Abstract
Pulley group plays an important role in the transmission of large mechanical equipment. To obtain informative data for condition monitoring, it is very important to optimize sensor placement on the pulley group. However, due to sharp speed fluctuation, heavy load and complex internal structure, sensor placement for acquiring optimal monitoring points is still a challenging task. Therefore, a novel sensor optimization method based on data fusion is proposed. In this method, the Kalman filter is firstly used to refine the collected signal for dealing with the variable noises. Subsequently, the variable periodicity strength of the signal is calculated to recognize the non-stationary characteristics of the measured signal. A data fusion technique based on maximum likelihood estimation (MLE) is then introduced to estimate sensitive components from the multi-source sensor signals for finding out optimal sensor placement points. The method is validated experimentally on a test rig of the pulley group with variable speed conditions. Analysis results show that the proposed method can recognize the optimal sensor placement points for the pulley group.
Highlights
The pulley group is the key structural component for transferring force and displacement in large mechanical equipment [1,2,3]
This paper proposed a sensor optimization method for pulley group condition monitoring under speed variations
After the refinement based on the Kalman filter, the non-stationary noise interference is reduced
Summary
The pulley group is the key structural component for transferring force and displacement in large mechanical equipment [1,2,3]. This paper takes into account the difficulties of a sensor configuration for the pulley group and proposes an optimal sensor placement technique based on data fusion. In this method, the Kalman filter is employed to deal with the non-stationary noise interference. Correlation analysis between the estimated sensitive signal and sensor collected signals is found to evaluate the optimal locations In this way, the best sensor placements can be successfully identified for the health condition monitoring of the pulley group.
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