Abstract
The accuracy of traditional model-based fault detection of coal mine water level sensor is easily influenced by modeling errors and disturbances of complex mine environment, which cause underreporting or false alarms. In order to solve the problem, this paper presents a fault detection method of principal component cluster analysis for the coal mine water level sensor. The method gets the sample set of the main elements of the water level sensor fault information through main elements extraction and filters the main interference factors of the main elements sample in main fault information by the clustering control method to obtain accurate sensor fault detection data. Then, it analyzes the corresponding fault detection signal through effective fault detection method to determine whether the corresponding sensor is faulty, in order to complete the accurate detection of the fault information of the mine water level sensors. Simulation results show that this method can overcome the adverse impact of the error, and the external environment, timely and accurate detection of the mine water level sensor related fault, improve operating efficiency and safety of the mine water level monitoring system.
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