基于无限学生t混合模型聚类的机械故障预警方法

往复式压缩机、柴油机等复杂机械的振动信号往往呈现较强的非平稳特性，导致传统单特征门限报警法的报警准确率较低。针对该问题，提出一种基于无限学生t混合模型（infinite student's t-mixture model，iSMM）聚类的机械故障预警方法：首先，通过提取机械振动信号特征构建高维特征空间，并采用iSMM对其进行建模，以描述机械设备的状态；其次，利用基于匹配的KL散度近似算法计算机械设备在历史正常状态和观测状态下的模型间距离；最后，将该距离与基于3σ准则自学习出的报警阈值进行比较，实现故障预警。利用往复式压缩机故障案例对所提方法进行验证，结果表明本文方法较单特征门限报警法报警准确率高且时效性好，可有效地对复杂机械进行故障预警。

A mechanical fault early warning method based on infinite student's t-mixture model clustering

Due to the non-stationary characteristics of the vibration signals of complex machinery such as reciprocating compressors and gas turbines, the signal feature threshold alarm method has low alarm accuracy. In order to solve this problem, a fault early warning method based on the infinite student's t-mixture model (iSMM) is proposed. The proposed method first uses iSMM trained by a high-dimensional feature space which is based on the mechanical vibration signal features to describe the change in performance of the equipment. Secondly, the divergence between the normal condition model and the real-time working model is calculated by matching based on approximating KL divergence. Finally, real-time fault early warning for the mechanical equipment is realized by comparing the divergence with the early warning threshold calculated based on the 3σ rule of thumb. The proposed method has been validated by using actual failure case data for a reciprocating compressor. The results show that this method has high alarm accuracy and good timeliness. The proposed method can provide effective mechanical fault early warning.