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.

剩余使用寿命 (RUL) 预测的效率和稳健性对于系统健康监测至关重要。因此，用于 RUL 预测的 Deep LSTM 模型的内部逻辑计算主要是在训练数据集上进行塑造和评估，并在测试数据集上检查其性能。本文提出了一个框架，用于测试用于剩余使用寿命预测的深度长期短期记忆 (LSTM) 架构的鲁棒性，该框架能够使经过训练的 LSTM 模型对 RUL 预测充满信心并确保更好的质量。首先检查所提出的使用应力函数进行 RUL 估计的深度 LSTM 网络的弹性，然后分析应力对模型性能的影响。

此外，本文的主要目的是确定在经过训练的 LSTM 模型附近的 Deep LSTM 模型在多大程度上仍然具有较高的测试准确率和质量评分。因此，使用$\phi$-stress 算子表明我们可以为 RUL 预测构建稳定且独立于数据的 Deep LSTM 模型。最后，使用商用模块化航空推进系统仿真 (C-MAPSS) 数据集验证了所提出的框架。