Bearing faults diagnosis using fuzzy expert system relying on an Improved Range Overlaps and Similarity method

Abstract Bearing fault diagnosis represents the core of induction machines condition monitoring. This paper presents an application of fuzzy expert system (FES) to bearing faults diagnosis. Here, fuzzy rules are automatically induced from numerical data using the Similarity partition method. Data of faulty bearings presents high noise level. Thus, an Improved Range Overlaps method (IRO) is proposed to select input feature vectors by giving them validity degrees. The Similarity method partition was found confused with features presenting range overlap. Consequently, the new proposed Improved Range Overlaps method is found quite suitable for improving the classifier accuracy. The model validity and efficiency were proved using experimental bearing faults data from Case Western Reserve University database and the NSF I/UCR Center on Intelligent Maintenance Systems (IMS) database.