Research on Multi-faults Classification of Hoister Based on Improved LMD and Multi-class SVM

Zhao, Zhike; Zhang, Xiaoguang; Han, Mengfang; Chen, Yingying; Liu, Weichao; Tian, He

doi:10.1109/iscid.2014.281

Cited by 2 publications

(3 citation statements)

References 6 publications

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“…Liu et al [35] proposed the integral extension LMD (IELMD) to suppress the end effects of LMD. Zhao et al [36] combined support vector regression (SVR) with LMD to achieve endpoint continuation of the decomposed signals, which reduces the end effects of LMD.…”

Section: Improved Lmd Methodsmentioning

confidence: 99%

“…Si et al [48] proposed an improved LMD method based on the cubic trigonometric Hermite interpolation (CTHI) with shape parameters, and experiments demonstrate its effectiveness in identifying different cutting categories of a shearer. Zhao et al [36] developed an improved LMD method to restrain the end effects of LMD and applied it to classify faults of a hoister. Zhao et al [120] employed rational Hermite interpolation to generate the envelope-line and demonstrated the advantages of the improved LMD in fault diagnosis of reciprocating compressors.…”

Section: Applications Using Improved Lmd Methodsmentioning

confidence: 99%

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Review of local mean decomposition and its application in fault diagnosis of rotating machinery

Liu

et al. 2019

Journal of Systems Engineering and Electronics

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Rotating machinery is widely used in the industry. They are vulnerable to many kinds of damages especially for those working under tough and time-varying operation conditions. Early detection of these damages is important, otherwise, they may lead to large economic loss even a catastrophe. Many signal processing methods have been developed for fault diagnosis of the rotating machinery. Local mean decomposition (LMD) is an adaptive mode decomposition method that can decompose a complicated signal into a series of mono-components, namely product functions (PFs). In recent years, many researchers have adopted LMD in fault detection and diagnosis of rotating machines. We give a comprehensive review of LMD in fault detection and diagnosis of rotating machines. First, the LMD is described. The advantages, disadvantages and some improved LMD methods are presented. Then, a comprehensive review on applications of LMD in fault diagnosis of the rotating machinery is given. The review is divided into four parts: fault diagnosis of gears, fault diagnosis of rotors, fault diagnosis of bearings, and other LMD applications. In each of these four parts, a review is given to applications applying the LMD, improved LMD, and LMD-based combination methods, respectively. We give a summary of this review and some future potential topics at the end.

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Section: Improved Lmd Methodsmentioning

confidence: 99%

Section: Applications Using Improved Lmd Methodsmentioning

confidence: 99%

Review of local mean decomposition and its application in fault diagnosis of rotating machinery

Liu

et al. 2019

Journal of Systems Engineering and Electronics

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“…In the current research situation, the multi-classification of SVM has been applied in many fields [1,4,10,18], and there are two kinds of SVM multi-classification ideas [2]. One is to compute all the classification decision functions and solve multiple classification problems at the same time; however, the optimization process of such method is very complex, the computation is huge, and the implementation is difficult, so it has not been widely applied.…”

Section: Analysis Of the Existing Algorithmsmentioning

confidence: 99%

SVM Multi-Classification Optimization Research based on Multi-Chromosome Genetic Algorithm

Ren¹

2018

IJPE

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Regarding SVM multi-classification problem, optimizing the parameters of SVM has become the key problem to improve the performance of the SVM multi-classification algorithm. In order to solve this problem, multi-chromosome genetic algorithm is proposed in this paper and used to optimize these parameters. In the SVM multi-classification decision tree, the algorithm constructs a chromosome for SVM parameter of each node and improves the corresponding rules of crossover and mutation in the genetic algorithm. The improved genetic algorithm optimizes the parameters of SVM in all nodes in the SVM multi-classification decision tree. The experimental results show that the SVM multi-classification decision tree algorithm using the multi-chromosome genetic algorithm has higher classification quality, compared with the traditional multi-SVM multi-classification algorithm.

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Research on Multi-faults Classification of Hoister Based on Improved LMD and Multi-class SVM

Cited by 2 publications

References 6 publications

Review of local mean decomposition and its application in fault diagnosis of rotating machinery

Review of local mean decomposition and its application in fault diagnosis of rotating machinery

SVM Multi-Classification Optimization Research based on Multi-Chromosome Genetic Algorithm

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