ArticlePloS one2017
An improved wrapper-based feature selection method for machinery fault diagnosis.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Reassessing negative 24 h pH impedance tests for hidden gastroesophageal reflux disease using multi feature anomaly detection.NPJ digital medicine · 2026Article
- Hybrid Diagnostic Framework for Interpretable Bearing Fault Classification Using CNN and Dual-Stage Feature Selection.Sensors (Basel, Switzerland) · 2025Article
- Multi-Filter Clustering Fusion for Feature Selection in Rotating Machinery Fault Classification.Sensors (Basel, Switzerland) · 2022Article
- Texture, Morphology, and Statistical Analysis to Differentiate Primary Brain Tumors on Two-Dimensional Magnetic Resonance Imaging Scans Using Artificial Intelligence Techniques.Healthcare informatics research · 2022Article
- Towards Interpretable Deep Learning: A Feature Selection Framework for Prognostics and Health Management Using Deep Neural Networks.Sensors (Basel, Switzerland) · 2021Article
- A fault diagnosis method based on Auxiliary Classifier Generative Adversarial Network for rolling bearing.PloS one · 2021Article
- Sparse support vector machines with LArtificial intelligence in medicine · 2019Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A major issue of machinery fault diagnosis using vibration signals is that it is over-reliant on personnel knowledge and experience in interpreting the signal. Thus, machine learning has been adapted for machinery fault diagnosis. The quantity and quality of the input features, however, influence the fault classification performance. Feature selection plays a vital role in selecting the most representative feature subset for the machine learning algorithm. In contrast, the trade-off relationship between capability when selecting the best feature subset and computational effort is inevitable in the wrapper-based feature selection (WFS) method. This paper proposes an improved WFS technique before integration with a support vector machine (SVM) model classifier as a complete fault diagnosis system for a rolling element bearing case study. The bearing vibration dataset made available by the Case Western Reserve University Bearing Data Centre was executed using the proposed WFS and its performance has been analysed and discussed. The results reveal that the proposed WFS secures the best feature subset with a lower computational effort by eliminating the redundancy of re-evaluation. The proposed WFS has therefore been found to be capable and efficient to carry out feature selection tasks.
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