Abstract: This study describes a fault diagnosis system for mechatronics modules realized through the combination of wavelet transformation, fuzzy logic and neural network techniques. As a reference base we have selected Daubechies wavelet. Fault diagnoses accomplish the characteristic frequencies using the fuzzy logic in aggregate neural network. The combination of advanced techniques reduces the learning time and increases the diagnosis accuracy. The experimental results indicate that the proposed method is promising for the mechatronics modules.
Tatiana Kruglova, Danil Shaykhutdinov, Dmitriy Shurygin, Sergey Yanvarev, Roman Leukhin, Dmitriy Litvin, Stas Tarkovalin and Aleksey Zinin, 2016. Intelligent Sensorless Fault Diagnosis of Mechatronics Module Wavelet Transformation Based. Asian Journal of Information Technology, 15: 4694-4697.