Bearing Fault Classification Based on Convolutional Neural Network in Noise Environment

Jiang, Qinyu; Chang, Faliang; Sheng, Bowen

doi:10.1109/access.2019.2919126

Cited by 69 publications

(33 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Inspired by the effectiveness of extracting time-frequency-domain features from acoustic, microseismic and vibrational signals, Mel frequency cepstral coefficients (MFCCs), the 1st-order differential (Delta) and the 2nd-order differential (Delta-Delta) coefficients have shown effective capabilities for many diagnostics problems [13][14][15][16]. For fault diagnosis of bearings and fans, Zhang et al [13] linearly fused zero-crossing rate (ZCR), MFCCs and Wavelet Packet Decomposition Energy features from acoustic signals from each component and classified each fault/operating condition using the SVM classifier.…”

Section: Motivation Related Work and Major Contributionsmentioning

confidence: 99%

A Cost-Efficient MFCC-Based Fault Detection and Isolation Technology for Electromagnetic Pumps

Akpudo

Hur

2021

Electronics

View full text Add to dashboard Cite

Fluid pumps serve critical purposes in hydraulic systems so their failure affects productivity, profitability, safety, etc. The need for proper condition monitoring and health assessment of these pumps cannot be overemphasized and this has resulted in extensive research studies on standard techniques for ensuring optimum fault detection and isolation (FDI) results for these pumps. Interestingly, mechanical vibrational signals reflect operating conditions and by exploring the robust time–frequency-domain feature extraction techniques, the underlying nonlinear characteristics can be captured for reliable fault diagnosis/condition assessment. This study is based on the use of vibrational signals for fault isolation of electromagnetic pumps. From the vibrational signals, Mel frequency cepstral coefficients (MFCCs), the first-order and the second-order differentials were extracted and the salient features selected by a rank-based recursive feature elimination (RFE) of uncorrelated features. The proposed framework was tested and validated on five VSC63A5 electromagnetic pumps at various fault conditions and isolated/classified using the Gaussian kernel SVM (SVM-RBF-RFE). Results show that the proposed feature selection approach is computationally cheaper and significantly improves diagnostics performance. In addition, the proposed framework yields a comparatively better diagnostics results on electromagnetic pumps in comparison with other diagnostics methods, hence a more reliable diagnostics tool for electromagnetic pumps.

show abstract

Section: Motivation Related Work and Major Contributionsmentioning

confidence: 99%

A Cost-Efficient MFCC-Based Fault Detection and Isolation Technology for Electromagnetic Pumps

Akpudo

Hur

2021

Electronics

View full text Add to dashboard Cite

show abstract

“…For example, an image can be regarded as a two-dimensional pixel grid, and the one-dimensional vibration data can be regarded as a one-dimensional grid. In recent years, the CNN has been well applied in many fields, such as image classification [ 29 ], fault diagnosis [ 30 ], and so on. Convolutional neural networks use convolution operations instead of general matrix multiplication operations.…”

Section: Preliminary Knowledge Of Some Conceptsmentioning

confidence: 99%

One-Dimensional Multi-Scale Domain Adaptive Network for Bearing-Fault Diagnosis under Varying Working Conditions

Wang

Zhao

et al. 2020

Sensors

View full text Add to dashboard Cite

Data-driven bearing-fault diagnosis methods have become a research hotspot recently. These methods have to meet two premises: (1) the distributions of the data to be tested and the training data are the same; (2) there are a large number of high-quality labeled data. However, machines usually work under different working conditions in practice, which challenges these prerequisites due to the fact that the data distributions under different working conditions are different. In this paper, the one-dimensional Multi-Scale Domain Adaptive Network (1D-MSDAN) is proposed to address this issue. The 1D-MSDAN is a kind of deep transfer model, which uses both feature adaptation and classifier adaptation to guide the multi-scale convolutional neural network to perform bearing-fault diagnosis under varying working conditions. Feature adaptation is performed by both multi-scale feature adaptation and multi-level feature adaptation, which helps in finding domain-invariant features by minimizing the distribution discrepancy between different working conditions by using the Multi-kernel Maximum Mean Discrepancy (MK-MMD). Furthermore, classifier adaptation is performed by entropy minimization in the target domain to bridge the source classifier and target classifier to further eliminate domain discrepancy. The Case Western Reserve University (CWRU) bearing database is used to validate the proposed 1D-MSDAN. The experimental results show that the diagnostic accuracy for the 12 transfer tasks performed by 1D-MSDAN was superior to that of the mainstream transfer learning models for bearing-fault diagnosis under variable working conditions. In addition, the transfer learning performance of 1D-MSDAN for multi-target domain adaptation and real industrial scenarios was also verified.

show abstract

“…Among them, convolutional neural network (CNN), deep belief network (DBN), and deep autoencoders (AE) have been viewed as prevailing and striking in fault diagnosis of rotating machinery [12,13,14,15,16]. CNN stands out as a result of its special strengths in automatic learning ability, which can implicitly learn from training data and achieve feature extraction via the contribution of convolution kernels [17]. In the light of bearing fault diagnosis, different methods based on CNN were developed, with new training strategies and aiming at imbalanced distribution problems of machinery data [18,19].…”

Section: Introductionmentioning

confidence: 99%

Data Preprocessing Techniques in Convolutional Neural Network Based on Fault Diagnosis Towards Rotating Machinery

2020

View full text Add to dashboard Cite

Rotating machinery plays a critical role in many significant fields. However, the unpredictable machinery faults may lead to the severe damage and losses. Hence, it is of great value to explore the precise approaches for fault diagnosis. With the development of the intelligent fault diagnosis methods based on deep learning, convolutional neural network (CNN) has aroused the attention of researchers in machinery fault diagnosis. In the light of the reduction of difficulty in feature learning and the improvement of final diagnosis accuracy, data preprocessing is necessary and crucial in CNN-based fault diagnosis methods. This review focuses on CNN-based fault diagnosis approaches in rotating machinery. Firstly, data preprocessing methods are overviewed. Then, we emphatically analyze and discuss several main techniques applied in CNN-based intelligent diagnosis, principally including the fast Fourier transform, wavelet transform, data augmentation, S-transform, and cyclic spectral analysis. Finally, the potential challenges and research objects are prospected on data preprocessing in intelligent fault diagnosis of rotary machinery. INDEX TERMS Data preprocessing, convolutional neural network, intelligent fault diagnosis, rotating machinery.

show abstract

Bearing Fault Classification Based on Convolutional Neural Network in Noise Environment

Cited by 69 publications

References 34 publications

A Cost-Efficient MFCC-Based Fault Detection and Isolation Technology for Electromagnetic Pumps

A Cost-Efficient MFCC-Based Fault Detection and Isolation Technology for Electromagnetic Pumps

One-Dimensional Multi-Scale Domain Adaptive Network for Bearing-Fault Diagnosis under Varying Working Conditions

Data Preprocessing Techniques in Convolutional Neural Network Based on Fault Diagnosis Towards Rotating Machinery

Contact Info

Product

Resources

About