Fault Diagnosis of Subway Indoor Air Quality Based on Local Fisher Discriminant Analysis

초록

This article proposes a combined principal component analysis (PCA) and local Fisher discriminant analysis (LFDA) scheme to improve the fault diagnosis performance of the indoor air quality (IAQ) measuring devices in subway stations. The combined scheme employs PCA for fault detection step and subsequently utilizes LFDA for diagnosing faulty IAQ sensors. A fault discriminant index based on LFDA discriminant components is proposed for fault diagnosis. Effectiveness of the proposed approach is demonstrated on the IAQ measuring system, where three types of IAQ sensor faults including bias fault, drifting fault, and complete failure fault are involved. Results demonstrate that diagnosing performance of LFDA is better than that of conventional Fisher discriminant analysis. The combined method has the capability of detecting and discriminating the sensor faults in the subway system.

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