Papers by Keyword: Time Domain Feature

Abstract: For the technical problems of coal and rock character recognition in fully mechanized caving faces. A method on characterization and recognition of coal and rock traits were discussed based on the time domain indexes of acoustic pressure data according to the differences of physics and mechanical parameters of coal and rock, and the differences of acoustic pressure data when coal and rock falling impact the rear beam of the sublevel caving hydraulic support. Firstly, the top coal caving experiments were carried out with mining portable vibration recorder developed by China University of Mining and Technology (Beijing) in fully mechanized caving faces in the underground mines, and the acoustic pressure data in quantity were acquired; Then, signal preprocessing were carried on to remove trend items for the selected acoustic pressure data; Finally, the acoustic pressure dates were analyzed in time domain and the time domain features were acquired. Comparison found, peak to peak, variance and kurtosis index are sensitive to the working conditions and the variance with a higher recognition rate. Accordingly proposed an analytical method that based on time-domain features of acoustic pressure date which used variance as recognition indicator, providing technical support for improving the caving automation and intelligent in the fully mechanized caving face.

Abstract: Because neural network has the advantages of fast parallel processing, associative memory, self-organizing and self-learning, it is widely applied in the fault diagnosis of hydraulic system. Present in this paper is a fault diagnosis approch to a typical failure in hydraulic system which is leakage of hydraulic cylinder.The fault diagnosis approch is based on monitoring preesure singal,time domain feature and neural network. According to the method, the time domain feature is extracted from the pressure singal and costitutes the eigenvectors at first, then these eigenvectors are input into neural network to identify faults. The experimental results show that three modes of no leakage, slighter leakage and severe leakage are correctly identified and it can be used in the fault diagnosia of hydraulic syetem.