Design of poka-yoke system based on fuzzy neural network for rotary-machinery monitoring

Abstract

Early detection of machine failure will improve the performance of the production process. The Poka-Yoke device was developed to monitor the machine. The vibration signal is captured by sensors and inputted in Poka-yoke device for processing. Poka-Yoke device has two components, Fuzzy-Neural Network identification and decision maker. The first component, the time-domain signal is transformed into the frequency domain, magnitude and frequency are treated as Fuzzy membership functions by using the statistical parameters as mechanical harmonic distortion and then are trained by Neural Network. The second component, the decision is in the form of machine condition statements such as normal, alarm, and shutdown. Simulation's results show that the method can be applied to identify the machine condition in term of bearing faults. Moreover, the Poka-yoke system that developed can be used to monitor machine condition automatically.