Intelligent fault diagnosis of shearer based on simplified interval kernel global-local feature fusion

As one of the main equipment in coal mining, the health status of shearer is difficult to be accurately monitored due to some factors such as harsh working environment and narrow operating space, and it is highly susceptible to the impact of coal and rock, which can cause faults and directly affect the working efficiency of shearer. In addition, due to the special working environment of shearer, the vibration data collected is very easy to be interfered by various factors and difficult to be used, which affects the reliability and intelligence level of its monitoring. To accurately monitor the health state of shearer, based on easily available data such as current, temperature and flow under normal state of shearer, the global and local characteristics of the dataset are comprehensively considered to avoid the loss of structural information. The objective function constructed by principal component analysis and local retention projection is used, and an intelligent fault diagnosis method based on a simplified interval kernel global-local feature fusion is established by combining mutual information, kernel function, interval product estimation and reconstruction contribution methods, which is used for the feature extraction of nonlinear uncertain data characterizing the state of coal mining machines. An experimental evaluation is carried out to assess the performance of the proposed method by using the simulated faults of the actual operational data of shearers in the Shanxi Xiegou Coal Mine and the actual fault data of the Shaqu No. 2 Coal mine. The results show that compared with the midpoint-radius kernel PCA, the kernel local preservation projection and interval kernel global-local feature fusion algorithms, the proposed method has a good monitoring effect on the single variable simulation fault, the multi-variable pick loss and the waterway blockage fault of shearers. Its fault monitoring accuracy reaches 99.90%, 99.40% and 98.70%, respectively, and its calculation time is only 0.324, 0.367 and 0.345 s respectively. Moreover, it can accurately identify the relevant variables that cause faults, which provides a theoretical basis for determining the fault location of shearer and also points out the direction for the accurate implementation of maintenance decisions.