Research On Diagnosis Methods Of Mechanical Fault In HV Motor Outfitted In Mine-used Main Fan

The content of this paper is a sub-topic continuation of "Mine ventilation and power system security condition monitoring and fault diagnosis of early warning systems"(No:2007BAK29B05), which is an "Eleventh Five-Year" National Science and Technology Support Program. It is based on the current situation that the HV motor for mine-used main fan prone to mechanical failure, the function of active duty fault diagnosis systems is imperfect and the accuracy is low.Mine-used main fan is one of the key equipments for ensuring safety production of mine, forcing stable air circulation to reduce the aggregation of gas and dust. Due to long-term continuous running, harsh operating environment and other factors, mechanical fault of HV motor for main fan continuously happen. Given the importance of HV motor for main fan, to ensure safety production of mine, Coal Mine Safety Regulations stipulate that two main fans must be installed, and one of them is used as a backup. As a kind of excess maintenance way, regular maintenance greatly reduces the failure rate of HV motor for main fan, but also increases the production cost, reduces production efficiency. Real time condition monitoring and reliable assessment for mechanical condition of HV motor have important practical and economic significance. Since they will effectively reduce the maintenance cost and prevent serious sudden fault.In this paper, the fault mechanism of two mechanical faults of HV motor for main fan, broken bars and bearing fault, have been analyzed in depth, the latest signal analysis technology and intelligent diagnostic technology have been studied. Through a lot of simulated fault experiments, this paper summed fault characteristic data, proposed a kind of diagnosis method for mechanical fault, which have high reliability and accuracy and can provide theoretical support for mechanical fault diagnosis system of HV motor for main fan. The main research contents are as follows:Combined with maintenance experience and relevant literature, the fault mechanism of broken bars and bearing fault have been studied. Target parameters, which can effectively reflect the operating state of the mechanical components, and software platforms, which used to realize condition monitoring and fault diagnosis, have been determined.Combined with mechanism of broken bars, the method based on Hilbert transform and support vector machine (SVM) was proposed. Among them, the Hilbert transform is used to extract the fault characteristic in the stator current signal, and support vector machine is used for pattern recognition of fault feature vectors. In this paper, the detailed theoretical derivation and simulation analysis for this fault diagnosis method was carried out, the common steps of how to establishing support vector machine classifier was given, and the programs of this method was compiled.Combined with mechanism of bearing fault, the method based on local mean decomposition (LMD) and support vector machine was proposed. Among them, the local mean decomposition is used to extract the fault characteristic in the acceleration signal, and support vector machine is also used for pattern recognition of fault feature vectors. In this paper, the detailed theoretical derivation and simulation analysis for local mean decomposition was carried out, and the method of support vector machine multiple classifier to build was given, finally, the paper also gave the MATLAB programs.According to the running situation of industrial field, the hardware and software structure of fault monitoring system was designed. The acquisition circuits of state parameters were configured, including appropriate voltage sensor, current sensor, rotate speed sensor, and the corresponding data acquisition card. And, the industrial computer was used a upper computer. Based on hardware equipment, the fault monitoring software system was developed, including signal acquisition and preprocessing procedure programs, database management programs, as well as man-machine interface. The test platform for fault monitoring was setted up in the laboratory, hardware and software was on-line debugged, and the PC software was optimized. Debugging results show that the parameters from fault monitoring system is real-time, accurate, and meetting the design requirements.Based on the cause of mechanical fault, combining with the production manufacturing technology, this paper made motors having corresponding mechanical fault. Through building simulated fault testbed, and using fault monitoring system, the state parameters of motor were collected and saved. Using of the fault diagnosis method, stator current signals and acceleration signals were analyzed, and the results show that the methods can accurately judge the type of mechanical fault and the goal of fault diagnosis was achieved.