针对掘进机的截齿磨损状态问题，构建了基于灰色-马尔科夫链模型、Gamma过程预测模型以及维纳过程磨损状态预测模型。通过搭建试验台，提取振动及声发射数据样本，考虑实验环境对实验数据提取的影响，应用小波包分解方法，对数据进行降噪处理。定义6种截齿磨损程度状态，每种状态取50组数据样本，进行模型精度验证，均符合精度要求，进而应用模型进行数据预测研究，对比真是实验数据，结果表明：振动信号加速度能量和下灰色-马尔科夫模型相对误差为0.89%，Gamma模型相对误差为0.47%，维纳过程相对误差为0.39%；声发射信号加速度能量和下灰色-马尔科夫模型相对误差为1.02%，Gamma模型相对误差为0.84%，维纳过程相对误差为0.47%；三种模型预测精度都很好，其中维纳过程预测误差最小，为掘进机截齿磨损退化状态预测研究提供了新的方法。
Abstract
In order to solve the problem of picking gear wear state of road header, the wear state prediction model based on Grey-Markov chain model, Gamma process prediction model and Wiener process prediction model were constructed.The vibration and acoustic emission data samples were extracted by building a test bench. Considering the influence of experimental environment on the extraction of experimental data, the wavelet packet decomposition method was used to de-noise the data.Define 6 kinds of cutter tooth wear state, each state take 50 groups of data samples, verifies the accuracy of model, all conform to the requirements of the precision, data prediction research and application model, contrast is the experimental data, the results show that the vibration acceleration signal energy and the Grey-Markov model relative error is 0.89%, Gamma model relative error is 0.47%, wiener process relative error is 0.39%;The relative error of AE signal acceleration energy and lower Grey-Markov model is 1.02%, the relative error of Gamma model is 0.84%, and the relative error of Wiener process is 0.47%.The prediction accuracy of the three models are all good, and the prediction error of Wiener process is the least, which provides a new method for the prediction of the degradation state of road header pick wear.
关键词
截齿磨损 /
灰色-马尔科夫链 /
Gamma过程 /
维纳过程 /
预测精度
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Key words
Pick wear /
Grey-Markov chain /
Gamma process /
Wiener process /
Prediction accuracy
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