ArticleSensors (Basel, Switzerland)2022
Integrated Gradient-Based Continuous Wavelet Transform for Bearing Fault Diagnosis.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Fault Diagnosis of Rolling Bearings Based on HFMD and Dual-Branch Parallel Network Under Acoustic Signals.Sensors (Basel, Switzerland) · 2025Article
- Research on rolling bearing compound fault diagnosis based on AMOMCKD and convolutional neural network.Scientific reports · 2025Article
- Unsupervised Learning for Machinery Adaptive Fault Detection Using Wide-Deep Convolutional Autoencoder with Kernelized Attention Mechanism.Sensors (Basel, Switzerland) · 2024Article
- A One-Dimensional Depthwise Separable Convolutional Neural Network for Bearing Fault Diagnosis Implemented on FPGA.Sensors (Basel, Switzerland) · 2024Article
- Failure Mode Classification for Rolling Element Bearings Using Time-Domain Transformer-Based Encoder.Sensors (Basel, Switzerland) · 2024Article
- Lightweight Ghost Enhanced Feature Attention Network: An Efficient Intelligent Fault Diagnosis Method under Various Working Conditions.Sensors (Basel, Switzerland) · 2024Article
- Fault Diagnosis of Rotating Machinery Using an Optimal Blind Deconvolution Method and Hybrid Invertible Neural Network.Sensors (Basel, Switzerland) · 2024Article
- Time-Frequency Multi-Domain 1D Convolutional Neural Network with Channel-Spatial Attention for Noise-Robust Bearing Fault Diagnosis.Sensors (Basel, Switzerland) · 2023Article
- A Novel Hybrid Technique Combining Improved Cepstrum Pre-Whitening and High-Pass Filtering for Effective Bearing Fault Diagnosis Using Vibration Data.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
Bearing fault diagnosis is important to ensure safe operation and reduce loss for most rotating machinery. In recent years, deep learning (DL) has been widely used for bearing fault diagnosis and has achieved excellent results. Continuous wavelet transform (CWT), which can convert original sensor data to time-frequency images, is often used to preprocess vibration data for the DL model. However, in time-frequency images, some frequency components may be important, and some may be unimportant for DL models for fault diagnosis. So, how to choose a frequency range of important frequency components is needed for CWT. In this paper, an Integrated Gradient-based continuous wavelet transform (IG-CWT) method is proposed to address this issue. Through IG-CWT, the important frequency components and the component frequency range can be detected and used for data preprocessing. To verify our method, experiments are conducted on four famous bearing datasets using 3 DL models, separately, and compared with CWT, and the results are compared with the original CWT. The comparisons show that the proposed IG-CWT can achieve higher fault diagnosis accuracy.
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