基于贝叶斯理论的刀具剩余寿命预测

Prediction of Remaining Tool Life Based on Bayesian Theory

摘要: 刀具健康状态的监测以及刀具剩余寿命的预测，对于企业与厂家的降本增效来说异常重要。针对单一通道预测所带来的数据预测不精确，包含的退化信息过少，可靠性低等问题，提出一种基于隐马尔可夫模型及多通道融合的刀具剩余寿命预测方法。首先通过计算多通道信号所提取特征的时间序列与对应时间向量的斯皮尔曼等级相关系数对特征时序做单调性排序，其次筛选出单调性优异的特征代入隐马尔可夫模型进行训练，并构建得到健康因子作为观测数据，最后借助贝叶斯理论和马尔科夫链蒙特卡洛采样估计退化模型，实现实时在线不断更新的退化模型模拟真实的退化进程。本文用PHM2010公开数据挑战赛中三槽球头硬质合金铣刀切削不锈钢过程的磨损全寿命数据验证了方法的有效性。

Abstract: The monitoring of tool health status and the prediction of remaining tool life are exceptionally im-portant for cost reduction and efficiency of companies and manufacturers. To address the problems of inaccurate data prediction, low reliability due to the inclusion of too little degradation infor-mation caused by single channel prediction, a tool remaining life prediction method based on Hid-den Markov Model and multi-channel fusion is proposed. Firstly, the time series of the extracted features of the multi-channel signals are ranked monotonically by calculating the Spearman rank correlation coefficients of the corresponding time vectors, secondly, the features with excellent monotonicity are selected and substituted into the hidden Markov model for training, and the health factors are constructed as the observed data, and finally the degradation model is estimated with the help of Bayesian theory and Markov Chain Monte Carlo sampling to achieve real-time online The continuously updated degradation model simulates the real degradation process. This paper validates the validity of the method using wear full life data from the PHM2010 Open Data Challenge for a three-flute ball-tipped carbide milling cutter cutting stainless steel.