With the advent of Industry 4.0, making accurate predictions of the remaining useful life (RUL) of industrial components has become a crucial aspect in predictive maintenance (PdM). To this aim, various Deep Neural Network (DNN) models have been proposed in the recent literature. However, while the architectures of these models have a large impact on their performance, they are usually determined empirically. To exclude the time-consuming process and the unnecessary computational cost of manually engineering these models, we present a Neural Architecture Search (NAS) technique based on an Evolutionary Algorithm (EA) applied to optimize the architecture of a DNN used to predict the RUL. The EA explores the combinatorial parameter space of a multi-head Convolutional Neural Network with Long Short Term Memory (CNN-LSTM) to search for the best architecture. In particular, our method requires minimum computational resources by making use of an early stopping policy and a history of the evaluated architectures. We dub the proposed method ENAS-PdM. To our knowledge, this is the first work where an EA-based NAS is used to optimize a CNN-LSTM architecture in the field of PdM. In our experiments, we use the well-established Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset from NASA. Compared to the current state-of-the-art, our method obtains better results in terms of two different metrics, RMSE and Score, when aggregating across all the C-MAPSS sub-datasets. Without aggregation, we achieve lower RMSE in 3 out of 4 sub-datasets. Our experimental results verify that the proposed method is a reliable tool for obtaining state-of-the-art RUL predictions and as such it can have a strong impact in several industrial applications, especially those with limited available computing power. © 2021 Elsevier B.V. All rights reserved.

Evolutionary neural architecture search for remaining useful life prediction / Mo, Hyunho; Custode, Leonardo Lucio; Iacca, Giovanni. - In: APPLIED SOFT COMPUTING. - ISSN 1568-4946. - 108:(2021), pp. 107474.1-107474.20. [10.1016/j.asoc.2021.107474]

Evolutionary neural architecture search for remaining useful life prediction

Mo, Hyunho;Custode, Leonardo Lucio;Iacca, Giovanni
2021-01-01

Abstract

With the advent of Industry 4.0, making accurate predictions of the remaining useful life (RUL) of industrial components has become a crucial aspect in predictive maintenance (PdM). To this aim, various Deep Neural Network (DNN) models have been proposed in the recent literature. However, while the architectures of these models have a large impact on their performance, they are usually determined empirically. To exclude the time-consuming process and the unnecessary computational cost of manually engineering these models, we present a Neural Architecture Search (NAS) technique based on an Evolutionary Algorithm (EA) applied to optimize the architecture of a DNN used to predict the RUL. The EA explores the combinatorial parameter space of a multi-head Convolutional Neural Network with Long Short Term Memory (CNN-LSTM) to search for the best architecture. In particular, our method requires minimum computational resources by making use of an early stopping policy and a history of the evaluated architectures. We dub the proposed method ENAS-PdM. To our knowledge, this is the first work where an EA-based NAS is used to optimize a CNN-LSTM architecture in the field of PdM. In our experiments, we use the well-established Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset from NASA. Compared to the current state-of-the-art, our method obtains better results in terms of two different metrics, RMSE and Score, when aggregating across all the C-MAPSS sub-datasets. Without aggregation, we achieve lower RMSE in 3 out of 4 sub-datasets. Our experimental results verify that the proposed method is a reliable tool for obtaining state-of-the-art RUL predictions and as such it can have a strong impact in several industrial applications, especially those with limited available computing power. © 2021 Elsevier B.V. All rights reserved.
2021
Mo, Hyunho; Custode, Leonardo Lucio; Iacca, Giovanni
Evolutionary neural architecture search for remaining useful life prediction / Mo, Hyunho; Custode, Leonardo Lucio; Iacca, Giovanni. - In: APPLIED SOFT COMPUTING. - ISSN 1568-4946. - 108:(2021), pp. 107474.1-107474.20. [10.1016/j.asoc.2021.107474]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/305215
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