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A dirty data recognition method for machinery condition monitoring in big data era

Condition monitoring of machinery has entered the big data era, while the existence of dirty data reduces the quality of the whole data. In order to recognize the dirty data included in machinery monitoring data, a new method is proposed in this paper. First, a feature named sampled power index (SPI) is designed to transform the dirty data recognition issue into the outlier recognition. Then the windowing technique, the difference operation and the logarithm transform are introduced to reduce the feature tendency and the feature volatility. Next, auto regression-generalized autoregressive conditional heteroskedasticity (AR-GARCH) model is applied to regress the feature series and produce the crippled local means and local volatilities. Finally, the features are normalized and the 3σ criterion is applied to recognize the dirty data. The performance and the feasibility of this proposed method are evaluated by a simulation and an experiment. The results validate the effectiveness of the proposed method.