Study On Application Of Fault Detection Method In Continuous Miner Based On The Improved Kica

The continuous miner is an important equipment in the excavate coal industry. It may make the coal-mining system broken-down, if continuous miner stop running cause of fault.The importance of the continuous miner fault diagnosis is increasingly prominent. In this paper, the fault detection method based on improved kernel independent component analysis (KICA) is developed. The proposed approach is applied to cutting unit reducer of continuous miner for fault detection.KICA is unsupervised and combines the advantages of KPCA and ICA to develop a nonlinear approach to detect fault online. Because the data mapped into feature space become linearly redundant, linear relation data introduce error while the kernel trick is used and the size of the kernel matrix are the square of the number of samples in the training process of KPCA, similarity analysis is developed to improve KICA algorithm, i.e., observation data is deal with using similarity analysis in input space and feature space before KICA algorithm. This method not only decreases the computation load, but also reduces the error while the kernel trick is used.We can extract the dominant feature data from the data of continuous miner by the KICA method. The statistics, calculated based on the feature data, can be better to describe the mining status of the continuous miner. This paper makes use of the Hotelling T2 and SPE statistics for fault detection. The original formula for calculating the SPE statistics is not available any more, because the data is mapped from input space to feature space. To solve this problem, a new formula for KICA is established.This method is applied to the fault detection of the reducer of continuous miner in this paper. The simulation result shows that the KICA method can capture the nonlinear dynamic features effectively, and detects the fault successfully.