D. Martinez-Rego, . Fontenla-Romero, A. Alonso-Betanzos, M.V. Jarel, and A.B. Durn (Spain)
Engineering applications, Vibration analysis, machinery predictive maintenance, artificial neural networks, local modeling
In recent years, scientific and technological interest in the predictive maintenance of industrial machinery has steadily risen. This interest is explained by the huge economic sav ings that can be obtained with an early diagnosis of ma chinery faults. In this area, vibration analysis has become almost the universal method to assess the state of a ma chine. Although there are many general known rules that can help to assess the state of a machine, it is still necessary to know and evaluate each machine individually to obtain an accurate diagnosis and forecasting of its future condi tion. Furthermore, this evaluation must be done by experts in the area. This leads to the necessity of automatizing this process. To build a complete automatic predictive maintenance sys tem based on vibration analysis, a module capable of es tablishing the baseline state and forecasting the future con dition of a machine must be constructed as the base of the overall monitoring system. In this paper, a scheme to use neural networks for this purpose is presented. Also, the re sults achieved by a local modeling neural network applied to this problem are presented to empirically prove the ef fectiveness of the proposed method.
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