A Bayesian approach to consequent parameter estimation in probabilistic fuzzy systems and its application to bearing fault classification
A Bayesian approach to consequent parameter estimation in probabilistic fuzzy systems and its application to bearing fault classification
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DOI：
10.1016/j.knosys.2017.05.007
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发表时间：
2017-08-01
影响因子：
8.8
通讯作者：
de Oliveiraae, Jose Valente
中科院分区：
文献类型：
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作者：
Li, Chuan;Ledo, Luiz;de Oliveiraae, Jose Valente
轴承是旋转机械中的重要部件，是其故障的主要原因之一，其健康状况直接关系到这类机械的安全有效运行。据我们所知，将概率模糊系统应用于轴承故障分类尚属首次。所考虑的概率模糊分类器类型是基于简约模糊规则的模型，其中每个规则可以诊断一组故障，每个故障具有其概率。对于这种现实世界的应用，人们希望开发出可解释的、准确的MIMO模糊系统，能够处理数据(振动信号)中存在的维度和不确定性。对于参数估计，我们采用了两步序贯最先进的数据驱动方法。首先，使用迭代监督聚类算法估计规则的前件。基于前件，然后估计后件参数。为此，提出了一种新的后验估计方法。这是基于这样一种观察，即对于定义一条规则而言，并非该规则适用范围内的所有训练数据都是平等相关的。提出了在特征空间中选择相关区域进行后续参数估计的两个准则。结果表明，与上述最先进的方法相比，用所提出的方法训练的概率模糊诊断器的性能在统计上有显著改善，而与用于定义相关区域的标准无关。此外，所提出的后验参数估计方法对诊断人员的整体训练几乎没有任何开销。结果表明，可以在模型的细节层次和精度之间找到一个平衡点。然而，当精度是唯一的比较标准时，所提出的概率模糊系统系统地与其他数据驱动模型的性能相匹配，例如基于距离的方法(K-近邻)、连接者(概率神经网络)或最大间隔分类器(支持向量机)。(C)2017爱思唯尔B.V.保留所有权利。
A bearing is an essential component in rotating machinery, one of its principal cause of failure, and its health condition is directly related to the safety and effective operation of such machinery. To the best of our knowledge, it is the first time that a probabilistic fuzzy system is applied to bearing fault classification. The type of probabilistic fuzzy classifier considered is a parsimonious fuzzy rule based model where each rule can diagnose a set of faults each one with its probability. For this kind of real world application, it is desirable to develop interpretable and accurate MIMO fuzzy systems, able to deal with the dimensionality and uncertainty present in data (vibration signals). For parameter estimation we adopt a two steps sequential state-of-the-art data-driven method. First, the antecedents of the rules are estimated using an iterative supervised clustering algorithm. Based on the antecedents the consequent parameters are then estimated. For this, a new method for consequent estimation is proposed. This is based on the observation that for defining a rule consequent not all training data within the region of applicability of that rule are equality relevant. Two criteria for selecting a relevant region in the feature space for consequent parameter estimation are proposed. Results show a statistically significant improvement on the performance of probabilistic fuzzy diagnosers trained with the proposed method, independently of the criterion used for defining the relevant region, when compared with the above mentioned state-of-the-art method. Moreover, the proposed consequent parameter estimation method practically has no overhead on the overall training of the diagnoser. Results show that an equilibrium can be found between the model level of detail and its accuracy. However, when accuracy is the sole comparison criterion, the proposed probabilistic fuzzy systems systematically matches the performance of other data-driven models like distance based methods (K-nearest neighbors), connectionists (probabilistic neural networks), or maximum margin classifiers (support vector machines). (C) 2017 Elsevier B.V. All rights reserved.