Abstract
Dynamic Principle Components Analysis (DPCA) is a well-known technique for dynamic system fault detection. Similar to PCA, it computes a standard space with time-dependent average data and then uses a series of residual functions to judge whether a new state vector deviates from the normal space. What makes it superior over normal static PCA is the consideration of historical data and system input data. In this paper, we consider an airship fault detection method using DPCA. Additionally, a scheme based on Kalman filter is proposed to lower the influence of sensor noise. Simulation under DPCA and comparing results under PCA illustrate the effectiveness of the method.
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