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A wide range of applications have started to use Wireless Sensor Networks (WSNs) as an information collection and monitoring tool. However, frequently appeared data faults make it difficult to extract and interpret information from the collected data. Therefore, it is driving the need for fault detection. In this paper, we present a multi-scale principal component analysis (MSPCA) based data fault detection method for wireless sensor networks. MSPCA integrates wavelet analysis and principal component analysis. Wavelet analysis is applied to the collected sensor data to capture time-frequency information, while principal component analysis is performed at each scale to detect data fault, including gradual and persistent faults in coarse scales and high frequent fault in fine scales. Experimental results on real world dataset show the effectiveness of the proposed algorithm.