针对齿轮箱复合故障特征难于提取，诊断缺乏自动识别性，且单通道往往无法全面表征故障信息等问题，提出一种基于随机森林与证据理论相融合的多通道齿轮箱复合故障诊断方法。首先通过小波包变换(WPT)对各通道复合故障信号进行分解，得到故障信号的特征向量；之后引入一种新的特征集组合框架构建针对不同故障的特征数据集，通过随机森林算法划分单个分类模型；接着综合考虑各分类模型，合成每个通道下的集成分类器，并提出一种新的迭代自更新策略不断完善分类器的性能；最后设计一种基于Lance距离的改进D-S证据理论算法，该算法采用Lance 距离来度量各空间证据间的证据距离，并构造Lance矩阵，由此获得相似度矩阵来衡量各证据体间的相似程度和支持度，通过计算各通道的敏感度权重系数进行BPA修正，获得最终的诊断融合结果。通过齿轮箱实验平台进行算法验证，结果表明该方法能有效识别出复合故障中包含的每类故障，并能全面融合不同通道的故障冗余信息，实现齿轮箱复合故障的精确诊断。
Abstract
Aiming at the difficulty of extracting the composite fault features of the gearbox, the lack of automatic identification in the diagnosis, and the failure information is often not fully represented by a single channel, a multi-channel gearbox composite fault diagnosis method based on the fusion of random forest and evidence theory is proposed. Firstly, the composite faults signal of each channel is decomposed by wavelet packet transform (WPT), and the eigenvectors of the fault signal are obtained; Next, a new feature set combination framework is introduced to construct feature datasets for different faults, and a single classification model is divided by random forest algorithm; Then comprehensively consider each classification model, synthesize the ensemble classifier under each channel, and propose a new iterative self-update strategy to continuously improve the performance of the classifier; Finally, an improved D-S evidence theory algorithm based on Lance distance is designed, the algorithm uses the Lance distance to measure the evidence distance between each spatial evidence, and constructs the Lance matrix, from this, a similarity matrix is obtained to measure the similarity and support between the various evidence bodies, the final diagnostic fusion result is obtained by calculating the sensitivity weight coefficients of each channel for BPA correction. The algorithm is verified by the gearbox experimental platform, and the results show that the method can effectively identify each type of fault contained in the composite faults, and can fully integrate the fault redundancy information of different channels to realize the accurate diagnosis of the gearbox composite faults.
关键词
齿轮箱 /
复合故障 /
随机森林算法 /
多通道 /
证据理论
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Key words
gearbox /
composite faults /
random forest algorithm /
multi-channel /
evidence theory
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