Automated fault diagnosis of rolling element bearings based on morphological operators and M-ANFIS

Abstract

Condition monitoring and fault diagnosis of rolling element bearings (REBs) are at present very important to ensure the reliability of rotating machinery. This paper presents a new pattern classification approach for bearings diagnostics, which combines Mathematical Morphology (MM) and Multi-output Adaptive Neuro Fuzzy Inference System (M-ANFIS) classifier. MM is used for filtering Vibration signals, which acquired through the accelerometers mounted on the bearing housing. In this regard, to have an effective morphological operator, the structure elements (SEs) are selected based on the Kurtosis value. Then, to design an automated fault diagnosis structure, the features of this filtered signal, are extracted and used in the M-ANFIS model to learn and classify the bearing condition. The MM method overcomes the drawbacks of other signal processing methods and the M-ANFIS model can handle variation conditions. The experimental results indicate that the proposed strategy not only reduces the error rate but also is robust to changes of load, speed and size of defects.