Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme. (15th August 2019)

• Record Type: Journal Article
• Authors: Yu, Wennian; Kim, II Yong; Mechefske, Chris
• Is Part Of: Mechanical systems and signal processing. Volume 129(2019)
• Page Start: 764
• Page End: 780
• Publication Date: 2019-08-15
• Subjects: Remaining useful life -- Bidirectional recurrent neural network -- Autoencoder -- Health index; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2019.05.005
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A sensor-based data-driven scheme using deep learning for RUL estimation. The bidirectional recurrent neural network is used to increase input information. A novel bidirectional recurrent neural network based autoencoder scheme is proposed. It shows obvious competitiveness compared with most state-of-art algorithms. Abstract: System remaining useful life (RUL) estimation is one of the major prognostic activities in industrial applications. In this paper, we propose a sensor-based data-driven scheme using a deep learning tool and the similarity-based curve matching technique to estimate the RUL of a system. The whole procedure consists of two steps: in the first step, a bidirectional recurrent neural network based autoencoder is trained in an unsupervised way to convert the multi-sensor (high-dimensional) readings collected from historical run-to-failure instances (i.e. multiple units of the same system) to low-dimensional embeddings, which are used to construct the one-dimensional health index (HI) values to reflect various health degradation patterns of the instances. In the second step, the test HI curve obtained from sensor readings collected from an on-line instance is compared with the degradation patterns built in the offline phase using the similarity-based curve matching technique, from which the RUL of the test unit can be estimated at an early stage. The proposed scheme was tested on two publicly available run-to-failure datasets: the turbofan engine datasets (simulation datasets) and the milling datasets (experimental datasets). The prognostic performance of the proposed procedure was directly compared with the existing state-of-art prognostic models in terms of various prognostic metrics on the two datasets respectively. The comparison results demonstrate the competitiveness of the proposed method used for RUL estimation of systems.