Dempster-Shafer evidence theory for multi-bearing faults diagnosis
Dempster-Shafer evidence theory for multi-bearing faults diagnosis
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DOI：
10.1016/j.engappai.2016.10.017
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发表时间：
2017-01-01
影响因子：
8
通讯作者：
Al-Obaidi, Salah Mahdi
中科院分区：
文献类型：
--
作者：
Hui, Kar Hoou;Lim, Meng Hee;Al-Obaidi, Salah Mahdi
支持向量机（SVM）是一种常用的机器故障自动诊断方法，通过处理大量的输入特征和低采样数据集来对多个机器故障进行分类。SVM对于仅涉及二进制故障分类的故障检测是公知的（即，健康与故障）。然而，当支持向量机用于多故障诊断和分类时，它们导致分类精度下降;这是因为支持向量机用于多故障分类的适应需要将多分类问题减少到多个二进制分类问题的子集，这导致每个单独的支持向量机模型产生许多矛盾的结果。为了克服这个问题，一种新的SVM-DS（Dempster Shafer证据理论）模型，提出了解决冲突的结果从每个SVM模型，从而提高分类精度。结果分析表明，所提出的SVM-DS模型提高了故障诊断模型的准确性从76%到94%，因为SVM-DS不断完善，并消除了所有冲突的结果从原来的SVM模型。所提出的SVM-DS模型被发现是更准确和有效地处理多故障诊断和分类问题中常见的行业，相比，原来的SVM方法。
Support vector machines (SVMs) are frequently used in automated machinery faults diagnosis to classify multiple machinery faults by handling a high number of input features with low sampling data sets. SVMs are well known for fault detection that involves binary fault classifications only (i.e., healthy vs. faulty). However, when SVMs are used for multi-faults diagnostics and classification, they result in a drop in classification accuracy; this is because the adaptation of SVMs for multi-faults classifications requires the reduction of the multiple classification problem into multiple subsets of binary classification problems that result in many contradictory results from each individual SVM model. To overcome this problem, a novel SVM-DS (Dempster Shafer evidence theory) model is proposed to resolve conflicting results generated from each SVM model and thus increase the classification accuracy. The analysis of results shows that the proposed SVM-DS model increased the accuracy of the fault diagnosis model from 76% to 94%, as SVM-DS continuously refines and eliminates all conflicting results from the original SVM model. The proposed SVM-DS model is found to be more accurate and effective in handling multi-faults diagnostic and classification problems commonly faced in the industries, as compared to the original SVM method.