Mechanical Looseness Diagnosis Using Wavelet Packet Energy Analysis and Supervised Feature Relevance Evaluation

Mechanical looseness is one of the most common faults affecting rotating machinery and may lead to severe degradation or catastrophic failure if not detected at early stages. This paper presents a vibration-based methodology for looseness diagnosis in rotating shafts using Wavelet Packet Transform (WPT) energy analysis and supervised feature relevance evaluation. Experiments were conducted on a rotating machinery test bench under healthy and defective conditions with an artificially introduced diametral clearance of 0.5 mm. Vibration signals acquired at different rotational speeds were processed through WPT to extract packet energy features from the time-frequency domain. Neighborhood Component Analysis (NCA) was employed to evaluate the discriminative capability of the extracted features and to investigate the distribution of looseness-related information across the WPT packets. The results revealed that mechanical looseness generates characteristic energy redistribution patterns concentrated within specific frequency regions and that rotational speed significantly influences the localization and separability of the fault-related vibration components. The influence of different mother wavelets was additionally analyzed, showing clear operating-condition-dependent differences in the discriminative relevance of the extracted WPT energy features.