Abstract
The effectiveness and reliability of measurement techniques for bearing condition monitoring are affected by both the locations of the sensors and the signal processing algorithms selected for defect feature extraction. This chapter describes a structural dynamics-based sensor placement strategy by investigating the mechanisms of signal propagation from the source of its generation to the sensor location. Numerical simulation of a group of sensors for measuring vibration measurement on two custom-designed bearing test beds is presented, and an approach to optimizing the sensor placement based on the Effective Independence (EfI) method is introduced. The chapter then comparatively investigates several commonly employed signal processing techniques for feature extraction, such as wavelet transform-based signal enveloping, the Wigner-Ville distribution, and the wavelet packet transform, and evaluates performance using vibration signals measured from the bearing test beds.
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Gao, R.X., Yan, R., Sheng, S., Zhang, L. (2006). Sensor Placement and Signal Processing for Bearing Condition Monitoring. In: Wang, L., Gao, R.X. (eds) Condition Monitoring and Control for Intelligent Manufacturing. Springer Series in Advanced Manufacturing. Springer, London. https://doi.org/10.1007/1-84628-269-1_7
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DOI: https://doi.org/10.1007/1-84628-269-1_7
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