A zeropoint fault of load cells in truck scale is a typical minor fault and it is difficult to be detected online. A method for detecting zeropoint fault online is proposed by combining a recursive principal component analysis (RPCA) with four types of fault detection indicators. In this method, firstly, the principal component model is updated online by the principal recursive algorithm based on rank 1 modification, and then the four statistics, i.e., the Hotelling's T2 statistic, the squared prediction error (SPE) statistic, the Hawkins TH2 statistic, and the principal component related variable residual (PVR) statistic, are used to construct a comprehensive evaluation method for fault detection. This proposed method for fault detection online is applied to load cells in truck scale, and the experimental results show that the accuracy of zeropoint fault detection is increased with an order of magnitude by the traditional method, which proves the effectiveness of this proposed method.