Support vector data description for fusion of multiple health indicators for enhancing gearbox fault diagnosis and prognosis

A novel method for enhancing gearbox fault diagnosis and prognosis is developed by fusion of multiple health indicators through support vector data description. First, the Comblet transform is used to identify gear residual error signals from the raw signal. Second, based on the observation of gear residual error signals, a total of 11 gear health indicators are identified, and are categorized into two types of indicators. The first and second types of indicators are for fault diagnosis and prognosis, respectively. The first type has six indicators, which are sensitive to impulsive signals triggered by anomalous impacts. The second type has five indicators, which are suitable for tracking degradation of faults. Third, through the support vector data description, the first six health indicators are fused into type one indicators for fault diagnosis. The remaining five indicators are fused into type two indicators for fault prognosis. Finally, a Gaussian kernel is designed to enhance the performance of type one and two indicators by optimal range of width size. The effectiveness of the proposed method is validated through experiments. The new method has been proven to be superior to methods that use unfused indicators individually. © 2011 IOP Publishing Ltd.