Fault Prognosis of Mechanical Components Using On-Line Learning Neural Networks

Predictive maintenance of industrial machinery has steadily emerge as an important topic of research. Due to an accurate automatic diagnosis and prognosis of faults, savings of the current expenses devoted to maintenance can be obtained. The aim of this work is to develop an automatic prognosis system based on vibration data. An on-line version of the Sensitivity-based Linear Learning Model algorithm for neural networks is applied over real vibrational data in order to assess its forecasting capabilities. Moreover, the behavior of the method is compared with that of an efficient and fast method, the On-line Sequential Extreme Learning Machine. The accurate predictions of the proposed method pave the way for future development of a complete prognosis system.