In the practical process, various faults occur, which may affect the quality of products. Meanwhile, due to the presence of the feedback closed loop, the effects of certain faults might be compensated. Consequently, not all faults occurring in the system will affect the product quality. According to the impacts of faults on the quality, the faults can be categorized as a quality-unrelated fault, quality-semirelated fault, and quality-related fault. For different categories of faults, corresponding responses should be adopted. Motivated by this, a novel quality-supervised double-layer method (QSDLM) is proposed in this paper to detect and classify the faults simultaneously. The first layer is used for fault detection using principal component analysis (PCA). The second layer is to detect the occurrence of quality-related fault based on the proposed key variable-orthogonal weight PCA. By comparing the result of the first layer with that of the second layer, whether the fault is related to the product quality can be identified, and the fault classification can be conducted. The proposed QSDLM is used in the Tennessee Eastman process to prove its effectiveness. Compared with three typical methods, the proposed QSDLM method can obtain the best results.