Robustness testing framework for RUL prediction Deep LSTM networks

Efficiency and robustness in remaining useful life (RUL) prediction are crucial in system health monitoring. Thus, the internal logic computation of a Deep LSTM model for RUL prediction is mainly shaped and evaluated over a training data-set and its performance examined on a testing data-set. This paper proposes a framework for testing robustness of deep Long Short Term Memory (LSTM) architecture for remaining useful life prediction that enables to gain confidence in the trained LSTM model for RUL prediction and ensures better quality. The resiliency of proposed Deep LSTM networks for RUL estimation using stress functions is first checked then the effect of the stress on model performance is analyzed. A comparison between the performance of the constructed mutant fuzzed Deep LSTM networks and the original Deep LSTM model for RUL prediction is provided to determine the quality of the RUL prediction model. Furthermore, the main purpose of this paper is to determine to what extent Deep LSTM models in the neighborhood of the trained LSTM model still have high test accuracy and quality scoring. Thus, the use of phi-stress operators shows that we could build stable and data-independent Deep LSTM models for RUL prediction. Finally, the proposed framework is validated using the Commercial Modular Aero Propulsion System Simulation (C-MAPSS) data-set. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes a framework to test the robustness of deep LSTM architecture for RUL prediction, and validates its resilience through the use of stress functions. The comparison between mutant fuzzed Deep LSTM networks and the original model indicates the quality of the RUL prediction model. The use of phi-stress operators demonstrates the ability to build stable and data-independent Deep LSTM models for RUL prediction.