ArticleSensors (Basel, Switzerland)2021
Bearing Fault Diagnosis via Improved One-Dimensional Multi-Scale Dilated CNN.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Frequency-Aware Lightweight Convolutional Neural Network for Edge-Enabled Intelligent Bearing Fault Diagnosis and Health Management.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
- Rolling Bearing Fault Diagnosis Model Based on Multi-Scale Depthwise Separable Convolutional Neural Network Integrated with Spatial Attention Mechanism.Sensors (Basel, Switzerland) · 2025Article
- A multi-dilated convolution network for speech emotion recognition.Scientific reports · 2025Article
- Vibration-Based Anomaly Detection for Induction Motors Using Machine Learning.Sensors (Basel, Switzerland) · 2025Article
- Article
- Research on Fault Diagnosis of Rolling Bearing Based on Gramian Angular Field and Lightweight Model.Sensors (Basel, Switzerland) · 2024Article
- A Novel Deep Convolutional Neural Network Combining Global Feature Extraction and Detailed Feature Extraction for Bearing Compound Fault Diagnosis.Sensors (Basel, Switzerland) · 2023Article
- ThelR547v1-An Asymmetric Dilated Convolutional Neural Network for Real-time Semantic Segmentation of Horticultural Crops.Sensors (Basel, Switzerland) · 2022Article
- Integrated Gradient-Based Continuous Wavelet Transform for Bearing Fault Diagnosis.Sensors (Basel, Switzerland) · 2022Article
- A Novel Image-Based Diagnosis Method Using Improved DCGAN for Rotating Machinery.Sensors (Basel, Switzerland) · 2022Article
- A New Statistical Features Based Approach for Bearing Fault Diagnosis Using Vibration Signals.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Bearings are the key and important components of rotating machinery. Effective bearing fault diagnosis can ensure operation safety and reduce maintenance costs. This paper aims to develop a novel bearing fault diagnosis method via an improved multi-scale convolutional neural network (IMSCNN). In traditional convolutional neural network (CNN), a fixed convolutional kernel is often employed in the convolutional layer. Thus, informative features can not be fully extracted for fault diagnosis. In the proposed IMSCNN, a 1D dimensional convolutional layer is used to mitigate the effect of noise contained in vibration signals. Then, four dilated convolutional kernels with different dilation rates are integrated to extract multi-scale features through the inception structure. Experimental results from the popular CWRU and PU datasets show the superiority of the proposed method by comparison with other related methods.
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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.