1. A computer-implemented method for automatically diagnosing new faults in a device, said method comprising: said computer generating a measure of membership with respect to each feature vector belonging to at least one known class among a plurality of known classes utilizing a classifier; said computer clustering a plurality of members associated with said at least one known class into two clusters including core class members and at least one outlier cluster in order to detect a presence of a new defect and a drift in the probabilistic distribution of the plurality of members between the core class and the at least one outlier cluster in a known defect in the device overtime, wherein the device is a rendering device and the known defect is an image quality defect; said computer determining if a probabilistic distribution of said plurality members of said outlier cluster associated with said class quantifies a change in distribution when a set of spurious features becomes sufficiently different from said corresponding distribution of said core class members to therefore indicate said new faults and said drift in said known defect and thereafter diagnose said new faults and said drift in said known defect in said device, wherein said new faults are unknown image quality defects; and said computer retraining said classifier with updated characteristics of said new defect.