A dual-LSTM framework combining change point detection and remaining useful life prediction

By Zunya Shi, Abdallah A. Chehade. Reliability Engineering & System Safety, 2021

308 citations recorded by the source corpus.

Abstract Remaining Useful Life (RUL) prediction is a key task of Condition-based Maintenance (CBM). The massive data collected from multiple sensors enables monitoring the complex systems in near real-time. However, such multiple sensors data environments pose a challenging task of combining the sensor data to infer the quality and RUL of the system. To address this task, we propose a Dual-LSTM framework that leverages Long-Short Term Memory (LSTM) for degradation analysis and RUL prediction. The Dual-LSTM relaxes the strong assumption of the fixed change point and detects the uncertain change point unit by unit at first. Then, the Dual-LSTM predicts the health index beyond the change point which can be leveraged to calculate the RUL. The proposed Dual-LSTM (i) achieves real-time high-precision RUL prediction by connecting the change point detection and RUL prediction with the health index construction, (ii) introduces a novel one-dimension health index function, (iii) leverages historical information to achieve detection and prediction tasks by characterizing both long and short-term dependencies of sensor signals through LSTM network. The effectiveness of the proposed Dual-LSTM framework is validated and compared to state-of-art benchmark methods on two publicly available turbofan engine degradation datasets.

Subfields: Control and Systems Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/23589598e261e107e5191f94b16d53350e0ebd75

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Olga Fink
2. Kai F. Goebel
3. Manuel Arias Chao
4. Chetan S. Kulkarni

In Safety, Risk, Reliability and Quality

1. Olga Fink
2. Kai F. Goebel
3. Manuel Arias Chao

Citations in the corpus

• Fusing Physics-based and Deep Learning Models for Prognostics (2020, background): “Motivated by the main study, this final architecture is the result of conducting a grid search with the following search space: number of convolutional hidden layers [5-6], number of channels at each hidden layer [5, 10], filter size [3, 5] and number of neurons at the fully connected layer [50, 100”
• Transformer Network for Remaining Useful Life Prediction of Lithium-Ion Batteries (2022, unknown): “Dual-LSTM [52], with two different LSTM cells in point detection, is used to capture the non-linearity between capacities.”