| Coal mining industry occupies an important position in China’s economy, and the safety of coal mining is also a big problem which should not be ignored. Mine fan is one of the key equipments to ensure the safety of coal mining. Once the mine fan out of order, it will trigger off a chain reaction. So it is important to real time monitor and diagnose the malfunction of the mine fan. In recent years, sensor technology, signal processing technology, intelligent fault diagnosis technology and modern fault diagnosis technology have been developed rapidly, especially in signal processing technology has been rapidly developed and applied. Based on this reason, it is a good way to use signal processing technique to extract and analyze the vibration signal feature of the mine fan.This paper discuss the survey of mechanical fault diagnosis and analyze several common definition and deficiency of time-frequency in the process of signal processing. Simulation experiment clearly shows the Hilbert-Huang transform algorithm and its implementation principles. By the DRVI virtual instrument platform vibration test which verify the feasibility of the Hilbert-Huang transform in fault diagnosis of rotating machinery.Choosing the type GAF mine fan as the object of study, The principle and common faults of the fan vibration mechanism are carefully studied. Using the Space time-index method to test the fan vibration signal and non-stationary of the EMD decomposition layer component, identifying the non-stationary of the vibration signal of fan and selecting the most appropriate method of time-frequency analysis.Finally, application of Hilbert-Huang transform in fault diagnosis of mine fan, using the traditional Fourier transform and Hilbert-Huang transform analysis of vibration signal of mine fan, and based on Hilbert-Huang transform analysis of vibration data acquisition from different time intervals, which proves Hilbert-Huang transform in coal mine ventilator fault diagnosis and superiority from the features. |
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