2012年5月8日星期二

Gear vibration reducer online monitoring and fault diagnosis


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The gear transmission is the most commonly used and most importantly, drive mode, in machinery and equipment are widely used in aviation, aerospace, machinery and other industrial sectors. With the extensive use of the gear reducer to high-speed, overloaded and requirements of small vibration, low noise and increased life expectancy and improve the operating environment of the direction of vibration of the gear, especially with the backlash gear vibration problem has gradually become One outstanding problems to be studied in depth. In addition, in order to prevent the gear reducer at work, a sudden accident and extend the life of the gear shaft parts, the need for specific gear reducer device on-line monitoring and intelligent fault diagnosis research.
Chapter II with the side gear clearance and eccentric quality of the gear system (the system is the vibration problem of a nonlinear time-varying systems), a numerical study, results show that: gear operating speed and load the same tooth side of the gap change gear fault vibration frequency has a great influence, and when the side gear clearance increases, the composition of the gear fault vibration frequency is not only an integer multiple o f the mesh frequency, but there are meshing frequency scores of times, which produce sub-harmonic and ultra-harmonics; work also affect the speed of gear vibration failure frequency, when the speed reaches a certain value, the higher the operating speed, the gear fault vibration frequency scores of ingredients, the more obvious; work load amplitude changes also affect the gear vibration fault frequencies light load, the gear fault vibration frequency scores ingredients contained and heavy, the load the greater the fraction composition of the gear fault vibration frequency is more obvious; analysis of the frequency of gear torsional vibration failure, due to the power of the gear teeth coupling must consider the impact of the eccentric quality of the gear.
Chapter III of the fault vibration signal picked up on the gear reducer, the findings show that: by ICP-type accelerometer with piezoelectric sensors and impedance converte r to do the sensor, and thus the output of the sensor for low-impedance voltage, signal transmission distance, good stability, high reliability, acceleration sensor monitoring the use of the gear reducer is recommended; also proposed a strain signal to monitor the gear drive pick up on the bearing outer ring in the gear transmission device within the shaft vibration fault signal components, the method can all reflect the vibration of the shaft parts within the gear train fault signal, this signal analysis of the gear transmission shaft parts for a more correct fault diagnosis.
The fourth chapter of the gear reducer gears shaft component failure vibration frequency formula summarizes the study, and launched a heavy duty gear reducer machines commonly used in aligning roller bearing fault vibration frequency formula; also based on variable The time base technology as the basis of the impact of incenti ve modal analysis method, the deceleration of the chassis cover modal analysis, modal analysis by the deceleration of the chassis cover reducer transmission of accurate fault diagnosis Yang sensor installation position to help.
Chapter from the device characteristics, device failure analysis, equipment monitoring and fault diagnosis principles and monitoring system hardware and software, Tsinghua University, design and research THMDS signal monitoring and fault diagnosis system, the system\\ s main function is to complete a large The key equipment for steel mills vibration, displacement, speed, temperature, current words pickup, amplification, filtering, data acquisition, data processing and data network transmission and on-line long-term monitoring, alarm and intelligent fault diagnosis in a timely manner in the event of abnormal operation and fault.
Sixth start with pattern recognition point of view a more detailed discussion of the application of neural network technology in the diagnosis of large rotating machinery so the chapter, the improved BP algorithm on the speed of network training, the results show that this method can improve the speed of network training 70%. The chapter also studied the fault vibration trend forecasting, modeling and fault vibration trend analysis of stationary time series can be collected relatively simple AR (M),; collected non-level implicit time series can be more practical GM (l, 1) model and AR (M), the combination of modeling and fault vibration trend analysis; neural network ensemble forecasting model can improve forecast accuracy involved in the combination of traditional time series forecasting methods.

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