaluation. The proposal of the combination of complete ensemble empirical mode decomposition and wavelet packet transform for feature extraction could reduce the average root-mean-square error (RMSE) by 5.14–27.15% compared with six approaches. When it comes to the prediction algorithm, the results of the RUL prediction model could be that the equipment needs to be repaired or replaced within a shorter or a longer period of time. Incorporating this characteristic could enhance the performance of the RUL prediction model. In this paper, we have proposed the RUL prediction algorithm in combination with recurrent neural network (RNN) and long short-term memory (LSTM). The former takes the advantages of short-term prediction whereas the latter manages better in long-term prediction. The weights to combine RNN and LSTM were designed by non-dominated sorting genetic algorithm II (NSGA-II). It achieved average RMSE of 17.2. It improved the RMSE by 6.07–14.72% compared with baseline models, stand-alone RNN, and stand-alone LSTM. Compared with existing works, the RMSE improvement by proposed work is 12.95–39.32%. Full article
(This article belongs to the Special Issue Fault Diagnosis and Prognosis of Mechatronic Systems Using Artificial Intelligence and Estimation Theory)
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19 pages, 4884 KiB  
Article
An Improved Fault Diagnosis Using 1D-Convolutional Neural Network Model
by Chih-Cheng Chen, Zhen Liu, Guangsong Yang, Chia-Chun Wu and Qiubo Ye
Electronics 2021, 10(1), 59; https://doi.org/10.3390/electronics10010059 - 31 Dec 2020
Cited by 62 | Viewed by 4989
Abstract
The diagnosis of a rolling bearing for monitoring its status is critical in maintaining industrial equipment while using rolling bearings. The traditional method of diagnosing faults of the rolling bearing has low identification accuracy, which needs artificial feature extraction in order to enhance [...] Read more.
The diagnosis of a rolling bearing for monitoring its status is critical in maintaining industrial equipment while using rolling bearings. The traditional method of diagnosing faults of the rolling bearing has low identification accuracy, which needs artificial feature extraction in order to enhance the accuracy. The one-dimensional convolution neural network (1D-CNN) method can not only diagnose bearing faults accurately, but also overcome shortcomings of the traditional methods. Different from machine learning and other deep learning models, the 1D-CNN method does not need pre-processing one-dimensional data of rolling bearing’s vibration. In this paper, the 1D-CNN network architecture is proposed in order to effectively improve the accuracy of the diagnosis of rolling bearing, and the number of convolution kernels decreases with the reduction of the convolution kernel size. The method obtains high accuracy and improves the generalizing ability by introducing the dropout operation. The experimental results show 99.2% of the average accuracy under a single load and 98.83% under different loads. Full article
(This article belongs to the Special Issue Fault Diagnosis and Prognosis of Mechatronic Systems Using Artificial Intelligence and Estimation Theory)
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20 pages, 5839 KiB  
Article
Open-Circuit Fault Diagnosis of Three-Phase PWM Rectifier Using Beetle Antennae Search Algorithm Optimized Deep Belief Network
by Bolun Du, Yigang He and Yaru Zhang
Electronics 2020, 9(10), 1570; https://doi.org/10.3390/electronics9101570 - 25 Sep 2020
Cited by 9 | Viewed by 2655
Abstract
Effective open-circuit fault diagnosis for a two-level three-phase pulse-width modulating (PWM) rectifier can reduce the failure rate and prevent unscheduled shutdown. Nevertheless, traditional signal-based feature extraction methods show poor distinguishability for insufficient fault features. Shallow learning diagnosis models are prone to fall into [...] Read more.
Effective open-circuit fault diagnosis for a two-level three-phase pulse-width modulating (PWM) rectifier can reduce the failure rate and prevent unscheduled shutdown. Nevertheless, traditional signal-based feature extraction methods show poor distinguishability for insufficient fault features. Shallow learning diagnosis models are prone to fall into local extremum, slow convergence speed, and overfitting. In this paper, a novel fault diagnosis strategy based on modified ensemble empirical mode decomposition (MEEMD) and the beetle antennae search (BAS) algorithm optimized deep belief network (DBN) is proposed to cope with these problems. Initially, MEEMD is applied to extract useful fault features from each intrinsic mode function (IMF) component. Meanwhile, to remove features with redundancy and interference, fault features are selected by calculating the importance of each feature based on the extremely randomized trees (ERT) algorithm, and the dimension of fault feature vectors is reduced by principal component analysis. Additionally, the DBN stacked with two layers of a restricted Boltzmann machine (RBM) is selected as the classifier, and the BAS algorithm is used as the optimizer to determine the optimal number of units in the hidden layers of the DBN. The proposed method combined with feature extraction, feature selection, optimization, and fault classification algorithms significantly improves the diagnosis accuracy. Full article
(This article belongs to the Special Issue Fault Diagnosis and Prognosis of Mechatronic Systems Using Artificial Intelligence and Estimation Theory)
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18 pages, 5635 KiB  
Article
Low-Cost Monitoring and Diagnosis System for Rolling Bearing Faults of the Induction Motor Based on Neural Network Approach
by Pawel Ewert, Czeslaw T. Kowalski and Teresa Orlowska-Kowalska
Electronics 2020, 9(9), 1334; https://doi.org/10.3390/electronics9091334 - 19 Aug 2020
Cited by 20 | Viewed by 2765
Abstract
In this article, a low-cost computer system for the monitoring and diagnosis of the condition of the induction motor (IM) rolling bearings is demonstrated and tested. The system allows the on-line monitoring of the IM bearings and subsequent fault diagnostics based on analysis [...] Read more.
In this article, a low-cost computer system for the monitoring and diagnosis of the condition of the induction motor (IM) rolling bearings is demonstrated and tested. The system allows the on-line monitoring of the IM bearings and subsequent fault diagnostics based on analysis of the vibration measurement data. The evaluation of the bearing condition is made by a suitably trained neural network (NN), on the basis of the spectral and envelope analysis of the mechanical vibrations. The system was developed in the LabVIEW environment in such a way that it could be run on any PC. The functionality of the application has been tested on a real object. The study was conducted on a low-power IM equipped with a set of specially prepared bearings so as to model the different damages. In the designed computer system, a selected NN for detecting and identifying the defects of individual components of the induction motor’s bearings was implemented. The training data for NNs were obtained from real experiments. The magnitudes of the characteristic harmonics, obtained from the spectral analysis and the envelope analysis, were used for training and testing the developed neural detectors based on Matlab toolbox. The experimental test results of the developed monitoring and diagnosis system are presented in the article. The evaluation of the system’s ability to detect and identify the defects of individual components of bearings, such as the rolling element, and outer race and inner race, was made. It was also shown that the developed NN-based detectors are insensitive to other motor faults, such as short-circuits of the stator winding, broken rotor bars or motor misalignment. Full article
(This article belongs to the Special Issue Fault Diagnosis and Prognosis of Mechatronic Systems Using Artificial Intelligence and Estimation Theory)
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20 pages, 7016 KiB  
Article
Efficiency of Cascaded Neural Networks in Detecting Initial Damage to Induction Motor Electric Windings
by Maciej Skowron and Teresa Orłowska-Kowalska
Electronics 2020, 9(8), 1314; https://doi.org/10.3390/electronics9081314 - 15 Aug 2020
Cited by 9 | Viewed by 2396
Abstract
This article presents the efficiency of using cascaded neural structures in the process of detecting damage to electrical circuits in a squirrel cage induction motor (IM) supplied from a frequency converter. The authors present the idea of a sequential connection of classic neural [...] Read more.
This article presents the efficiency of using cascaded neural structures in the process of detecting damage to electrical circuits in a squirrel cage induction motor (IM) supplied from a frequency converter. The authors present the idea of a sequential connection of classic neural structures to increase the efficiency of damage classification and detection presented by individual neural structures, especially in the initial phase of single or multiple electrical failures. The easily measurable axial flux signal is used as a source of diagnostic information. The developed cascaded neural networks are implemented in the measurement and diagnostic software made in the LabVIEW environment. The results of the experimental research on a 1.5 kW IM supplied by an industrial frequency converter confirm the high efficiency of the use of the developed cascaded neural structures in the detection of incipient stator and rotor winding faults, namely inter-turn stator winding short circuits and broken rotor bars, as well as mixed failures in the entire range of changes of the load torque and supply voltage frequency. Full article
(This article belongs to the Special Issue Fault Diagnosis and Prognosis of Mechatronic Systems Using Artificial Intelligence and Estimation Theory)
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