某雷达维修训练模拟器的设计与实现

维修训练模拟器应用于教学可降低对实装的损耗,训练针对性更强,效率更高。在分析某型雷达特性的基础上提出了虚实结合的设计思想,按照模拟系统与实装系统结构相同、功能一致,控制过程相同、逻辑一致的基本要求,规范了操作训练和维修训练指标,以及模拟器的体系结构。采用与实装一致的组合面板建立一个逼真的人机交互操作环境,通过虚拟装备和典型实装部组件构建系统硬件环境;采用虚拟装备分系统结合典型实装部组件,构建模拟维修训练环境,利用虚拟及真实测量工具实现装备维修模拟训练;设置信息交互接口,以实现与数据传输系统和仿真分布交互平台间的互联。该训练模拟器操作简便,可靠性高,为装备操作和维修提供了训练和考核环境。     

A hybrid framework combining data-driven and model-based methods for system remaining useful life prediction

Remaining useful life prediction is one of the key requirements in prognostics and health management. While a system or component exhibits degradation during its life cycle, there are various methods to predict its future performance and assess the time frame until it does no longer perform its desired functionality. The proposed data-driven and model-based hybrid/fusion prognostics framework interfaces a classical Bayesian model-based prognostics approach, namely particle filter, with two data-driven methods in purpose of improving the prediction accuracy. The first data-driven method establishes the measurement model (inferring the measurements from the internal system state) to account for situations where the internal system state is not accessible through direct measurements. The second data-driven method extrapolates the measurements beyond the range of actually available measurements to feed them back to the model-based method which further updates the particles and their weights during the long-term prediction phase. By leveraging the strengths of the data-driven and model-based methods, the proposed fusion prognostics framework can bridge the gap between data-driven prognostics and model-based prognostics when both abundant historical data and knowledge of the physical degradation process are available. The proposed framework was successfully applied on lithium-ion battery remaining useful life prediction and achieved a significantly better accuracy compared to the classical particle filter approach.     

基于虚拟现实的军用后勤装备维修训练系统模型设计

通过研究分析虚拟现实理论和特点,结合后勤装备维修训练系统要素和具体要求,探索一种适应部队信息化建设发展的数字化维修训练系统,即基于虚拟现实的后勤装备虚拟维修训练系统。从系统模型组织结构入手,深入分析了各层次内涵,为建立具体装备的虚拟维修训练系统提供了理论基础。     

融合Cox回归与维纳过程的设备状态评估方法

针对机场自助行李托运设备运行状态评估存在数据利用率低和预测精度不高的问题,提出一种融合Cox回归与维纳过程的设备状态评估方法;首先基于事件型数据构建多风险因素影响下的设备状态突变模型,又基于关键子系统的状态型数据构建设备状态渐变模型,提出了复合退化指标并基于维纳过程建立设备性能退化模型,得到设备整体的健康状态值并提出相应维修决策;利用商用模块化航空推进系统仿真数据集和自助行李托运设备运行监测数据对所提方法进行实验验证,结果表明融合Cox回归与维纳过程的设备状态评估方法提高了设备数据利用率和预测精度.     

A neurophysiological approach to assess training outcome under stress: A virtual reality experiment of industrial shutdown maintenance using Functional Near-Infrared Spectroscopy (fNIRS)

Shutdown maintenance, i.e., turning off a facility for a short period for renewal or replacement operations is a highly stressful task. With the limited time and complex operation procedures, human stress is a leading risk. Especially shutdown maintenance workers often need to go through excessive and stressful on-site trainings to digest complex operation information in limited time. The challenge is that workers’ stress status and task performance are hard to predict, as most trainings are only assessed after the shutdown maintenance operation is finished. A proactive assessment or intervention is needed to evaluate workers’ stress status and task performance during the training to enable early warning and interventions. This study proposes a neurophysiological approach to assess workers’ stress status and task performance under different virtual training scenarios. A Virtual Reality (VR) system integrated with the eye-tracking function was developed to simulate the power plant shutdown maintenance operations of replacing a heat exchanger in both normal and stressful scenarios. Meanwhile, a portable neuroimaging device – Functional Near-Infrared Spectroscopy (fNIRS) was also utilized to collect user’s brain activities by measuring hemodynamic responses associated with neuron behavior. A human–subject experiment (n = 16) was conducted to evaluate participants’ neural activity patterns and physiological metrics (gaze movement) related to their stress status and final task performance. Each participant was required to review the operational instructions for a pipe maintenance task for a short period and then perform the task based on their memory in both normal and stressful scenarios. Our experiment results indicated that stressful training had a strong impact on participants’ neural connectivity patterns and final performance, suggesting the use of stressors during training to be an important and useful control factors. We further found significant correlations between gaze movement patterns in review phase and final task performance, and between the neural features and final task performance. In summary, we proposed a variety of supervised machine learning classification models that use the fNIRS data in the review session to estimate individual’s task performance. The classification models were validated with the k-fold (k = 10) cross-validation method. The Random Forest classification model achieved the best average classification accuracy (80.38%) in classifying participants’ task performance compared to other classification models. The contribution of our study is to help establish the knowledge and methodological basis for an early warning and estimating system of the final task performance based on the neurophysiological measures during the training for industrial operations. These findings are expected to provide more evidence about an early performance warning and prediction system based on a hybrid neurophysiological measure method, inspiring the design of a cognition-driven personalized training system for industrial workers.     

复杂系统维护策略最新研究进展: 从视情维护到预测性维护

对于复杂、可修复的工程系统, 设备维护是确保系统安全性、可靠性、可用性的重要手段之一. 系统维护策略已经历修复性维护、定时维护、视情维护等多种维护策略. 其中, 视情维护是目前最受关注的维护策略, 它通过收集和评估系统的实时状态信息进行维护决策, 具有全寿命周期内系统可靠性高、运营维护成本低等优点. 近年来, 随着物联网技术、信息技术和人工智能的快速发展, 一种更新颖的视情维护策略——预测性维护逐渐成为领域研究热点. 本文首先简要回顾了系统维护策略的发展历程; 然后, 重点介绍了视情维护的研究进展, 根据决策支持技术的不同, 将视情维护划分为基于随机退化模型的视情维护和基于数据驱动的预测性维护, 对每类技术的发展分支与研究现状进行了疏理、分析和总结; 最后, 探讨了当前复杂系统维护策略面临的挑战性问题和可能的未来研究方向.     

装备虚拟维修培训系统设计与实现

针对目前部队及相关军事院校对武器装备维修培训的需求,结合虚拟现实技术的发展现状、系统结构特征设计了基于虚拟维修技术的装备维修培训系统总体方案。对其中的三维模型设计、维修过程设计、碰撞检测、交互方式等关键技术进行了分析与研究,实现了角色管理、虚拟维修演示与训练、维修信息记录等功能,涵盖学习、训练、评估环节。最后以某武器装置为对象对该系统进行实际应用,结果证明可以满足用户在装备维修训练领域的需求,可有效提高装备维修效率,为装备维修培训提供了新的可行方法。     

某型战车信息终端仿真器的设计

针对装备维修训练中存在的装备结构复杂,价格昂贵,维修训练成本高的问题;设计了一个用于装备维修训练的信息终端仿真器;以某型战车驾驶员任务终端的内部结构、工作原理、信号特征和维修流程为依据,采用仿真技术、嵌入式设计技术等,重新设计与实物相似的终端仿真器,并以该仿真器为核心,辅以信号发生器及上位机等软硬件设备构造用于维修训练的仿真平台;该仿真器可进行信息终端的内部构造原理认知、维修过程训练和训练结果评估,应用表明该仿真器可满足装备维修教学和培训需求,提高维修人员的维修技能水平。     

基于虚拟现实的装备可视化维修训练系统

针对装备武器系统传统维修训练模式的不足,本文提出了一种基于虚拟现实技术的可视化装备虚拟维修训练 系统,给出了系统的总体架构、结构组成和功能实现。该系统已成功应用于某型号装备的虚拟维修训练中。     

某型雷达虚拟维修训练系统的设计与实现

为提高教学训练效果，弥补装备教学训练中实装不足、装备损耗大、教学训练成本高等实际问题，设计并实现了某型雷达虚拟维修训练系统。该虚拟维修训练系统包括系统主界面和系统集成框架两大部分，具有装备结构展示、装备操作训练、装备维修训练、训练效果评估、数据支持等多项功能，有效解决了上述装备教学训练中存在的实际问题。目前，该系统已应用到相关专业的装备构造与维修、装备综合实践等多个课程当中，取得的很好的教学训练效果，系统的设计开发可为其他装备虚拟维修训练系统的开发提供参考与借鉴。论文首先介绍了系统的基本功能和结构，然后详细论述了系统模型的开发过程，最后给出了虚拟训练工作流程。     

A predictive maintenance system for multi-granularity faults based on AdaBelief-BP neural network and fuzzy decision making

Predictive maintenance of production equipment is a prerequisite to ensure safe and reliable manufacturing operations. To avoid unexpected shutdown and even casualties caused by faults during production, it is paramount to design an effective predictive maintenance decision system for production equipment. Most of the related research works concentrate on early warn of specific faults but neglect the differentiations of the fault severity. To address the issue, this paper presents an intelligent predictive maintenance system for multi-granularity faults of production equipment based on the AdaBelief-BP (back propagation) neural network and the fuzzy decision making. The characteristics of the system presented in this paper include: (1) The proposed system implements a two-stage framework, integrating the functions of fault type prediction and fault degree prediction, which can provide comprehensive fault information throughout production lifecycles; (2) On the maintenance solution identification stage, fuzzy logic-based decision making is carried out to determine appropriate maintenance solutions based on the practical vague boundary of fault severity. In the system, the design of the AdaBelief-BP neural network can achieve a higher convergence rate and a better generalization capability as well. Meanwhile, to the best of our knowledge, in this research, it is the first time to use the migration of the fuzzy membership degree as the indicator of the changing condition of fault severity to facilitate more accurate maintenance solution identification. To verify the effectiveness of the system, comparison experiments with some popular algorithms are conducted. Benchmarking results show that the developed system can achieve higher prediction accuracy as well as higher efficiency than the comparative methods.     

舰船动力装置虚拟维修训练软件的开发

随着舰船动力装置的不断复杂化,其维修训练成为装备使用维护过程中的一大难题.针对传统维修训练模式受装备数最限制、开发周期长,投资大的特点,在分析虚拟维修训练系统发展现状及其功能需求的基础上,以CATIA为建模平台,以VIRTOOLS为三维交互引擎,以ACCESS为后台数据库平台,以.NET为系统集成平台,研究并开发了舰船动力装置虚拟维修训练软件.经实践验证表明,软件不仅能够满足舰船动力装置维修圳练任务的需要,缩短开发周期,节约训练经费,同时能够用作一个通用的虚拟维修洲练平台.     

PredMaX: Predictive maintenance with explainable deep convolutional autoencoders

A novel data exploration framework (PredMaX) for predictive maintenance is introduced in the present paper. PredMaX offers automatic time period clustering and efficient identification of sensitive machine parts by exploiting hidden knowledge in high-dimensional, unlabeled temporal data. Condition monitoring systems often provide such data, which is further analyzed by human experts or used for training predictive models.PredMaX reduces data dimensionality in two steps: An explainable deep convolutional autoencoder is applied on the data first, followed by principal component analysis. The automatic clustering is performed in the latent space of the autoencoder, ensuring higher accuracy than the clustering in the space of principal components. If clusters of normal and abnormal operation are known, the reasoning module is able to reveal the measurement channels that contributed the most to the latent representation moving from normal to abnormal operation.Beyond the detailed presentation of the PredMaX approach, the paper presents the case study of identifying the most important signals that can be used for predicting oil degradation in an industrial gearbox. The case study is performed on a data-driven basis with minimal human assistance and without preliminary knowledge of the machine.     

企业离散式智能设备预测性维护综述

针对智能设备的大量使用且缺乏根据监测大数据进行故障自动分析、判断与处理的问题,研究了基于物联网技术、大数据技术、边云协同技术的智能设备预测性维护框架和模式.提出针对非智能设备安装传感器实现设备智能化的方法.指出边缘计算负责设备工况数据的实时采集、分析,可快速甄别设备故障并实时报警;云计算聚焦同类设备运行海量历史数据的挖掘和分析,形成故障自动预测分析和诊断模式并下载至智能边缘设备.在研究了模型驱动、数据驱动、概率统计驱动、数字孪生和概率数字孪生驱动等故障预测模式后,提出了采用数据驱动的多层级数据融合模式,为制定企业性智能设备维保方案提供借鉴作用.     

基于建康管理技术的机载计算机智能故障诊断方法

为了满足飞机机载电子设备以状态监控为基础的视情维修保障策略,提升设备可维护性,提出了一种基于在线检测、故障预测、辅助决策的健康监控管理故障诊断方法,支持对机载电子设备的健康状态进行预测和评估。通过划分机载电子设备子功能的敏感威胁区域,对这些区域设计专门的威胁预警监控电路,进行功能危害监控,建立推理监控模型对监控电路故障进行预警监控,结合辅助决策的方式对预警到的故障进行定位,实现对电子设备的智能故障诊断。通过FMEA的分析与故障注入测试验证,该预警电路、推理模型和辅助决策能有效的预测故障及定位,具有较高的故障预测覆盖率,可提高机载计算机的维修性、降低维修时间,在电子设备视情维修策略上具备工程应用价值。     

基于风洞的设备健康管理与数据有效性判定平台研究

低速增压风洞是满足我国航空工业科技发展而建设的一座气动力重大基础试验设施;为了保障该设施的高效率和可靠地运行,以各机电设备、电气测控设备、机械装置为对象,根据其故障模式和故障特点选取合适的监测点,获取实时工作状态数据,再以数据为基础,进行状态监测、故障诊断、故障预测,实现预先性决策和针对性快速维修;基于OSA-CBM+体系构建的风洞健康管理系统,根据设备的运行状态,实现对试验数据的有效性进行实时判定,并实现了风洞装备由事后维修向视情维修转变;实现了装备从使用、维护、管理模式由分散式管理向集约式管理的转变;实现了装备系统故障诊断、预测及判读从人工智能向机器智能的转变。     

Prediction maintenance integrated decision-making approach supported by digital twin-driven cooperative awareness and interconnection framework

Predictive maintenance is one of the important technical means to guarantee and improve the normal industrial production. The existing bottlenecks for popularization and application are analyzed. In order to solve these problems, a cooperative awareness and interconnection framework across multiple organizations for total factors that affect prediction maintenance decision-making is discussed. Initially, the structure and operation mechanism of this framework are proposed. It is designed to support the sharing of data, knowledge and resources. As a key supporting technology, the digital twin is also integrated into it to improve the accuracy of fault diagnosis and prediction and support making a maintenance plan with higher accuracy and reliability. Then, under this framework, an integrated mathematical programming model is established with considering the parameter uncertainty and an NSGA-II hybrid algorithm is utilized to solve it. Moreover, an adjustment strategy for a maintenance plan is discussed in response to the dynamic characteristics of the actual maintenance environment. Finally, a case, prediction maintenance decision-making for bearings in grinding rolls of the large vertical mill, is studied. Analysis results verify the advantage of the integrated solving mechanism based on the proposed framework. The framework and integrated decision-making approach can guide the implementation of predictive maintenance with higher accuracy and reliability for industrial enterprises.     

Intelligent machinery health prognostics under variable operation conditions with limited and variable-length data

High accuracy health prognostics are significant to machinery intelligent operation and maintenance. Current data-driven prognostics achieve great success that benefits from amply learning samples. In fact, data scarcity challenge widely exists in machinery prognostics and health management, especially for high-end equipment. This study aims to solve this dilemma and proposes a novel meta learning algorithm reconstructed by classic variable-length prediction mode and attention mechanism, namely meta attention recurrent neural network (MARNN). Specifically, we first develop the encoder-decoder with attention mechanism (EDA) cell to perform episodic learning for the subtask-level upgrade. Then multiple subtasks with EDA as prediction models are aggregated to accomplish meta-level upgrade, thus mining the general degradation knowledge from historical datasets. Finally, cross-domain prognostics tasks can be easily realized through fine-tuning tricks, and three rotating machinery run-to-failed experiments are conducted to prove the generalizations of MARNN, which can obtain desired results even when the on-site adaptation data is reduced to one-twentieth.     

智能装备故障预测与健康管理系统研究

针对智能装备预测性维护存在的智能化和网络化程度不高、物理模型建模困难等 问题,研究了数据驱动的智能装备远程故障预测与健康管理系统(PHM)的实施框架、关键技术 和系统开发方法。具体阐述了数据驱动 PHM 系统的运行模式,在此基础上分析了 PHM 系统的 软件架构和关键技术,首先利用 EEMD 对原始信号进行降噪和重构,将重构后的信号作为输入 建立基于 RBF 神经网络的故障诊断模型;然后采用动态神经网络建立基于时间序列的故障预测 模型,并建立基于故障阈值的故障报警机制;最后利用混合编程和网络化开发技术开发了数据 驱动的远程 PHM 系统。实际应用结果表明,该系统能以较高效率完成故障诊断、故障预测等 核心功能,具有良好的实用性。     

装备分布式虚拟维修训练云仿真关键技术

传统分布式虚拟维修训练系统仿真任务和仿真设备耦合度高,系统仿真效率低,扩展困难;同时,作为分布式交互支撑的高层体系结构(HLA)和运行支撑框架(RTI)仅适用于局域网络,无法实现大范围的仿真资源共享和维修协同互操作。借鉴云计算和云仿真思想,提出了一种应用于装备虚拟维修训练领域的云仿真体系结构和框架实现方案;研究了面向Web服务的广域网RTI组件层次化体系结构,设计了基于Web服务的仿真邦员,实现了仿真过程监控和远程邦员任务迁移功能;基于最小调度法实现了仿真平台的动态负载平衡,研究了平台的可视化仿真功能和仿真数据的分布式存储技术。在此基础上完成了某型装备的虚拟维修训练网络化仿真开发,初步证明了虚拟维修训练云仿真的可行性和有效性