抽象的

Multi step ahead prognosis of bearing signals using NARX network

Shridhar Kurse, Narendra Viswanath, K . S. Srinivasan, Pradeep S

Machinery prognosis is the forecast of the remaining operational life, future condition, or probability of reliable operation of the machine based on the acquired condition monitoring data. This approach to modern maintenance practice promises to reduce downtime, spares inventory, maintenance costs, and safety hazards. However, prognosis approaches are not accurate enough, which has become the bottleneck for achieving the full power of Condition-Based Maintenance (CBM). Artificial Neural Network (ANN) based methods have been considered to be a very promising category of methods for machine health condition prediction. In this paper, we train a neural network prediction model called Nonlinear Autoregressive Neural Network with eXogenous inputs (NARX) for a Multi-Step ahead prediction of deteriorating bearing signal. The NARX network is trained using a bearing dataset from FEMTO-ST Institute [1]. As a result the trained network shows satisfactory results for (short term) multi-step ahead prediction. However the long term prediction is challenging and needs further probe. MatLab software is used for network training. Both acceleration and temperature data are used for prognosis.