Robustness of AI-based prognostic and systems health management

Prognostic and systems Health Management (PHM) is an integral part of a system. It is used for solving reliability problems that often manifest due to complexities in design, manufacturing, operating environment and system maintenance. For safety-critical applications, using a model-based development process for complex systems might not always be ideal but it is equally important to establish the robustness of the solution. The information revolution has allowed data-driven methods to diffuse within this field to construct the requisite process (or system models) to cope with the so-called big data phenomenon. This is supported by large datasets that help machine-learning models achieve impressive accuracy. AI technologies are now being integrated into many PHM related applications including aerospace, automotive, medical robots and even autonomous weapon systems. However, with such rapid growth in complexity and connectivity, a systems’ behaviour is influenced in unforeseen ways by cyberattacks, human errors, working with incorrect or incomplete models and even adversarial phenomena. Many of these models depend on the training data and how well the data represents the test data. These issues require fine-tuning and even retraining the models when there is even a small change in operating conditions or equipment. Yet, there is still ambiguity associated with their implementation, even if the learning algorithms classify accordingly. Uncertainties can lie in any part of the AI-based PHM model, including in the requirements, assumptions, or even in the data used for training and validation. These factors lead to sub-optimal solutions with an open interpretation as to why the requirements have not been met. This warrants the need for achieving a level of robustness in the implemented PHM, which is a challenging task in a machine learning solution.This article aims to present a framework for testing the robustness of AI-based PHM. It reviews some key milestones achieved in the AI research community to deal with three particular issues relevant for AI-based PHM in safety-critical applications: robustness to model errors, robustness to unknown phenomena and empirical evaluation of robustness during deployment. To deal with model errors, many techniques from probabilistic inference and robust optimisation are often used to provide some robustness guarantee metric. In the case of unknown phenomena, techniques include anomaly detection methods, using causal models, the construction of ensembles and reinforcement learning. It elicits from the authors’ work on fault diagnostics and robust optimisation via machine learning techniques to offer guidelines to the PHM research community. Finally, challenges and future directions are also examined; on how to better cope with any uncertainties as they appear during the operating life of an asset.     

计及信息不确定性的风电机组健康状态实时评估方法

运行工况识别作为风电机组状态监测与健康管理领域的重要环节,往往受到不确定信息以及高速实时数据流的影响,造成健康状态评估难以有效实施。在此背景下,文中提出一种基于Spark流式处理的健康状态实时评估方法。首先,采用大数据分析技术实现风电机组运行工况的空间划分;然后,在充分考虑风电机组监测信息不确定性的情况下,结合数据采集与监控(SCADA)历史运行数据,对基于高斯云模型和高斯云变换的健康状态评估模型进行训练,并以健康指数作为风电机组健康状态评估的指标。最后,将该评估方法应用在中国北方某风电场1.5 MW风电机组故障前的健康状态评估中。算例分析结果表明,该方法可监测到风电机组健康状态的变化趋势,初步实现了故障的早期预警。     

单板计算机的故障模式与机理及影响分析

故障模式、机理及影响分析((FMMEA）是研究产品的每个组成部分可能存在的故障模式、故障机理并确定各个故障模式对产品组成部分和功能的影响的一种可靠性分析方法。详细介绍了对单板计算机进行FM-MEA的实施过程,包括系统定义,确定潜在的故障模式,分析故障原因、故障机理、故障影响等一系列的过程。对单板计算机进行了有限元建模,并通过热应力分析和振动应力分析,得到了其在不同环境下的温度分布、振动模态等信息,为故障物理模型提供输入。利用各种故障物理模型对单板计算机各单点的故障进行了定量的计算。分析结果表明：由温度循环引起的小外形封装(SOP）的随机存储器芯片的焊点疲劳故障的故障前时间最短,是整个电路板的薄弱环节;焊点热疲劳故障为单板计算机主故障机理,在实施PHM过程中要重点监测该部位的故障状况。     

基于PHM的企业联盟加工过程质量控制技术研究

在网络化制造环境下,由于企业联盟具有信息化、集成化、全球化和动态化的特点,对传统的质量控制技术提出了新的要求.基于预测与健康管理( PHM)技术对网络化制造环境下的机械加工过程质量控制进行探索,提出一种由设备层、分布式信息处理层、网络构架层,以及管理与决策层构成的系统体系结构与实施方案,并给出了基于模糊专家系统和模糊神经网络的故障诊断方法.     

用于PHM的数据采集处理系统设计

本设计应用于故障预测与健康管理(PHM)体系,负责采集传感器数据,并按照传感器种类对数据进行相应的数字信号处理,提取出上层系统感兴趣的信息.传感数据采集处理系统以DSP为主处理器,负责数字信号处理,以FPGA为辅助处理器,控制AD芯片进行采集,采用CPCI接口与上层系统通信.该系统实现了多通道高速采集、高性能DSP与F...     

Prognostics and Health Management of PEMFC – State of the art and remaining challenges

Fuel Cell systems (FC) represent a promising alternative energy source. However, even if this technology is close to being competitive, it is not ready for large scale industrial deployment: FC still must be optimized, particularly by increasing their limited lifespan. This involves a better understanding of wearing processes and requires emulating the behavior of the whole system. Furthermore, a new area of science and technology emerges: Prognostics and Health Management (PHM) appears to be of great interest to face the problems of health assessment and life prediction of FCs. According to this, the aim of this paper is to present the current state of the art on PHM of FCs, more precisely of Proton-Exchange Membrane Fuel Cells (PEMFC) stack. PHM discipline is described in order to depict the processing layers that allow early deviations detection, avoiding faults, deciding mitigation actions, and thereby increasing the useful life of FCs. On this basis, a taxonomy of existing works on PHM of PEMFC is given, highlighting open problems to be addressed. The whole enables getting a better understanding of remaining challenging issues in this area.     

舰炮电气故障神经网络预测技术研究

为实现舰炮的故障预测和健康管理,针对舰炮电气系统的运行机理,利用电路仿真软件提取了该系统的故障知识,建立了故障诊断专家系统知识库,并运用BP神经网络方法实现了该舰炮电气系统的故障预测。仿真结果验证了该方法的有效性,取得了良好的诊断预测效果。     

空空导弹PHM系统总体设计研究

在分析空空导弹结构组成的基础上,参照视情维修开放体系结构OSA－CBM,提出了空空导弹PHM框架模型,据此展开现役空空导弹应用PHM技术的实例研究：分析了贮存寿命期影响导弹性能的因素,确定采集参数,根据成本、尺寸、传输方式等提出传感器选型要求,建立导弹包装箱状态监控系统;利用电气属性测试中采集的参数,参考取值范围设置原理,建立评分标准,根据权重值计算出导弹健康指标。     

舰船预测与健康管理系统研究

介绍了PHM技术的内涵和舰船PHM系统的作用,阐述了PHM关键技术,并根据研究现状展望了PHM技术的今后发展方向。     

健康监测技术在机载雷达结构上的应用分析

故障预测与健康管理（Prognostics and Health Management, PHM）技术是一项前沿的复杂工程应用技术,在机载雷达系统上具有迫切的需求和广泛的应用前景。文中首先分析了典型的飞机PHM系统架构;然后,针对机载雷达系统结构特点,提出一种基于故障模式、影响及危害性分析（Failure Mode, Effects & Criticality Analysis, FMECA）的结构健康监测系统构建流程,得到了典型机载雷达系统结构的健康监测系统架构;最后,对PHM技术在某机载雷达大型一维转台上的应用进行了分析,获得了各关键系统的多参量实时数据,为机载雷达系统的结构安全提供早期预警、故障诊断与寿命预测。本研究对结构健康监测技术在机载雷达领域的工程化应用具有指导意义。