{'fraction_non_alphanumeric': 0.016570605187319884, 'fraction_numerical': 0.0, 'mean_word_length': 5.371559633027523, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Rolling bearing remaining useful life prediction via weight tracking relevance vector machine

The application scenarios of rotating machinery are becoming increasingly complicated due to the rapid development of the manufacturing industry. The remaining useful life (RUL) prediction of rolling bearings has gradually been considered in many industry fields for ensuring the safety and reliability of whole systems. As an effective way to analyze data, the relevance vector machine (RVM) approach holds great potential for RUL prediction. However, the redundant features of rolling bearing vibration signals can easily lead to overfitting and low accuracy of the RVM model for RUL prediction. To conquer these issues, inspired by the idea of the boosting algorithm and ensemble learning, this paper proposes a new RVM model, called the weight-tracking relevance vector machine (WTRVM). Within the proposed WTRVM model, an adaptive sequential optimal feature selection method is designed to avoid overfitting by selecting the best features. The error between the prediction value of the RVM model and the true value is counted for the RVM model training and weight tracking. The most accurate model can be obtained when all selected features have been trained. Finally, the proposed WTRVM algorithm is experimentally demonstrated to be effective for the RUL prediction of rolling bearings.