A neuro-fuzzy technique for diagnosing faults in rotating machinery

Malfunctions in machinery are often sources of reduced productivity and increased maintenance costs in various industrial applications. For this reason, machine condition monitoring has been developed to recognize incipient fault conditions. In this paper, the fault diagnostic problem is tackled within a neuro-fuzzy approach to pattern classification. An important practical issue for the applicability of any diagnostic tool is the transparency of the underlying classification model, to allow for a direct physical interpretation and inspection for validation purposes. In this view, besides the primary purpose of a high rate of correct classification, the proposed neuro-fuzzy approach strives for obtaining also a transparent classification model, i.e. readable and easily interpretable from the physical point of view. To this aim, appropriate coverage and distinguishability constraints on the fuzzy input partitioning interface are used to achieve the physical interpretability of the membership functions and of the associated inference rules. The efficiency of the approach is verified with respect to a literature problem and then applied to a case of motor bearing fault classification.