In this paper we develop a fault detection and isolation method based on data-driven approach. Data-driven methods are effective for feature extraction and feature analysis using statistical techniques. In the proposal, the Cumulated Sum (CUSUM) efficiency is explored for incipient fault detection. The fault is assumed to be a Gain variation, an Offset evolution, a Phase shifting or one of the multiple possible combination of such faults. A first preprocessing stage is proposed for this study and using some statistical other features we proceed to the operations of a Fault Detection and Diagnosis process: Detection, and Isolation. For the detection, the CUSUM efficiency is proved. For the isolation, we propose a specific algorithm based on the combination of several multivariate statistical techniques such as Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA). The classification of each fault or one of their combination is accurately obtained. The results show that for incipient faults (<;10%), the fault detection and isolation is accurate with a relative classification error lower than 3%.