组合核RVM与EEMD信息熵的机械设备可靠度评估与预测

The ability of mechanical equipment to work normally in the expected service environment is usually reflected by operational reliability. Traditional methods for evaluating operational reliability of mechanical equipment are often based on a large number of historical samples. However, traditional methods are difficult to accurately evaluate the actual operation status of mechanical equipment without historical samples and its future performance degradation trend effectively. The normalized EEMD information entropy and relevance vector machine with combined kernel function were constructed to evaluate and predict the operation reliability of mechanical equipment. Firstly, the vibration signals of mechanical equipment during the operation process were collected, and the vibration signals were decomposed by the ensemble empirical mode decomposition method (EEMD). Several intrinsic mode functions (IMF) components were obtained, and their relative energy and normalized EEMD information entropy were calculated to construct characteristic indicators to characterize the operation status of mechanical equipment. Secondly, the operational samples of mechanical equipment were used to train the relevance vector machine, and particle swarm optimization algorithm was used to optimize the weights and kernel function parameters in the RVM. Finally, the constructed operation state characteristic indicators were input into the relevant vector machine to predict the performance degradation trend for mechanical equipment. The results of evaluation and prediction of rolling bearing operation state showed that the proposed method can fully extract the characteristic information reflecting rolling bearing operation state, and the prediction method of operation reliability also fully takes into account the historical regularity of the rolling bearing performance degradation. Compared with single kernel function relevance vector machine and other intelligent prediction models, the combined kernel relevant vector machine improves the accuracy and robustness of rolling bearing operation state prediction, and provides a novel mode for mechanical equipment operation state evaluation and performance degradation prediction in engineering application.