This paper presents a model-free method for the online identification of sensor faults and learning of their fault dictionary. The method, designed having in mind Cyber-Physical Systems (CPSs), takes advantage of functional relationships among the datastreams acquired by CPS sensing units. Existing model-free change detection mechanisms are proposed to detect faults and identify the fault type thanks to a fault dictionarywhich is built over time. The main features of the proposed algorithm are its ability to operate without requiring any a priori information about the system under inspection or the nature of the possibly occurring faults. As such, the method follows the model-free approach, characterized by the fact the fault dictionaryis constructed online once faults are detected. Whenever available, humans can be considered in the loop to label a fault or a fault class in the dictionary as well as introduce fault instances generated thanks to a priori information. Experimental results on both synthetic and real datasets corroborate the effectiveness of the proposed fault diagnosis system.
IEEE-INNS International Joint Conference on Neural Networks (IJCNN16)