Fuzzy Neural Network for the Machine Health Diagnosis

Large-scale and complex mechanical equipments usually operate under complicated and terrible conditions, making them inevitable for faults with various modes and severity. As they can incur substantial production loss and recovery cost. It is therefore, extremely essential to prognose an incipient fault before it leads to serious damage. However, faults of large scale and complex mechanical equipments are characterized by weak response, multi-fault coupling, etc., and it is hard to detect and diagnose incipient and compound faults for these equipments. Hence, the need to automatically analyze the data is apparent and provides an opportunity for computational intelligence (CI) methods to have a significant impact on fault diagnosis research. Neural Networks and Fuzzy Logic, individually or combined, can of great support in these studies. Companionship of Artificial Neural Networks (ANN) and Fuzzy logic (FL) have attracted the growing interest of researchers in various scientific and engineering especially mechanical engineering areas due to the growing need of adaptive intelligent systems to solve the real world problems. This paper focuses the review on the various applications of ANN and FL in the field of machinery health diagnosis.