Abstract
Rolling element bearings (REBs) are vital parts of rotating machinery across various industries. For preventing breakdowns and damages during operation, it is crucial to establish appropriate techniques for condition monitoring and fault diagnostics of these bearings. The development of machine learning (ML) brings a new way of diagnosing the fault of rolling element bearings. In the current work, ML models, namely, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), are used to classify the faults associated with different ball bearing elements. Using open-source Case Western Reserve University (CWRU) bearing data, machine learning classifiers are trained with extracted time-domain and frequency-domain features. The results show that frequency-domain features are more convincing for the training of ML models, and the KNN classifier has a high level of accuracy compared to SVM.
Highlights
Fault detection and diagnosis of rotating machinery play an essential role in maintenance planning, human safety, and cost reduction in modern industrial systems
Bearing defect detection based on Hidden Markov Modeling (HMM) using vibration signal is proposed in reference [10]
The current research, which employs machine learning techniques, demonstrates that the specific selection of fault features plays a significant role in training machine learning models for bearing defect classification
Summary
Fault detection and diagnosis of rotating machinery play an essential role in maintenance planning, human safety, and cost reduction in modern industrial systems. Many research findings support the machine learning approach in machinery fault diagnosis as the ML methods are more competitive than signal-based methods [12,13,14,15]. FEATURE-BASED PERFORMANCE OF SVM AND KNN CLASSIFIERS FOR DIAGNOSIS OF ROLLING ELEMENT BEARING FAULTS. Various time and frequency-domain features are extracted from the data and are used to distinguish different bearing conditions using the SVM and KNN classifiers. A data point is viewed as a p-dimensional vector (a list of p numbers) in support-vector machines, and one wants to know if such points can be separated with a (p − 1)-dimensional hyperplane. The value of K should be selected to decrease the number of errors when making predictions from each run
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