基于QAR数据的民用飞机故障预测及验证

飞机飞行过程中产生成百上千种飞行参数和数量庞大的飞行数据,但目前这些数据并没有得到充分有效的利用,飞机的维修还处在以定期维修和事后维修为主的阶段。随着航空技术的不断发展,利用飞行数据进行故障预测,转变民机维修模式向视情维修发展变得越来越有必要。首先对基于QAR数据的民用飞机故障预测技术路线进行了说明。其次介绍了适用于民机QAR数据的两种故障预测方法,包括基于曲线拟合的性能预测方法和基于时间序列的趋势预测方法。再次,详细描述了基于QAR数据的故障预测系统的实现途径。通过预测关键参数变化趋势,达到提前发现故障,以制定合理的维护计划,确保飞行安全的目的。最后采用提出的方法对波音飞机的空调、滑油系统关键参数数据进行预测,预测结果验证了方法的有效性。     

基于灰色Verhulst-小波神经网络的装备故障预测研究

针对现代武器装备故障预测样本少、故障预测精度低、维修保障困难等问题,提出一种基于灰色Verhulst-小波神经网络组合模型的装备故障预测方法。该方法综合了灰色Verhulst模型所需样本少的优点和小波神经网络良好的时频局域化性质和学习能力,克服了小样本故障数据在BP神经网络训练中的缺陷。实验结果表明,与相关研究方法比较,所提出方法具有较高的预测精度,对于武器装备故障预测与维修保障具有一定的理论价值和现实意义。     

复杂系统故障预测方法与应用技术研究

故障预测技术是我国未来发展复杂系统增强型视情维修体系(CBM+)的核心技术;在分析了目前广泛使用的基于统计学的故障预测方法基础上,研究了精度更高的基于模型和基于数据驱动等多种故障预测方法,总结了不同预测方法的适用性,结合实际应用提出了一种多方法融合的复杂系统故障预测新方法,与传统的统计学预测法进行了对比,并完成了仿真验证;新方法有利于提高故障预测的准确性,为未来实现复杂系统视情维修与贮存延寿提供技术保障。     

基于改进Apriori算法的地面空调间歇故障预测

针对机坪地面空调间歇故障引起的使用效能低、维修滞后等问题,提出了二次关联累加数组（AS）-Apriori与聚类K-means相结合的间歇故障预测方法,并基于此实现了延误维修预测。其中：AS-Apriori算法解决了Apriori频繁扫描事务库的低效问题,通过实时构造间歇故障数组并对其对应项累加求和;延误维修预测是为了估计出永久故障临界区以安排合理维修,可采用正态分布求出不同间歇故障变量的维修波及延误概率并进行依次累加而实现。验证表明,AS-Apriori提高了运行效率,且二次关联规则支持度提升了20.656个百分点,能更准确预测间歇故障,同时参照数据分析,预测的维修波及延误累加概率呈线性分布,即可预测性高的间歇故障更便于预先维护管理,减少永久故障的形成。     

智能化雷达故障预测及检测技术综述

雷达故障预测和故障检测技术是雷达装备维护从传统化定期检修向智能化视情维修转变的关键技术。为保障雷达作战效能的发挥,需及时对雷达故障进行预测、检测并实时告警。随着微波测量和人工智能技术的日趋成熟,智能化雷达故障预测及检测技术也不断发展。文中详细阐述了当前故障预测与健康管理以及故障检测技术的国内外研究现状,分析了现有智能化雷达故障预测和检测技术的优缺点,梳理了该技术在雷达维修保障领域的研究进展,提出了雷达故障预测和检测过程中可能存在的问题和限制条件。针对实际问题和限制条件,对未来智能化雷达故障预测和检测技术的研究方向进行了展望,为智能化故障预测及故障检测技术在雷达维修保障领域的深入研究提供参考。     

轨道电路故障预测与健康管理体系结构设计

针对现有轨道电路实行故障维修和计划维修的缺陷,提出一种以状态为基础的设备故障预测维修机制.设计故障预测与健康管理(PHM)体系结构与工作流程,采用模糊神经网络的方法,建立一种高压不对称脉冲轨道电路故障预测模型.选定2个输入参数和4个故障输出参数,输出参数利用故障可信度描述,根据专家知识和现场经验形成模糊推理规则表.通过仿真实验验证了PHM体系结构是有效的.     

基于信息源特征的航空部附件故障预测研究

针对舰载机多机种一体化自主保障中机载设备的维修保障需求,提出了基于信息源特征分析的航空关键部附件故障预测方法;首先,从信息源数据特征、研究对象判定、用于预测的可用信息及不确定性4个角度对信息源特征的复杂性进行了分析;其次,根据航空部附件故障频率和平均停机维修时间采用四象限图实现航空关键部附件的判定;最后,基于信息源不同可用信息选择不同的故障预测方法,并介绍了智能融合的神经网络算法和能够消除不确定性的非线性滤波方法,提高了航空部附件故障预测方法的通用性和准确性。     

基于大数据的电力企业资产组维修策略优化研究

汇总过去若干年的电力设备故障数据,运用大数据分析方法,把故障预测技术引入到预防性维修的实践中,提出一种基于大数据的预防性维修策略。首先,根据由状态检测信息得到剩余寿命的预测结果,以预防性维修时的剩余寿命为阀值制定预防性维修策略。然后,根据更新过程理论,建立以电力设备的预防性维修阀值和预测间隔期为优化变量,综合考虑电力设备维修成本、客户满意度、电量销售、停电损失、维修时机选择等约束条件呢,以电力设备平均维修费用最小和电力设备可用度最大为优化目标的预防性维修优化模型。采用人群搜索算法进行优化求解,得到系统最佳的预防性维修阀值和维修预测间隔期。最后,通过引入算例,对所建模型优化仿真求解,得到电力设备最佳的预测周期,在保证电力设备可用度的同时,使电力设备的平均维修费用最小,验证了所建模型的可行性和有效性,从而提高电力企业的整体效益。     

基于虚拟样机的火炮故障仿真预测技术研究

为了实现火炮的预知维修,提高火炮执行任务的成功性,提出了基于虚拟样机的火炮故障仿真预测技术;火炮故障预测技术以当前火炮使用状态为起点,对火炮在未来任务段内可能出现的故障进行预测;建立了火炮虚拟样机,将虚拟样机作为一种定量推理方法引入到故障预测领域;提出了基于动态模糊评判的预测推理机制,基于虚拟样机实现故障过程仿真,实现了定性推理和定量推理的结合,确认实际发生的故障,找出对应的故障原因和故障部件,可以作为实现火炮预知维修的依据.     

基于LSSVM的电子装备故障预测研究

针对电子装备的故障信息不足,故障发生率高等特点,通过故障预测有效的监测设备故障状态以及发展趋势,实现对设备的事先维修,避免重大事故的发生,提高电子设备的安全性。对电子装备故障预测进行了分析,提出了一种基于最小二乘支持向量机（LSSVM）的故障预测方法。首先介绍了LSSVM故障预测算法的基本原理和预测流程;然后,对整个电子装备的故障预测研究可以从一个类似的模拟带通滤波器电路故障预测研究出发,将该元件容差设为不同范围来定义电路的不同故障状态,将LSSVM方法与最小二乘法、支持向量机法对电路的不同状态进行预测,可以得到不同状态的预测值,研究结果表明提出的方法能够实现模拟电路的缓变故障预测,且预测效果较好。     

大型暂冲式风洞自主式维修保障系统研究

2.4米×2.4米暂冲式风洞长期面临着繁重的试验任务,部分设备经常处于超负荷运行状态,故障频次、维修负担也逐年加重。为了解决风洞试修矛盾,提升风洞试验能力,本文基于故障预测与健康管理技术,针对2.4米×2.4米暂冲式风洞的运行和保障需求,结合风洞装备管理业务流程,从数据采集、数据存储、数据分析、数据应用这四个工作流程出发设计了系统软硬件架构,最终搭建了大型暂冲式风洞自主式维修保障系统。实现对故障的实时监测、诊断、预测。经过系统运行实践证明,风洞设备的故障率显著降低,试验能力和效率提升明显,初步建立了风洞的视情维修保障体系。     

Two-stage classifications for improving time-to-failure estimates: a case study in prognostic of train wheels

In order to meet the need for higher equipment availability and lower maintenance cost, much attention is being paid to the development of prognostic systems. Such systems support a proactive maintenance strategy by continuously monitoring the components of interest and predicting their failures sufficiently in advance to avoid disruptions during operation. Recent research demonstrated the potential of a comprehensive data mining methodology for building prognostic models from readily available operational and maintenance data. This approach builds a binary classifier that can determine the likelihood of a failure within a broad target window but cannot provide precise time to failure (TTF) estimations. This paper introduces a two-stage classification approach that helps improve the precision of TTF estimations. The new approach uses the initial methodology to learn a variety of base classifiers and then relies on meta-learning to integrate them. The paper details the model building process and demonstrates the usefulness of the proposed approach through a real-world prognostic application.     

Minor maintenance actions and their impact on diagnostic and prognostic CBM models

Minor maintenance actions can affect condition-monitoring measurements, which may in turn affect the accuracy of diagnostic and prognostic techniques used in condition-based maintenance (CBM). Outputs of a CBM model include the calculation of optimal maintenance decisions, conditional reliability, and the calculation of remaining useful life, among other measures. It is necessary to have a model for the manner in which the condition monitoring data changes over time to produce these output measures; many models have been developed to do so. It is also common to record minor maintenance actions carried out on critical assets, with lubricant changes being one of the most common, but it is unusual for models to consider the impact of such maintenance actions that affect the condition monitoring data. In this paper we discuss the impact of minor maintenance on CBM models. A dataset on a collection of gearboxes, consisting of reliability and oil analysis information, including data on oil changes and oil additions, is used to illustrate the benefit of modelling minor maintenance actions.     

模拟电路故障预测研究综述

故障预测作为近年来的研究热点,是一种提高设备可靠性和降低维护费用的先进技术,而对于模拟电路的故障预测目前还处在起步阶段。本文首先介绍了故障预测技术的概念,然后详细说明了目前国内外的研究现状。针对研究重点模拟电路的故障预测,本文提出了目前存在的问题,并论述了未来的发展趋势,进行了初步探索和展望。     

Model-Based Prognostic Techniques Applied to a Suspension System

Conventional maintenance strategies, such as corrective and preventive maintenance, are not adequate to fulfill the needs of expensive and high availability transportation and industrial systems. A new strategy based on forecasting system degradation through a prognostic process is required. The recent advances in model-based design technology have realized significant time savings in product development cycle. These advances facilitate the integration of model-based diagnosis and prognosis of systems, leading to condition-based maintenance and increased availability of systems. With an accurate simulation model of a system, diagnostics and prognostics can be synthesized concurrently with system design. In this paper, we develop an integrated prognostic process based on data collected from model-based simulations under nominal and degraded conditions. Prognostic models are constructed based on different random load conditions (modes). An Interacting Multiple Model (IMM) is used to track the hidden damage. Remaining-life prediction is performed by mixing mode-based life predictions via time-averaged mode probabilities. The solution has the potential to be applicable to a variety of systems, ranging from automobiles to aerospace systems.      

基于不确定性的故障预测方法综述

故障预测是实现视情维修策略的基础. 不确定性问题在故障预测中普遍存在, 对此, 总结了基于不确定性的故障预测方法的关键问题, 并以不确定性属性的特点将现有故障预测方法分为基于随机性、模糊性、灰性及混合不确定性等4 类. 综述了各类方法的研究现状与不足, 并展望了基于不确定性的故障预测方法的发展趋势, 探讨了基于区间不确定性的故障预测方法的可行性.

     

基于PHM的复杂机电系统健康管理技术研究

随着社会生产力的快速发展,复杂机电系统作为工业自动化领域重要组成之一,智能化、自动化程度越来越高,也对维修保障能力提出了更高要求。目前我国大多数复杂机电系统仍然以定期维修和故障维修为主,维修技术已经显著落后于设备发展水平。本文基于故障预测与健康管理技术,在剖析复杂机电系统常见故障模式的基础上,研究了复杂机电系统健康管理系统的层次架构、功能组成、技术途径,为视情维修保障模式在复杂机电系统中的应用方式方法进行了有益探索。     

Review,analysis and synthesis of prognostic-based decision support methods for condition based maintenance

In manufacturing enterprises, maintenance is a significant contributor to the total company’s cost. Condition based maintenance (CBM) relies on prognostic models and uses them to support maintenance decisions based on the predicted condition of equipment. Although prognostic-based decision support for CBM is not an extensively explored area, there exist methods which have been developed in order to deal with specific challenges such as the need to cope with real-time information, to predict the health state of equipment and to continuously update maintenance-related recommendations. The current work aims at providing a literature review for prognostic-based decision support methods for CBM. We analyse the literature in order to identify combinations of methods for prognostic-based decision support for CBM, propose a practical technique for selecting suitable combinations of methods and set the guidelines for future research.     

Condition-based maintenance of dynamic systems using online failure prognosis and belief rule base

Condition-based maintenance has attracted an increasing attention both academically and practically. If the required physical models to describe the dynamic systems are unknown and the monitored information only reflects part of the state of the dynamic systems, expert knowledge is a source of valuable information to be used. However, expert knowledge is usually in a qualitative form, and therefore, needs to be transformed and combined with the measured characteristic information to provide effective prognosis. As such, this paper focuses on developing a novel approach to deal with the problem. In the proposed approach, a belief rule base (BRB) for the failure prognostic model is constructed using the expert knowledge and the analysis of the failure mechanism. An online failure prognostic algorithm is then proposed on the basis of the currently available characteristic variable information. The failure prognostic model is finally used in a condition based decision model to support the replacement decision of the dynamic systems. A case example is examined to demonstrate the implementation and potential applications of the proposed failure prognostic algorithm and the condition-based replacement model.     

A multi-agent collaborative maintenance platform applying game theory negotiation strategies

Engineering asset management (EAM) is a broad discipline with distributed functions and services. When engineering assets are capital intensive, management requires specialized expertise for diagnosis, prognosis, maintenance and repairs. The current practice of EAM relies on self maintained experiential rules with coordinated collaboration and outsourcing for maintenance and repairs. In order to enhance the life long asset value and efficiency (from the stakeholder’s viewpoint) and after sales service quality (from the asset provider’s viewpoint), this research proposes a collaborative maintenance platform that integrates real time data collection with diagnostic and prognostic expertise. The collaborative system combines and delivers services among asset operation sites (the maintenance demanders), the service center (the intermediary coordinator), the system providers, the first tier maintenance collaborators, and the second and lower tier parts suppliers. Multi-agent system technology is used to integrate different systems and databases. Agents with autonomy and authority work to assist service providers and coordinate communications, negotiations, and maintenance decision support. Finally, game theory is used to design the decision models for strategic, tactical, and operational decision making during collaborative maintenance practices.