ArticleSensors (Basel, Switzerland)2021
An Explainable AI-Based Fault Diagnosis Model for Bearings.
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 11 papers.
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Who cites it
11 citing papers in PubMed, 68 citations in OpenAlex.
- Multi-Source Diagnosis of Bearing Faults Using Interpretable Boosted Trees.Sensors (Basel, Switzerland) · 2026Article
- AI-Driven Resilient Fault Diagnosis of Bearings in Rotating Machinery.Sensors (Basel, Switzerland) · 2025Article
- Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process.Sensors (Basel, Switzerland) · 2024Article
- Bearing fault detection by using graph autoencoder and ensemble learning.Scientific reports · 2024Article
- Explainable AI Algorithms for Vibration Data-Based Fault Detection: Use Case-Adadpted Methods and Critical Evaluation.Sensors (Basel, Switzerland) · 2022Article
- An Improved MobileNet Network with Wavelet Energy and Global Average Pooling for Rotating Machinery Fault Diagnosis.Sensors (Basel, Switzerland) · 2022Article
- Rolling Bearing Fault Diagnosis Based on Markov Transition Field and Residual Network.Sensors (Basel, Switzerland) · 2022Article
- A Hybrid Matching Network for Fault Diagnosis under Different Working Conditions with Limited Data.Computational intelligence and neuroscience · 2022Article
- Multistage Centrifugal Pump Fault Diagnosis Using Informative Ratio Principal Component Analysis.Sensors (Basel, Switzerland) · 2021Article
- Bearing Fault Diagnosis Using Multidomain Fusion-Based Vibration Imaging and Multitask Learning.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
In this paper, an explainable AI-based fault diagnosis model for bearings is proposed with five stages, i.e., (1) a data preprocessing method based on the Stockwell Transformation Coefficient (STC) is proposed to analyze the vibration signals for variable speed and load conditions, (2) a statistical feature extraction method is introduced to capture the significance from the invariant pattern of the analyzed data by STC, (3) an explainable feature selection process is proposed by introducing a wrapper-based feature selector-Boruta, (4) a feature filtration method is considered on the top of the feature selector to avoid the multicollinearity problem, and finally, (5) an additive Shapley explanation followed by k-NN is proposed to diagnose and to explain the individual decision of the k-NN classifier for debugging the performance of the diagnosis model. Thus, the idea of explainability is introduced for the first time in the field of bearing fault diagnosis in two steps: (a) incorporating explainability to the feature selection process, and (b) interpretation of the classifier performance with respect to the selected features. The effectiveness of the proposed model is demonstrated on two different datasets obtained from separate bearing testbeds. Lastly, an assessment of several state-of-the-art fault diagnosis algorithms in rotating machinery is included.
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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.