Enhanced Bearing Fault Analysis under Inconstant Loads Conditions by Cosine Weighted K-Nearest Neighbours Model

Bearing faults often lead to machinery failures, underscoring the importance of analyzing bearing vibrations to avert undesirable consequences. Leveraging Artificial Intelligence (AI) in this context benefits from the strides in intelligent data processing and computing capabilities. Traditionally, signal processing and feature engineering play pivotal roles in achieving accurate classifications. However, classification accuracy can decline notably during variable loading scenarios due to the diverse vibration patterns exhibited under different loads. This study assesses an AI model's performance under variable loading conditions using raw vibration signals, without recourse to signal processing or feature engineering. Introducing an enhanced AI model, known as Cosine Weighted K-Nearest Neighbours (CWKNN), resulted in a slightly improved 85.2–88.7% under stable loading conditions and 64.3–72.6% under variable loading conditions.