Bearing Health Evaluation Model using Segmentive Technique and Cosine KNN in Different Loading Situations

Abstract

Bearing faults are a common cause of machinery failure, and bearing vibration analysis is critical in preventing any unacceptable consequences of such failures. Advancements in smart data and computing make Artificial Intelligence (AI) preferable for bearing vibration analysis. Typically, signal processing and feature engineering are essential for achieving satisfactory classification accuracy. Additionally, a drop in classification accuracy is commonly observed during different loading situations due to the vastly varying vibration characteristics under different loads. This paper evaluates an AI model in variable loading situations using raw vibration signals, devoid of signal processing and feature engineering. The proposed AI model, Segmentive Cosine K-Nearest Neighbours (SCosKNN), demonstrated a higher overall classification accuracy of 90.6–94.3% in same loading situations, and 72.1–84.2% in different loading situations. An improvement of around 9% in same loadings and 10–14% in different loadings were observed compared to a model without Segmentive Technique