Interaction of various components in rotating machinery like gearboxes may generate excitation forces at various frequencies. These frequencies may sometimes overlap with the frequencies of the forces generated by other components in the system. Conventional vibration spectrum analysis does not attenuate noise and spectral frequency band overlapping, which in many applications masks the changes in the structural response caused by the deterioration of certain components in the machine. This problem is overcome by the use of time domain averaging (dsynchronous averaging). In time domain averaging, the vibration signal is sampled at a frequency that is synchronized with the rotation of the gear of interest and the samples obtained for each singular position of the gear are ensemble-averaged. When sufficient averages are taken, all the vibration from the gearbox, which is asynchronous with the vibration of the gear, is attenuated. The resulting time synchronously averaged signal obtained through this process indicates the vibration produced during one rotation of the monitored gear. This direct time domain averaging process essentially acts as a broadband noise synchronous filter, which filters out the frequency content that is asynchronous with the vibration of the gear of interest provided that enough averages are taken. The time domain averaging procedure requires an enormous amount of vibration data to execute, making it very difficult to develop online gearbox condition monitoring systems that make use of time domain averaging to enhance their diagnostic capabilities since data acquisition and analysis cannot be done simultaneously. The objective of this research was to develop a more efficient way for calculating the time domain average of a gear vibration signal. A study of Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) was conducted to assess their suitability for use in time domain averaging. Two time domain averaging models that use ANNs and SVMs were developed. Model 1 uses a single feedforward network configuration to map the input which are rotation synchronized gear vibration signals to the output which is the time domain average of the gear vibration signal, using only a section of the input space. Model 2 operates in two stages. In the first stage, it uses a feedforward network to predict the instantaneous time domain average of the gear vibration after 10 inputs (10 rotation synchronized gear vibration signals) to predict the instantaneous average of the gear rotation. The outputs from the first state are used as inputs to the second stage, where a second feedforward network is used to predict the time domain average of the entire vibration signal. When ANNs and SVMs were implemented, the results indicated that the amount of gear vibration date that is required to calculate the time domain average using Model 1 can be reduced by 75 percent and the amount of gear vibration data that needs to be stored in the data acquisition system when Model 2 is used can be reduced by 83 percent.
Dissertation (M Eng (Mechanical and Aeronautical Engineering))--University of Pretoria, 2006.