A Neural Network-Evolutionary Computational Framework for Remaining Useful Life Estimation of Mechanical Systems
| Autor: | David Laredo, Oliver Schütze, Jian-Qiao Sun, Zhaoyin Chen |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Computer Science - Machine Learning Neural Networks Databases Factual Computer science Cognitive Neuroscience cs.LG RUL estimation Evolutionary algorithm Machine Learning (stat.ML) 02 engineering and technology Evolutionary algorithms computer.software_genre Machine Learning (cs.LG) Databases Computer 020901 industrial engineering & automation Artificial Intelligence Simple (abstract algebra) Statistics - Machine Learning Component (UML) MD Multidisciplinary 0202 electrical engineering electronic engineering information engineering Artificial Intelligence & Image Processing cs.NE Neural and Evolutionary Computing (cs.NE) Layer (object-oriented design) Prognostics Factual Neurons Artificial neural networks Artificial neural network Computer Science - Neural and Evolutionary Computing Neural Networks (Computer) Perceptron stat.ML Biological Evolution Mechanical system Linear Models 020201 artificial intelligence & image processing Neural Networks Computer Data mining computer Algorithms Moving time window |
| Zdroj: | Laredo, David; Chen, Zhaoyin; Schütze, Oliver; & Sun, Jian-Qiao. (2019). A neural network-evolutionary computational framework for remaining useful life estimation of mechanical systems.. Neural networks : the official journal of the International Neural Network Society, 116, 178-187. doi: 10.1016/j.neunet.2019.04.016. UC Merced: Retrieved from: http://www.escholarship.org/uc/item/5d25p602 Neural networks : the official journal of the International Neural Network Society, vol 116 |
| DOI: | 10.1016/j.neunet.2019.04.016. |
| Popis: | This paper presents a framework for estimating the remaining useful life (RUL) of mechanical systems. The framework consists of a multi-layer perceptron and an evolutionary algorithm for optimizing the data-related parameters. The framework makes use of a strided time window to estimate the RUL for mechanical components. Tuning the data-related parameters can become a very time consuming task. The framework presented here automatically reshapes the data such that the efficiency of the model is increased. Furthermore, the complexity of the model is kept low, e.g. neural networks with few hidden layers and few neurons at each layer. Having simple models has several advantages like short training times and the capacity of being in environments with limited computational resources such as embedded systems. The proposed method is evaluated on the publicly available C-MAPSS dataset, its accuracy is compared against other state-of-the art methods for the same dataset. Published at Neural Networks 116, (2019) 178-187 |
| Databáze: | OpenAIRE |
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