Abstract
Prognostic is an engineering technique used to predict the future health state or behavior of an equipment or system. In this work, a data-driven hybrid approach for prognostic is presented. The approach based on Echo State Network (ESN) and Artificial Bee Colony (ABC) algorithm is used to predict machine’s Remaining Useful Life (RUL). ESN is a new paradigm that establishes a large space dynamic reservoir to replace the hidden layer of Recurrent Neural Network (RNN). Through the application of ESN is possible to overcome the shortcomings of complicated computing and difficulties in determining the network topology of traditional RNN. This approach describes the ABC algorithm as a tool to set the ESN with optimal parameters. Historical data collected from sensors are used to train and test the proposed hybrid approach in order to estimate the RUL. To evaluate the proposed approach, a case study was carried out using turbofan engine signals show that the proposed method can achieve a good collected from physical sensors (temperature, pressure, speed, fuel flow, etc.). The experimental results using the engine data from NASA Ames Prognostics Data Repository RUL estimation precision. The performance of this model was compared using prognostic metrics with the approaches that use the same dataset. Therefore, the ESN-ABC approach is very promising in the field of prognostics of the RUL.
Highlights
Unexpected machine failures often result in production downtime, delayed delivery schedule, poor customer satisfaction, economic losses and safety issues
The results achieved by the Echo State Network (ESN)-Artificial Bee Colony (ABC) is compared to the results of other researcher through prognostic metrics, this metrics result of a mathematical equations having as a input variables the estimated Remaining Useful Life (RUL) and the true RUL
Is described the configuration parameters, the topology and the results obtained by classical ESN and the hybrid approach ESN-ABC developed in this work
Summary
Unexpected machine failures often result in production downtime, delayed delivery schedule, poor customer satisfaction, economic losses and safety issues. Prognostic approaches for CMAPSS datasets was classified in the three categories by Ramasso and Saxena (2014), the first category (mapping between set of inputs and RUL) was applied in this paper For this category is showed the following methods: RNN, EKF, MLP, RBF, KF, ANN, ESN, Fuzzy Rules, GA, used by different authors. From different kind of ANN, the Recurrent Neural Networks (RNN) is a powerful tool that integrates large dynamic memory and high adaptable computational capabilities. Their training process is International Journal of Prognostics and Health Management, ISSN 2153-2648, 2016 006.
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