A novel deep stacking least squares support vector machine for rolling bearing fault diagnosis

Li, Xin; Yu, Yang; Pan, Haiyang; Cheng, Jian; Cheng, Junsheng

doi:10.1016/j.compind.2019.05.005

Cited by 90 publications

(43 citation statements)

References 31 publications

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“…Bearing dataset used in this paper is provided by Case Western Reserve University (CWRU), the dataset is widely discussed and analyzed in many researches [38][39][40][41][42]. The signals applied in this study are collected using accelerometers mounted at the drive end of the motor with 12 kHz of sampling frequency.…”

Section: Bearing Datasetmentioning

confidence: 99%

Vibration Signals Analysis by Explainable Artificial Intelligence (XAI) Approach: Application on Bearing Faults Diagnosis

2020

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This study introduces an explainable artificial intelligence (XAI) approach of convolutional neural networks (CNNs) for classification in vibration signals analysis. First, vibration signals are transformed into images by short-time Fourier transform (STFT). A CNN is applied as classification model, and Gradient class activation mapping (Grad-CAM) is utilized to generate the attention of model. By analyzing the attentions, the explanation of classification models for vibration signals analysis can be carried out. Finally, the verifications of attention are introduced by neural networks, adaptive network-based fuzzy inference system (ANFIS), and decision trees to demonstrate the proposed results. By the proposed methodology, the explanation of model using highlighted attentions is carried out.

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Section: Bearing Datasetmentioning

confidence: 99%

Vibration Signals Analysis by Explainable Artificial Intelligence (XAI) Approach: Application on Bearing Faults Diagnosis

2020

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“…The feature data extracted from the two approaches are substituted into the Back Propagation Neural Network (BPNN) [27], [28] for model training, testing, and classification of identification rate. The classification of accuracy rate and overall efficiency for the Support Vector Machine (SVM) [29], [30] and K Nearest Neighbor (KNN) [31], [32] are then compared for further discussion.…”

Section: Introductionmentioning

confidence: 99%

Inspection on Ball Bearing Malfunction by Chen-Lee Chaos System

Lin

et al. 2020

IEEE Access

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With the trend of Industry 4.0, the global machine tool industry is developing towards smart manufacturing. The ball bearing is a key component of the rotary axis of machine tool, and its functionality is to bear the external load on the axis as well as maintain the center position of the axis. A damaged bearing will result in abnormal vibration and noise, and thus will lead to the damage of the machine and produced workpieces. Therefore, inspection and identification of ball bearing failures is particularly important. This paper discusses the fault signals of ball bearings published by the Society for Machinery Failure Prevention Technology (MFPT) and creates a recognition model for the ball bearing state based on different fault states, and then we adopt two different approaches for feature extraction. The first approach implements Finite Impulse Response Filter (FIR) and Approximate Entropy (ApEn) to extract the signal features. The second approach utilizes the Chen-Lee chaotic system for analysis and takes its chaotic attractor as the feature of the state recognition. The comparison of model recognition accuracy for Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and K Nearest Neighbor (KNN) was conducted after acquiring the features through the two approaches in this paper. The results of the experiments in this paper show that both of the feature extraction approaches enable the state to be recognized easily. The Chen-Lee chaotic system with BPNN not only reaches 100% identification rate and it has the highest overall efficiency; it takes only 0.054 second to complete the feature extraction for 63 sets of data; this study is able to provide timely and precise solution for the failure of key mechanical components. INDEX TERMS Ball bearing, back propagation neural network, support vector machine, fault detection, approximate entropy, Chen-Lee chaotic system.

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“…Huang et al proposed MSVMD model which can automatically decompose the resonance frequency bands of the faulty bearing signal [10]. The DSLS-SVM proposed by Li et al implements bearing fault diagnosis under various working conditions on public data sets and experimental data sets [11]. On the other side, deep learning methods such as Qiao et al validated the effectiveness of AWMSCNN on the wheelset test bench and public datasets [12].…”

Section: Introductionmentioning

confidence: 99%

Lightweight Convolutional Neural Network and Its Application in Rolling Bearing Fault Diagnosis under Variable Working Conditions

Liu

Yao

Yang

et al. 2019

Sensors

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The rolling bearing is an important part of the train’s running gear, and its operating state determines the safety during the running of the train. Therefore, it is important to monitor and diagnose the health status of rolling bearings. A convolutional neural network is widely used in the field of fault diagnosis because it does not require feature extraction. Considering that the size of the network model is large and the requirements for monitoring equipment are high. This study proposes a novel bearing fault diagnosis method based on lightweight network ShuffleNet V2 with batch normalization and L2 regularization. In the experiment, the one-dimensional time-domain signal is converted into a two-dimensional Time-Frequency Graph (TFG) using a short-time Fourier transform, though the principle of graphics to enhance the TFG dataset. The model mainly consists of two units, one for extracting features and one for spatial down-sampling. The building units are repeatedly stacked to construct the whole model. By comparing the proposed method with the origin ShuffleNet V2, machine learning model and state-of-the-art fault diagnosis model, the generalization of the proposed method for bearing fault diagnosis is verified.

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A novel deep stacking least squares support vector machine for rolling bearing fault diagnosis

Cited by 90 publications

References 31 publications

Vibration Signals Analysis by Explainable Artificial Intelligence (XAI) Approach: Application on Bearing Faults Diagnosis

Vibration Signals Analysis by Explainable Artificial Intelligence (XAI) Approach: Application on Bearing Faults Diagnosis

Inspection on Ball Bearing Malfunction by Chen-Lee Chaos System

Lightweight Convolutional Neural Network and Its Application in Rolling Bearing Fault Diagnosis under Variable Working Conditions

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