School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Southwest Institute of Technical Physics, Chengdu 611731, China.
Sensors (Basel). 2022 Mar 10;22(6):2138. doi: 10.3390/s22062138.
Resulting from the short production cycle and rapid design technology development, traditional prognostic and health management (PHM) approaches become impractical and fail to match the requirement of systems with structural and functional complexity. Among all PHM designs, testability design and maintainability design face critical difficulties. First, testability design requires much labor and knowledge preparation, and wastes the sensor recording information. Second, maintainability design suffers bad influences by improper testability design. We proposed a test strategy optimization based on soft-sensing and ensemble belief measurements to overcome these problems. Instead of serial PHM design, the proposed method constructs a closed loop between testability and maintenance to generate an adaptive fault diagnostic tree with soft-sensor nodes. The diagnostic tree generated ensures high efficiency and flexibility, taking advantage of extreme learning machine (ELM) and affinity propagation (AP). The experiment results show that our method receives the highest performance with state-of-art methods. Additionally, the proposed method enlarges the diagnostic flexibility and saves much human labor on testability design.
由于生产周期短和设计技术快速发展，传统的预测和健康管理 (PHM) 方法变得不切实际，无法满足具有结构和功能复杂性的系统的要求。在所有 PHM 设计中，可测试性设计和可维护性设计面临着重大困难。首先，可测试性设计需要大量的劳动和知识准备，并且浪费了传感器记录的信息。其次，可维护性设计受到不当可测试性设计的影响。我们提出了一种基于软测量和集成置信度测量的测试策略优化方法来克服这些问题。与串行 PHM 设计不同，该方法在可测试性和维护之间建立了一个闭环，以生成具有软传感器节点的自适应故障诊断树。生成的诊断树具有高效率和灵活性的优势，利用了极限学习机 (ELM) 和亲和传播 (AP)。实验结果表明，我们的方法在最先进的方法中取得了最高的性能。此外，该方法还提高了诊断的灵活性，减少了可测试性设计的人力投入。
