由于生产周期短,设计技术发展迅速,传统的预后和健康管理(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需求。在所有PHM设计中,可测试性设计和可维护性设计面临着严峻的困难。首先,可测试性设计需要大量的劳动和知识准备,并浪费传感器记录信息。第二,可维护性设计因可测性设计不当而受到不良影响。为了克服这些问题,我们提出了一种基于软测量和集成信念测量的测试策略优化。而不是串行PHM设计,该方法在可测性和可维护性之间构造了一个闭环,以生成具有软传感器节点的自适应故障诊断树。生成的诊断树确保了高效率和灵活性,利用极限学习机(ELM)和亲和力传播(AP)。实验结果表明,我们的方法与最先进的方法相比具有最高的性能。此外,该方法提高了诊断的灵活性,节省了测试性设计的人力。
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.