Fault Diagnosis of Rolling Bearing Based on Shift Invariant Sparse Feature and Optimized Support Vector Machine

Yuan, Hongping; Wu, Nailong; Chen, Xinyuan; Wang, Yueying

doi:10.3390/machines9050098

Cited by 25 publications

(17 citation statements)

References 38 publications

(43 reference statements)

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“…The usage of the above-mentioned methods can minimize human participation in fault diagnosis and help in automating this process. Therefore, the usage of selected MLbased classifiers, shallow and deep neural networks, has been verified to detect various types of electric motor faults [10,[35][36][37][38][39][40][41][42][43][44][45]. Taking into account an electric motor fault other than mechanical failure, there are still very few scientific papers in which the usage of simple machine learning algorithms to detect PMSM stator winding faults is presented, especially taking into account the analysis of the key parameter selection of fault classifiers on their effectiveness.…”

Section: Introductionmentioning

confidence: 99%

On-line Detection and Classification of PMSM Stator Winding Faults Based on Stator Current Symmetrical Components Analysis and the KNN Algorithm

Pietrzak

Wolkiewicz

2021

Electronics

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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.

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Section: Introductionmentioning

confidence: 99%

On-line Detection and Classification of PMSM Stator Winding Faults Based on Stator Current Symmetrical Components Analysis and the KNN Algorithm

Pietrzak

Wolkiewicz

2021

Electronics

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“…Based on the machine learning techniques, fault diagnosis is regarded as a classification problem. In the traditional machine learning methods, representative features are first extracted from the raw signals, based on which pattern of recognition technology is applied to classify the health conditions of the equipment, for instance, support vector machines (SVM) [14], clustering algorithms [15] and artificial neural networks (ANN) [16,17] and so on. Shi et al [18] applied linear discriminant analysis and gray wolf optimizer to improve the SVM algorithm and enhance the performance of fault classification.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection of Bearing by Resnet Classifier with Model-Based Data Augmentation

Qian

Pan

et al. 2022

Machines

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It is always an important and challenging issue to achieve an effective fault diagnosis in rotating machinery in industries. In recent years, deep learning proved to be a high-accuracy and reliable method for data-based fault detection. However, the training of deep learning algorithms requires a large number of real data, which is generally expensive and time-consuming. To cope with this, we proposed a Resnet classifier with model-based data augmentation, which is applied for bearing fault detection. To this end, a dynamic model was first established to describe the bearing system by adjusting model parameters, such as speed, load, fault size, and the different fault types. Large amounts of data under various operation conditions can then be generated. The training dataset was constructed by the simulated data, which was then applied to train the Resnet classifier. In addition, in order to reduce the gap between the simulation data and the real data, the envelop signals were used instead of the original signals in the training process. Finally, the effectiveness of the proposed method was demonstrated by the real bearing experimental data. It is remarkable that the application of the proposed method can be further extended to other mechatronic systems with a deterministic dynamic model.

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“…Generally, there are two types of machine-learning-based fault diagnosis techniques: traditional machine learning techniques and deep learning techniques. The traditional machine learning algorithms commonly applied in intelligent fault diagnosis of rotating machinery mainly contain support vector machines (SVM) [7,8] and artificial neural networks (ANN) [9,10]. However, the traditional intelligent diagnosis methods have inherent limitations [11]: (1) Variable working conditions and composite faults make it difficult to extract signal features effectively; (2) the extracted signal features must be selected with the advice of experienced engineering experts; (3) shallow machine learning algorithms are not able to adequately learn complex nonlinear relationships between the input data.…”

Section: Introductionmentioning

confidence: 99%

An Improved Convolutional-Neural-Network-Based Fault Diagnosis Method for the Rotor–Journal Bearings System

Luo

Lin

Peng³

et al. 2022

Machines

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More layers in a convolution neural network (CNN) means more computational burden and longer training time, resulting in poor performance of pattern recognition. In this work, a simplified global information fusion convolution neural network (SGIF-CNN) is proposed to improve computational efficiency and diagnostic accuracy. In the improved CNN architecture, the feature maps of all the convolutional and pooling layers are globally convoluted into a corresponding one-dimensional feature sequence, and then all the feature sequences are concatenated into the fully connected layer. On this basis, this paper further proposes a novel fault diagnosis method for a rotor–journal bearing system based on SGIF-CNN. Firstly, the time-frequency distributions of samples are obtained using the Adaptive Optimal-Kernel Time–Frequency Representation algorithm (AOK-TFR). Secondly, the time–frequency diagrams of the training samples are utilized to train the SGIF-CNN model using a shallow information fusion method, and the trained SGIF-CNN model can be tested using the time–frequency diagrams of the testing samples. Finally, the trained SGIF-CNN model is transplanted to the equipment’s online monitoring system to monitor the equipment’s operating conditions in real time. The proposed method is verified using the data from a rotor test rig and an ultra-scale air separator, and the analysis results show that the proposed SGIF-CNN improves the computing efficiency compared to the traditional CNN while ensuring the accuracy of the fault diagnosis.

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Fault Diagnosis of Rolling Bearing Based on Shift Invariant Sparse Feature and Optimized Support Vector Machine

Cited by 25 publications

References 38 publications

On-line Detection and Classification of PMSM Stator Winding Faults Based on Stator Current Symmetrical Components Analysis and the KNN Algorithm

On-line Detection and Classification of PMSM Stator Winding Faults Based on Stator Current Symmetrical Components Analysis and the KNN Algorithm

Fault Detection of Bearing by Resnet Classifier with Model-Based Data Augmentation

An Improved Convolutional-Neural-Network-Based Fault Diagnosis Method for the Rotor–Journal Bearings System

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