Abstract
This chapter presents an in-depth study on the condition monitoring of rotating machinery using adaptive parametric modelling, focusing on the development of robust state indicators of gearboxes running from a brand new to breakdown state in a natural course, under varying load conditions. Three independent robust state indicators based on state-space representation of a time-varying autoregressive model and noise-adaptive Kalman filtering are proposed and compared with other state indicators considered in previous studies. The experimental validations make use of full lifetime vibration monitoring data of gearboxes under varying load conditions and analyze some critical properties of gear state indicators in real applications over the full lifetime horizon of gearboxes. The results show that the proposed three gear state indicators possess a highly effective and robust property in the state detection of a gearbox, which is independent of variable load conditions, as well as remarkable stability, early alarm for incipient fault and significant presence of fault effects. The proposed three gear state indicators can be directly employed by an online maintenance program as reliable quantitative condition covariates to make optimal maintenance decisions for rotating machinery.
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Zhan, Y., Makis, V. (2006). Robust State Indicators of Gearboxes Using Adaptive Parametric Modeling. 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_9
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DOI: https://doi.org/10.1007/1-84628-269-1_9
Publisher Name: Springer, London
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