Topic Correlation Analysis for Bearing Fault Diagnosis Under Variable Operating Conditions

Rolling bearings are present ubiquitously in industrial fields; timely fault diagnosis is of crucial significance in avoiding serious catastrophe. The extraction of ideal fault feature is a challenging task in vibration-based bearing fault detection. In this article, a novel method called class-information–incorporated kernel entropy component analysis is proposed for bearing fault diagnosis. The method is developed based on the Hebbian learning theory of neural network and the kernel entropy component analysis which attempts to compress the most Renyi quadratic entropy of input dataset after dimension reduction and presents a good performance for nonlinear feature extraction. Class-information–incorporated kernel entropy component analysis can take advantage of the label information of training samples to guide dimensional reduction and still follow the same simple mathematical formulation as kernel entropy component analysis. The high-dimensional feature dataset including time-domain, frequency-domain, and time–frequency domain characteristic parameters is first derived from the vibration signals. Then, the intrinsic geometric features are extracted by class-information–incorporated kernel entropy component analysis, and a classification strategy based on fusion information is applied to recognize different operating conditions of bearings. The experimental results demonstrated the feasibility and effectiveness of the proposed method.

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Rolling Element Bearing Fault Diagnosis Based on Deep Belief Network and Principal Component Analysis

Annual Conference of the PHM Society ◽

10.36001/phmconf.2019.v11i1.882 ◽

2019 ◽

Vol 11 (1) ◽

Cited By ~ 3

Author(s):

Guangxing Niu ◽

Bin Zhang ◽

Paul Ziehl ◽

Frank Ferrese ◽

Michael Golda

Keyword(s):

Principal Component Analysis ◽

Feature Extraction ◽

Fault Diagnosis ◽

Principal Component ◽

Operating Conditions ◽

Belief Network ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Rolling Element ◽

Ae Signal

Rolling element bearings are critical components in industrial rotating machines. Faults and failures of bearings can cause degradation of machine performance or even a catastrophe. Bearing fault diagnosis is therefore essential and significant to safe and reliable operation of systems. For bearing condition monitoring, acoustic emission (AE) signals attract more and more attention due to its advantages on sensitivity over the extensively used vibration signal. In bearing fault diagnosis and prognosis, feature extraction is a critical and tough work, which always involves complex signal processing and computation. Moreover, features greatly rely on the characteristics, operating conditions, and type of data. With consideration of changes in operating conditions and increase of data complexity, traditional diagnosis approaches are insufficient in feature extraction and fault diagnosis. To address this problem, this paper proposes a Deep Belief Network (DBN) and Principal Component Analysis (PCA) based fault diagnosis approach using AE signal. This proposed approach combines the advantages of deep learning and statistical analysis, DBN automatically extracts features from AE signal, PCA is applied to dimensionality reduction. Different bearing fault modes are identified by least squares support vector machine (LS-SVM) using the extracted features. An experimental case is conducted with a tapered roller bearing to verify the proposed approach. Experimental results demonstrate that the proposed approach has excellent feature extraction ability and high fault classification accuracy.

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Bearing fault diagnosis under different operating conditions based on cross domain feature projection and domain adaptation

2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) ◽

10.1109/i2mtc.2019.8826993 ◽

2019 ◽

Cited By ~ 1

Author(s):

Shuzhi Dong ◽

Guangrui Wen ◽

Zhifen Zhang

Keyword(s):

Fault Diagnosis ◽

Domain Adaptation ◽

Operating Conditions ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Cross Domain ◽

Feature Projection

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Fault Diagnosis for High-Speed Train Axle-Box Bearing Using Simplified Shallow Information Fusion Convolutional Neural Network

Sensors ◽

10.3390/s20174930 ◽

2020 ◽

Vol 20 (17) ◽

pp. 4930 ◽

Cited By ~ 1

Author(s):

Honglin Luo ◽

Lin Bo ◽

Chang Peng ◽

Dongming Hou

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Information Fusion ◽

High Speed ◽

High Reliability ◽

Operating Conditions ◽

High Speed Train ◽

Bearing Fault ◽

Bearing Fault Diagnosis

Axle-box bearings are one of the most critical mechanical components of the high-speed train. Vibration signals collected from axle-box bearings are usually nonlinear and nonstationary, caused by the complicated operating conditions. Due to the high reliability and real-time requirement of axle-box bearing fault diagnosis for high-speed trains, the accuracy and efficiency of the bearing fault diagnosis method based on deep learning needs to be enhanced. To identify the axle-box bearing fault accurately and quickly, a novel approach is proposed in this paper using a simplified shallow information fusion-convolutional neural network (SSIF-CNN). Firstly, the time domain and frequency domain features were extracted from the training samples and testing samples before been inputted into the SSIF-CNN model. Secondly, the feature maps obtained from each hidden layer were transformed into a corresponding feature sequence by the global convolution operation. Finally, those feature sequences obtained from different layers were concatenated into one-dimensional as the fully connected layer to achieve the fault identification task. The experimental results showed that the SSIF-CNN effectively compressed the training time and improved the fault diagnosis accuracy compared with a general CNN.

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Rolling Bearing Fault Diagnosis Algorithm Based on Overlapping Group Sparse Model-Deep Complex Convolutional Neural Network

10.21203/rs.3.rs-888210/v1 ◽

2021 ◽

Author(s):

FENGPING AN ◽

Jianrong Wang

Keyword(s):

Neural Network ◽

Deep Learning ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Rolling Bearing ◽

Operating Conditions ◽

Sparse Model ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Multi Scale

Abstract As the key component of a mechanical system, rolling bearings will cause paralysis of the entire mechanical system once they fail. In recent years, considering the high generalization ability and nonlinear modeling ability of deep learning, a rolling bearing fault diagnosis method based on deep learning has been formed, and good results have been achieved. However, because this kind of method is still in the initial development stage, its main problems are as follows. First, it is difficult to extract the composite fault signal feature of rolling bearing. Second, the existing deep learning rolling bearing fault diagnosis methods cannot well consider the problem of multi-scale information of rolling bearing signals. Therefore, this paper first proposes the overlapping group sparse model. It constructs weight coefficients by analyzing the salient features of the signal. It uses convex optimization techniques to solve the sparse optimization model, and applies the method to the feature extraction of rolling bearing composite faults. For the problem of multi-scale feature information extraction of rolling bearing composite fault signals, this paper proposes a new deep complex convolutional neural network model. This model fully considers the multi-scale information of rolling bearing signals. The complex information in this model not only contains rich representation ability, but also can extract more scale information. Finally, the classifier of this model is used to identify rolling bearing faults. Based on this, this paper proposes a new rolling bearing fault diagnosis algorithm based on overlapping group sparse model-deep complex convolutional neural network. The experimental results show that the method proposed in this paper can not only effectively identify rolling bearing faults under constant operating conditions, but also accurately identify rolling bearing fault signals under changing operating conditions. Additionally, the classification accuracy of the method proposed in this paper is greatly improved compared with traditional machine learning methods. It also has certain advantages over other deep learning methods.

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