Abstract
The underground local fan and auxiliary fan also play a vital role in the underground air quality, compared with the system fan. However, the number of underground local fans and auxiliary fans is large and widely distributed, which is disadvantageous to adopt the same method of online monitoring and fault diagnosis method as the system fan. In order to find a new fault diagnosis method, which is cost-effective and reliable, this paper proposes a fault diagnosis method based on sound signal. It analyzes the source of fan noise and studies the overall scheme of mine fan fault diagnosis expert system based on sound signal. The fault expert system consists of four parts: signal acquisition and noise elimination, feature extraction, state recognition, and fault diagnosis. Its principle is briefly introduced. The denoising method of wavelet is adopted in this paper. Wavelet packet is used to extract the characteristics of sound signal, and the energy size and energy proportion of each frequency component are used as the basis of knowledge acquisition and reasoning. Through the analysis of the measured signals of the fan in the normal operating state, the feature vectors were extracted as the basis for the discrimination of the normal state after noise elimination. At the same time, the audio processing software was used to simulate the sound signals in three fault states. Then, the feature vector of the fault state is extracted, which is obviously different from that of the fan in the normal operation. As the basis of fault state analysis of the expert system, it lays the foundation for the realization of the expert system of mine fan equipment running state diagnosis.
Highlights
Mine fan is the core equipment of mine ventilation safety, which provides the necessary power for air flow. e normal operation of the fan is very important for the whole ventilation system
Compared with the local fan which is used to press in the working face and the auxiliary fan which is used to adjust the air volume in the branch air path of the mine ventilation network, the realtime monitoring of the underground auxiliary fan and the local fan has the characteristics of large number and complex distribution. erefore, it is necessary to find an accurate and convenient detection method to detect the operation status of the fan in real time, so as to ensure a good working environment in the mine and the health and labor safety of underground workers
Hong and Liao proposed a classification method based on Drosophila algorithm to optimize the least squares support vector machine (LSSVM)
Summary
Mine fan is the core equipment of mine ventilation safety, which provides the necessary power for air flow. e normal operation of the fan is very important for the whole ventilation system. E sound signal under the normal operation state can be collected and analyzed to extract the detailed features, which can effectively carry out monitoring and fault diagnosis. Erefore, the real-time acquisition of acoustic signal from mine fan equipment and quantitative analysis of its variation law and mutation characteristics by digital signal processing method can diagnose faults or hidden dangers [8]. The state recognition is carried out, and the characteristic parameters of acoustic signal obtained after signal processing are compared with the allowed parameters or discriminant parameters in the system knowledge base through the inference engine, so as to diagnose whether there is fault in its operation state and further determine the type of fault. E monitoring and fault diagnosis of mine local fan can be realized by collecting the sound signal of fan operation, combining with the signal denoising and feature extraction technology, and using the expert system
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