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During actual engineering, due to the influence of complex operation conditions, the data of complex systems are distinct, and the range of similarity differs under complex operation conditions. Simultaneously, the length of the data used to calculate the similarity will also impact the result of the fault detection. According to these, this paper proposes a fault detection method based on correlation analysis and improved similarity. In the first place, the complex operation conditions are divided into several simple operation conditions via the existing historical data. In the next place, the length of the data used to calculate the similarity is determined by correlation analysis. Then, an improved similarity calculation method is proposed to make the range of the similarity under multi-operation conditions identical. Finally, this method is applied to the suspension system of the maglev train. The experiment results indicate that the method proposed in this paper can not only detect the fault or abnormal state of the suspension system but also observe the health index (HI) changes of the system at distinct times under multi-operation conditions.

Fault Detection for Complex System under Multi-Operation Conditions Based on Correlation Analysis and Improved Similarity