Abstract
The accuracy of a support vector machine (SVM) classifier is decided by the selection of optimal parameters for the SVM. The Backtracking Search Optimization Algorithm (BSA) is often applied to resolve the global optimization problem and adapted to optimize SVM parameters. In this research, a SVM parameter optimization method based on BSA (BSA-SVM) is proposed, and the BSA-SVM is applied to diagnose gear faults. Firstly, a gear vibration signal can be decomposed into several intrinsic scale components (ISCs) by means of the Local Characteristics-Scale Decomposition (LCD). Secondly, the MPE can extract the fault feature vectors from the first few ISCs. Thirdly, the fault feature vectors are taken as the input vectors of the BSA-SVM classifier. The analysis results of BSA-SVM classifier show that this method has higher accuracy than GA (Genetic Algorithm) or PSO (Particles Swarm Algorithm) algorithms combined with SVM. In short, the BSA-SVM based on the MPE-LCD is suitable to diagnose the state of health gear.
Highlights
The gearbox is the most crucial transmission mechanism in a rotating machine, and the implementation of online monitoring and diagnosis has become quite urgent and necessary
Based on the trial and error detection practice, this proportion was selected in order to evaluate the performance of the achieved Support vector machine (SVM) which was optimal for the available samples
Basing on the feature of non-stationary of gear fault signals, a method of diagnosing a faulty gear based on the Local Characteristics-Scale Decomposition (LCD)-multi-scale permutation entropy (MPE) and Backtracking Search Optimization Algorithm (BSA)-SVM is proposed
Summary
The gearbox is the most crucial transmission mechanism in a rotating machine, and the implementation of online monitoring and diagnosis has become quite urgent and necessary. The fault signal characteristic is very weak and is usually masked by noise, especially when the fault is in its early stages; it is very difficult for getting the fault features [1]. There are three steps in diagnosing fault gears, namely: characteristic signal detection, feature extraction and fault classification. The process of extracting the features of faults is divided into selection and extraction. In both theoretical and experimental fields, the fact of selection and extraction of fault features is the common principle in diagnosing faults. The extraction of the effective information about the fault characteristics from complex dynamic mechanical signals is the key factor to solve the problem of large-scale complex mechanical and electrical equipment fault diagnosis
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