Prognostic of RUL based on Echo State Network Optimized by Artificial Bee Colony

Edgar J. Amaya and Alberto J. Alvares
Publication Target: 
IJPHM
Publication Issue: 
1
Submission Type: 
Full Paper
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ijphm_16_006.pdf1.03 MBMay 30, 2016 - 6:46pm

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.

Publication Year: 
2016
Publication Volume: 
7
Publication Control Number: 
006
Page Count: 
12
Submission Keywords: 
RUL estimation
turbofan engines
echo state networks
Artificial bee Colony
Data-driven prognostics
failure prognostics
particle swarm optimization
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Health management system design and engineering
Software health management
  
 
 
 

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