scholarly journals Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 93155-93178 ◽  
Author(s):  
Dhiraj Neupane ◽  
Jongwon Seok
Keyword(s):  
Deep Learning ◽  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Learning Approaches ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Detection And Diagnosis ◽  
Western Reserve

scholarly journals Semiconductor Bearing Fault Recognition

2021 ◽  
Vol 11 (1) ◽  
pp. 21-26
Author(s):  
Nikhita Mishra ◽  
◽  
Ipshitta Chaturvedi ◽  
Janhvi Mehta ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Fault Detection ◽  
Original Data ◽  
Fault Detection And Diagnosis ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Machine Failure ◽  
Fault Recognition ◽  
Learning Techniques ◽  
Detection And Diagnosis

Semiconductor manufacturing is consid-ered to be one of the most technologically complicated manufacturing processes. Bearing, being a critical part of the rotating machinery used in the process, plays an essential role as it supports the mechanical rotating body and decreases the friction coefficient. However, extensive use makes this element a target of health degradation, which indirectly causes machine failure. A defective bearing causes approximately 50% of failures in electrical machines. Hence, there arises a dire need for effective fault detection and diagnosis methods to recog-nise fault patterns and help take preventive measures. This paper carries out a comprehensive comparative study of the pre-existing machine learning and deep learning techniques used for diagnosing bearing faults and further devises a novel framework for bearing fault diagnosis based on the results. Unlike the conventional Fault Detection Classifiers (FDC) that operate in the original data space, this algorithm explores the scope for feature extraction and transferability empowered by the deep learning models used.

scholarly journals Vibration-Based Bearing Fault Detection and Diagnosis via Image Recognition Technique Under Constant and Variable Speed Conditions

2018 ◽  
Vol 8 (8) ◽  
pp. 1392 ◽  
Author(s):  
Moussa Hamadache ◽  
Dongik Lee ◽  
Emiliano Mucchi ◽  
Giorgio Dalpiaz
Keyword(s):  
Fault Detection ◽  
Image Recognition ◽  
Fault Detection And Diagnosis ◽  
Variable Speed ◽  
Vibration Signals ◽  
Bearing Faults ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Detection And Diagnosis ◽  
Recognition Technique

This paper addresses the application of an image recognition technique for the detection and diagnosis of ball bearing faults in rotating electrical machines (REMs). The conventional bearing fault detection and diagnosis (BFDD) methods rely on extracting different features from either waveforms or spectra of vibration signals to detect and diagnose bearing faults. In this paper, a novel vibration-based BFDD via a probability plot (ProbPlot) image recognition technique under constant and variable speed conditions is proposed. The proposed technique is based on the absolute value principal component analysis (AVPCA), namely, ProbPlot via image recognition using the AVPCA (ProbPlot via IR-AVPCA) technique. A comparison of the features (images) obtained: (1) directly in the time domain from the original raw data of the vibration signals; (2) by capturing the Fast Fourier Transformation (FFT) of the vibration signals; or (3) by generating the probability plot (ProbPlot) of the vibration signals as proposed in this paper, is considered. A set of realistic bearing faults (i.e., outer-race fault, inner-race fault, and balls fault) are experimentally considered to evaluate the performance and effectiveness of the proposed ProbPlot via the IR-AVPCA method.

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