A physics-informed deep learning approach for bearing fault detection

2021 ◽  
Vol 103 ◽  
pp. 104295
Author(s):  
Sheng Shen ◽  
Hao Lu ◽  
Mohammadkazem Sadoughi ◽  
Chao Hu ◽  
Venkat Nemani ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fault Detection ◽  
Learning Approach ◽  
Bearing Fault ◽  
Bearing Fault Detection

scholarly journals Classifying Multivariate Signals in Rolling Bearing Fault Detection Using Adaptive Wide-Kernel CNNs

2021 ◽  
Vol 11 (23) ◽  
pp. 11429
Author(s):  
Jurgen van den Hoogen ◽  
Stefan Bloemheuvel ◽  
Martin Atzmueller
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Fault Detection ◽  
Condition Monitoring ◽  
Rolling Bearing ◽  
Data Driven ◽  
Automatic Data ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Multivariate Signals

With the developments in improved computation power and the vast amount of (automatic) data collection, industry has become more data-driven. These data-driven approaches for monitoring processes and machinery require different modeling methods focusing on automated learning and deployment. In this context, deep learning provides possibilities for industrial diagnostics to achieve improved performance and efficiency. These deep learning applications can be used to automatically extract features during training, eliminating time-consuming feature engineering and prior understanding of sophisticated (signal) processing techniques. This paper extends on previous work, introducing one-dimensional (1D) CNN architectures that utilize an adaptive wide-kernel layer to improve classification of multivariate signals, e.g., time series classification in fault detection and condition monitoring context. We used multiple prominent benchmark datasets for rolling bearing fault detection to determine the performance of the proposed wide-kernel CNN architectures in different settings. For example, distinctive experimental conditions were tested with deviating amounts of training data. We shed light on the performance of these models compared to traditional machine learning applications and explain different approaches to handle multivariate signals with deep learning. Our proposed models show promising results for classifying different fault conditions of rolling bearing elements and their respective machine condition, while using a fairly straightforward 1D CNN architecture with minimal data preprocessing. Thus, using a 1D CNN with an adaptive wide-kernel layer seems well-suited for fault detection and condition monitoring. In addition, this paper clearly indicates the high potential performance of deep learning compared to traditional machine learning, particularly in complex multivariate and multi-class classification tasks.

scholarly journals Bearing Fault Detection Using Scalogram and Switchable Normalization-Based CNN (SN-CNN)

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Dhiraj Neupane ◽  
Yunsu Kim ◽  
Jongwon Seok
Keyword(s):  
Fault Detection ◽  
Bearing Fault ◽  
Bearing Fault Detection

Memory Residual Regression Autoencoder for Bearing Fault Detection

2021 ◽  
pp. 1-1
Author(s):  
Xin Huang ◽  
Guangrui Wen ◽  
Shuzhi Dong ◽  
Haoxuan Zhou ◽  
Zihao Lei ◽  
...  
Keyword(s):  
Fault Detection ◽  
Bearing Fault ◽  
Bearing Fault Detection
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