![]() ![]() As the key component of CBM, effective fault diagnosis can reduce the risk of unplanned shutdown. Therefore, it’s essential to develop the CBM system and detect faults accurately. While operating in complex conditions, key components of these machines will deteriorate over time. Failure of major equipment, such as aero-engine and helicopter, might lead to huge losses of life and property. Ĭondition-based maintenance (CBM) of machinery is important in the modern industry. ![]() Experiments results demonstrate a significant diagnostic performance improvement over other explainable or denoising deep learning networks. It's worth noting that SPINN may be readily applied to different deep learning networks by simply adding filterbank and feature modules in front. Taking advantage of wavelet transform, FWConv extracts multiscale features while learning wavelet scales and selecting important wavelet bases automatically DHT dynamically eliminates noise-related components via point-wise hard thresholding inspired by index-based filtering, ISF optimizes and selects optimal filters for diagnostic feature extraction. As one of the practical implementations for SPINN, a denoising fault-aware wavelet network (DFAWNet) is developed, which consists of fused wavelet convolution (FWConv), dynamic hard thresholding (DHT), index-based soft filtering (ISF), and a classifier. To address these limitations, inspired by the Filterbank-Feature-Decision methodology, we propose a new Signal Processing Informed Neural Network (SPINN) framework by embedding SP knowledge into the DL model. In addition, the extraction of weak fault features from signals with heavy noise is imperative in industrial applications. However, the lack of explainability makes DL-based fault diagnosis methods difficult to be trusted and understood by industrial users. Deep learning (DL) is progressively popular as a viable alternative to traditional signal processing (SP) based methods for fault diagnosis. ![]()
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