Bearing Fault Classification using Acoustic Features and Artificial Neural Network

Sharma, Anju; Patra, G.; Naidu, Vps

doi:10.1109/i4c57141.2022.10057769

Cited by 1 publication

(1 citation statement)

References 13 publications

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“…Various methods, including vibration, acoustics, temperature, and lubricant analysis, are employed to analyze bearing faults [4][5][6][7]. The prevalent approach to monitoring the health of rotating machines relies on analyzing vibration signals [8].…”

Section: Introductionmentioning

confidence: 99%

SHapley Additive exPlanations (SHAP) for Efficient Feature Selection in Rolling Bearing Fault Diagnosis

Santos,

Guedes,

Sanchez-Gendriz

2024

MAKE

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This study introduces an efficient methodology for addressing fault detection, classification, and severity estimation in rolling element bearings. The methodology is structured into three sequential phases, each dedicated to generating distinct machine-learning-based models for the tasks of fault detection, classification, and severity estimation. To enhance the effectiveness of fault diagnosis, information acquired in one phase is leveraged in the subsequent phase. Additionally, in the pursuit of attaining models that are both compact and efficient, an explainable artificial intelligence (XAI) technique is incorporated to meticulously select optimal features for the machine learning (ML) models. The chosen ML technique for the tasks of fault detection, classification, and severity estimation is the support vector machine (SVM). To validate the approach, the widely recognized Case Western Reserve University benchmark is utilized. The results obtained emphasize the efficiency and efficacy of the proposal. Remarkably, even with a highly limited number of features, evaluation metrics consistently indicate an accuracy of over 90% in the majority of cases when employing this approach.

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