Fault Condition Recognition of mine hoist Combining Kernel PCA and SVM

Niu, Qiang; Xia, Shixiong; Zhou, Yong; Zhang, Lei

doi:10.1109/icitechnology.2007.4290398

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…In [20], to recognize the state of viscoelastic sandwich structure, an approach based on the adaptive redundant second-generation wavelet packet transform, permutation entropy and the wavelet support vector machine (SVM) was proposed. In [21], a novel state recognition method combining kernel principal component analysis and support vector machine was proposed, and experimental results demonstrated the effectiveness of the proposed algorithm. In [22,23], an artificial neural network was applied to wear state recognition.…”

Section: State Recognitionmentioning

confidence: 99%

A State Recognition Approach for Complex Equipment Based on a Fuzzy Probabilistic Neural Network

Wang

Tan

et al. 2016

Algorithms

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Due to the traditional state recognition approaches for complex electromechanical equipment having had the disadvantages of excessive reliance on complete expert knowledge and insufficient training sets, real-time state identification system was always difficult to be established. The running efficiency cannot be guaranteed and the fault rate cannot be reduced fundamentally especially in some extreme working conditions. To solve these problems, an online state recognition method for complex equipment based on a fuzzy probabilistic neural network (FPNN) was proposed in this paper. The fuzzy rule base for complex equipment was established and a multi-level state space model was constructed. Moreover, a probabilistic neural network (PNN) was applied in state recognition, and the fuzzy functions and quantification matrix were presented. The flowchart of proposed approach was designed. Finally, a simulation example of shearer state recognition and the industrial application with an accuracy of 90.91% were provided and the proposed approach was feasible and efficient.

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Section: State Recognitionmentioning

confidence: 99%