ArticleMicromachines2022
Explainable AI for Bearing Fault Prognosis Using Deep Learning Techniques.
Article in Micromachines, 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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Who cites it
9 citing papers in PubMed.
- Remaining Useful Life Prediction for Bearings Across Domains via a Subdomain Adaptation Network Driven by Spectral Clustering.Sensors (Basel, Switzerland) · 2025Article
- Remaining Useful Life Prediction Across Conditions Based on a Health Indicator-Weighted Subdomain Alignment Network.Sensors (Basel, Switzerland) · 2025Article
- Fault Detection of Cyber-Physical Systems Using a Transfer Learning Method Based on Pre-Trained Transformers.Sensors (Basel, Switzerland) · 2025Article
- The Prediction of the Remaining Useful Life of Rotating Machinery Based on an Adaptive Maximum Second-Order Cyclostationarity Blind Deconvolution and a Convolutional LSTM Autoencoder.Sensors (Basel, Switzerland) · 2024Article
- Aircraft Engine Fault Diagnosis Model Based on 1DCNN-BiLSTM with CBAM.Sensors (Basel, Switzerland) · 2024Article
- A Two-Stage Screw Detection Framework for Automatic Disassembly Using a Reflection Feature Regression Model.Micromachines · 2023Article
- DCFF-MTAD: A Multivariate Time-Series Anomaly Detection Model Based on Dual-Channel Feature Fusion.Sensors (Basel, Switzerland) · 2023Article
- Application and Prospect of Artificial Intelligence Methods in Signal Integrity Prediction and Optimization of Microsystems.Micromachines · 2023Review
- Explainable AI Algorithms for Vibration Data-Based Fault Detection: Use Case-Adadpted Methods and Critical Evaluation.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting bearing failures is a vital component of machine health monitoring since bearings are essential parts of rotary machines, particularly large motor machines. In addition, determining the degree of bearing degeneration will aid firms in scheduling maintenance. Maintenance engineers may be gradually supplanted by an automated detection technique in identifying motor issues as improvements in the extraction of useful information from vibration signals are made. State-of-the-art deep learning approaches, in particular, have made a considerable contribution to automatic defect identification. Under variable shaft speed, this research presents a novel approach for identifying bearing defects and their amount of degradation. In the proposed approach, vibration signals are represented by spectrograms, and deep learning methods are applied via pre-processing with the short-time Fourier transform (STFT). A convolutional neural network (CNN), VGG16, is then used to extract features and classify health status. After this, RUL prediction is carried out with the use of regression. Explainable AI using LIME was used to identify the part of the image used by the CNN algorithm to give the output. Our proposed method was able to achieve very high accuracy and robustness for bearing faults, according to numerous experiments.
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Registered trials
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