ArticleSensors (Basel, Switzerland)2025
Fault Diagnosis Method of Rolling Bearing Based on 1D Multi-Channel Improved Convolutional Neural Network in Noisy Environment.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Advances and Challenges in Non-Contact Acoustic Signal-Based Bearing Fault Diagnosis: A Review.Sensors (Basel, Switzerland) · 2026Review
- ACSE-RNformer: Amplitude-Calibrated Sequence Embedding and Response-Normalized Transformer for Vibration-Based Rotating Machinery Fault Diagnosis.Sensors (Basel, Switzerland) · 2026Article
- Multi-Scale Temporal Coordinate Attention Network with Peak-Aware Mechanism for Rolling Bearing Fault Diagnosis Under Low Signal-to-Noise Ratio Conditions.Sensors (Basel, Switzerland) · 2026Article
- A Gradient-Penalized Conditional TimeGAN Combined with Multi-Scale Importance-Aware Network for Fault Diagnosis Under Imbalanced Data.Sensors (Basel, Switzerland) · 2025Article
- Bearing Fault Diagnosis Based on Time-Frequency Dual Domains and Feature Fusion of ResNet-CACNN-BiGRU-SDPA.Sensors (Basel, Switzerland) · 2025Article
- Optimization of a Coupled Neuron Model Based on Deep Reinforcement Learning and Application of the Model in Bearing Fault Diagnosis.Sensors (Basel, Switzerland) · 2025Article
- A Novel Parallel Multi-Scale Attention Residual Network for the Fault Diagnosis of a Train Transmission System.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
The vibration signal of mechanical equipment in operating environments is the key to describing fault characteristics, but due to thez influence of equipment density and environmental interference, the accuracy of fault diagnosis is often affected by noise. In this paper, a fault diagnosis method based on a 1D Multi-Channel Improved Convolutional Neural Network (1DMCICNN) is proposed. By introducing BiLSTM, an attention mechanism and a local sparse structure of a two-channel Convolutional Neural Network, the feature information of the noisy timing signal is fully extracted at different scales while reducing the computational parameters. The model is verified through experiments under different signal-to-noise ratios and loads. The results show that the accuracy of 1DMCICNN is 98.67%, 99.71%, 99.04%, and 99.71% on different load and speed datasets. Meanwhile, compared with the unoptimized two-channel Convolutional Neural Network, the training parameters are reduced by 55.58%.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.