The fault subspace matrix is the common fault identification method for the principal component analysis (PCA) fault diagnosis. However, the fault subspace matrix can only describe which variable deviates from its normal value when a fault happens. When two kinds of faults have the same fault variable, the subspace matrix method cannot distinguish them. In order to improve the capability of fault identification based on the fault subspace matrix method, the positivenegative fault subspace matrix was proposed. The positivenegative fault subspace matrix not only reveals the fault variable but also the changing trend of fault variable, so that the fault types represented by subspace matrixes are more specific. Firstly, taking the minimum of monitoring statistics as the optimization object, the amplitude vectors of variables in various fault states were solved. Secondly, the variables’ reconstructions are computed by the amplitude vectors, and the sign matrix is obtained by taking the sign of each element in amplitude vectors. Thirdly, the fault subspace matrix corresponding to the smallest reconstruction contribution rate was found. Fourthly, the positivenegative fault subspace matrix was calculated by multiplying the sign matrix and the fault subspace matrix. Finally, the current fault type was identified according to the positivenegative fault subspace matrix. The fault identification results for the car dumper hydraulic system proved that the proposed method has higher accuracy than the subspace matrix method, and has a broad scope of application.