| Summary: | The degradation of a machine is nonlinear, which brings challenges to its performance assessment during condition monitoring, especially when there is a run-in period. Technically, the quantification of mechanical degradation is to define a distance metric from a health baseline. This paper develops an integrated condition monitoring scheme, where the degradation evaluation and fault diagnosis are combined by using one technical framework. Specifically, an optimum healthy state (OHS) is determined based on the clustering center of the self-organizing map (SOM) neural network instead of the commonly used initial working state. Then, the distance metric deviating from the OHS is defined as a health index, where the perceptual vibration hashing is improved to make it more sensitive to degradation. Visualized fault diagnosis is carried out by the SOM when the health index exceeds the preset threshold. Two cases with experiments are conducted to demonstrate the accuracy and robustness of the proposed method.
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