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A novel support vector regression method for online reliability prediction under multi-state varying operating conditions

机译：多状态变化工况下在线可靠性预测的支持向量回归新方法

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摘要

Modeling the evolution of system reliability in the presence of Condition Monitoring (CM) signals is an important issue for improved reliability assessment and system lifetime prediction. In practice, during its lifetime, a system usually works under varying operating conditions due to internal or external factors such as the ambient environments, operational profiles or workloads. In this context, the system reliability can show varying evolution behaviors (follow changing underlying trajectories), which presents new challenges to describe precisely the dynamics of system reliability. Thus, this paper proposes a novel data-driven approach to address the problems including the identification of varying operating conditions, the construction and dynamical updating of evolution model, and finally the online prediction of system reliability, focusing on systems under one common and typical case of varying operating conditions, the multi-state operating condition. Experiments based on artificial data and some widely studied real reliability cases reveal that the proposed method has superior performance compared with some existing benchmark approaches, in the case under consideration. This improved reliability prediction provides fundamental basis for advanced prognostics such as the Remaining Useful Life (RUL) estimation.
机译：在状态监测（CM）信号存在下对系统可靠性的演变进行建模是改进可靠性评估和系统寿命预测的重要问题。实际上，由于其内部或外部因素（例如周围环境，操作配置文件或工作负载），系统在其生命周期内通常会在变化的操作条件下工作。在这种情况下，系统可靠性可以表现出变化的演化行为（遵循不断变化的基础轨迹），这给精确描述系统可靠性的动态提出了新的挑战。因此，本文提出了一种新颖的数据驱动方法，以解决以下问题：识别变化的工作条件，演化模型的构建和动态更新，最后是系统可靠性的在线预测，重点是在一种常见和典型情况下的系统。变化的工作条件，即多状态工作条件。基于人工数据的实验和一些经过广泛研究的真实可靠性案例表明，在考虑中的情况下，与现有的一些基准测试方法相比，该方法具有更好的性能。这种改进的可靠性预测为高级预测（例如，剩余使用寿命（RUL）估计）提供了基础。

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