Condition-Adaptive Permutation Entropy: A Novel Dynamic Complexity-Based Health Indicator for Bearing Health Monitoring

Bearing health monitoring (BHM) is vital in preventing unforeseen machinery shutdowns caused by frequent bearing failures. Within the BHM process, constructing health indicators takes center stage, serving the dual purpose of detecting incipient faults and assessing the monotonous degradation trend for predicting residual useful life. In terms of detecting incipient faults, permutation entropy (PE) serves as a promising tool due to its simplicity and rapid computation. However, when it comes to assessing irreversible degradation, PE often exhibits notable fluctuations and nonmonotonicity even after signal denoising processes. This issue arises from PE's vulnerability to impulsive noise and its invariance to monotonic signal transformations. To tackle this challenge, the article introduces a novel approach termed condition-adaptive permutation entropy (CAPE) for BHM. CAPE begins with a condition-based signal processing method to mitigate the influence of impulsive noise, followed by an amplitude-aware algorithm to break PE's invariance to monotonic signal processing. Moreover, CAPE adaptively selects fault-relevant permutation patterns to enhance its monotonicity. The effectiveness, superiority, and applicability of CAPE are rigorously demonstrated using simulation data and two experimental datasets.