Fault diagnosis based on support vector machines with parameter optimization by an ant colony algorithm

Abstract

Since support vector machines (SVM) exhibit a good generalization performance in the small sample cases, these have a wide application in machinery fault diagnosis. However, a problem arises from setting optimal parameters for SVM so as to obtain optimal diagnosis result. This article presents a fault diagnosis method based on SVM with parameter optimization by ant colony algorithm to attain a desirable fault diagnosis result, which is performed on the locomotive roller bearings to validate its feasibility and efficiency. The experiment finds that the proposed algorithm of ant colony optimization with SVM (ACO—SVM) can help one to obtain a good fault diagnosis result, which confirms the advantage of the proposed ACO—SVM approach.

Intelligent fault diagnosis of roller bearings with multivariable ensemble-based incremental support vector machine

“…Parameters (C, r) of support vector machine are selected from [0.5, 50] and 10-fold cross validation method is used. Ant colony algorithm [38,39] is also adopted for parameters optimization of C and r. intelligent fault diagnosis method with multivariable ensemble-based incremental support vector machine, which are used in the following Tables. From Table 5, it is seen that the proposed method and typical support vector machine exhibit better performance than other seven methods (Discrete Cosine Transform, Daubechies wavelet, Symlets wavelet, Walsh transform, FFT, Walsh-Rough set theory, FFT-Rough set theory) in identifying three common fault conditions (ball fault, inner race fault and outer race fault), which hit the highest accuracy of 100%.…”

Combined Failure Diagnosis of Slewing Bearings Based on MCKD‐CEEMD‐ApEn

“…Xiong et al [26] developed a scheme using SVMs for diagnosing bearing conditions, where a 97.42% accuracy is achieved. Zhang et al [27] proposed a fault diagnosis scheme for locomotive roller bearings using an SVM classifier. Compared with some methods based on other neural networks, SVM has both a simple structure and an improved generalization capability.…”

A Rolling Element Bearing Fault Diagnosis Approach Based on Multifractal Theory and Gray Relation Theory

“…While SVMs based on statistical learning theory, which is of specialties for a smaller number of samples, have better generalization than ANNs and ensure that the local and global optimal solution are exactly the same [24]. However, the accuracy of a support vector machine (SVM) classifier is highly decided by the selection of optimal parameters for SVMs [24,25]. To ensure the diagnostic accuracy, an optimization algorithm [24,25] or / and complex multi-class concept [13,26] have to be used subsidiarily to improve the effective of SVMs.…”

“…However, the accuracy of a support vector machine (SVM) classifier is highly decided by the selection of optimal parameters for SVMs [24,25]. To ensure the diagnostic accuracy, an optimization algorithm [24,25] or / and complex multi-class concept [13,26] have to be used subsidiarily to improve the effective of SVMs. Here, in order to solve the issue of generality versus accuracy, an adaptive gray relation algorithm was developed to achieve accurate patter recognition based on a small number of samples.…”