HMM在电机轴承上的故障诊断

Fault diagnosis of bearing failure using HMMS

摘要:

为了提高其机械系统故障诊断能力及其准确性，以历史的经验数据为基础对滚动轴承进行健康管理，提出一种新的基于多个隐马尔可夫模型与蚁群聚类算法（ACC）和神经网络相结合的方法来用于轴承故障的诊断与检测，该方法采用HMM与模式识别相结合的方法通过对轴承振动信号进行特征提取，在时频域内进行分析其老化的现象，分别将历史数据和新数据进行故障诊断和检测，并通过HMM和ANFIS来估计其剩余使用寿命和年限.实验结果表明：HMM与模式识别相结合的方法可以准确地对故障进行诊断及预测，通过对结果分析可以得到该方法降低了计算的复杂度，提高了诊断的精度，通过对不同故障诊断实例详细阐述了基于HMM故障诊断方法的有效性和可行性.

Abstract:

In order to improve the fault diagnosis ability of the system and its accuracy, with previous experience in this article is based on data Rolling health management, this paper presents a novel based on multiple hidden Markov models and Kazuo artificial neural network algorithms and methods of combining ant colony to be used to diagnose and detect bearing faults, which uses HMM and pattern recognition method by combining the bearing vibration signal feature extraction, in the frequency domain analysis of the aging phenomenon, namely the historical data and the new data fault diagnosis and testing, while HMM and ANFIS fault prediction is to estimate the remaining useful life and the life. The experimental results show that the method of HMM and pattern recognition can be used to diagnose and predict the faults. The method can reduce the computational complexity and improve the accuracy of diagnosis, through the different fault diagnosis example elaborates on HMM-based fault diagnosis method effectiveness and feasibility.