Condition-based Maintenance with Multi-Target Classification Models

Condition-based maintenance (CBM) recommends maintenance actions based on the information collected through condition monitoring. In many modern cars, the condition of each subsystem can be monitored by onboard vehicle telematics systems. Prognostics is an important aspect in a CBM program as it deals with prediction of future faults. In this paper, we present a data mining approach to prognosis of vehicle failures. A multitarget probability estimation algorithm (M-IFN) is applied to an integrated database of sensor measurements and warranty claims with the purpose of predicting the probability and the timing of a failure in a given subsystem. The results of the multi-target algorithm are shown to be superior to a singletarget probability estimation algorithm (IFN) and reliability modeling based on Weibull analysis.     

Application of the condition based maintenance checking system for aircrafts

Condition Based Maintenance (CBM) systems have evolved as an effective health and usage monitoring mechanism in aircrafts by reducing the costs associated with unscheduled maintenance. CBM systems help maintainers to detect and manage the condition of aviation system components and take maintenance actions when there is evidence of need. In this paper we describe the application of a software prototype, which is an automation of the CBM practices. We briefly explain the novel framework on which it is built. We illustrate the building of the configuration information, required to generate the maintenance reports that are deemed essential for continuous improvement under CBM, using a markup language called XML. We explain the procedure in generating the reports using the developed prototype. We demonstrate that the developed prototype has the functional capabilities essential to implement CBM on any aircraft and is valuable in cutting down the software maintenance costs as it can perform new operations without having to modify the existing source codes.     

集成状态监测和故障诊断的设备管理平台的建立

针对目前设备状态维修中故障诊断和维修管理相互脱节的问题，在状态维修思想的指导下，构建了实施状态维修的设备管理平台，给出了系统平台的集成架构。结合发电企业设备维修的实际，提出了状态维修实施的信息模型。运用IDEF方法对整个设备管理平台的总体功能进行了详细分析，建立了系统的功能模型。在构建系统模型的基础上，综合采用网络技术、计算机技术和数据库技术等开发出了集成状态监测和故障诊断的设备管理系统，为企业成功开展状态维修提供了有效的管理平台。     

大功率液力机械变速器状态监测与故障诊断系统

大功率液力机械器是重型特种车辆传动系统的关键核心部件,为满足整车高速、重载和高任务可靠性的使用要求,一方面需要提升液力机械变速器自身的强度和可靠性,另一方面要建立和完善大功率液力机械变速器的状态监测和故障诊断系统;基于专家系统和典型零部件的失效物理模型,针对液力机械变速箱系统和关键零部件的常见失效形式,建立了大功率液力机械变速器状态监测和故障诊断系统,分为变矩与闭锁功能监测及报警模块、换挡提示及报警模块、油位监测及报警模块、系统油压监测及报警模块和油温监测及报警模块共五大部分;通过实车测试证明,大功率液力机械变速器状态监测和故障诊断系统能够较为有效的识别系统的潜在失效和使用风险,或在出现严重故障前,通过简单的维护和保养,避免严重故障的发生,从而减少人力和零备件的需求,降低保障费用。     

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.     

Condition based maintenance optimization considering multiple objectives

In condition based maintenance (CBM) optimization, the main optimization objectives include maximizing reliability and minimizing maintenance costs, which are often times conflicting to each other. In this work, we develop a physical programming based approach to deal with the multi-objective condition based maintenance optimization problem. Physical programming presents two major advantages: (1) it is an efficient approach to capture the decision makers’ preferences on the objectives by eliminating the iterative process of adjusting the weights of the objectives, and (2) it is easy to use in that decision makers just need to specify physically meaningful boundaries for the objectives. The maintenance cost and reliability objectives are calculated based on proportional hazards model and a control limit CBM replacement policy. With the proposed approach, the decision maker can systematically and efficiently make good tradeoff between the cost objective and reliability objective. An example is used to illustrate the proposed approach.     

An intelligent condition-based maintenance platform for rotating machinery

Maintenance is of necessity for sustaining machinery availability and reliability in order to ensure productivity, product quality, on-time delivery, and safe working environment. The costly maintenance strategies such as corrective maintenance and scheduled maintenance have been progressively replaced by superior maintenance strategies in which condition-based maintenance (CBM) is one of the delegates. This strategy commonly consists of sequent modules such as data acquisition, signal processing, feature extraction and feature selection, condition monitoring, etc. However, approaches in literature which have been developed for each module and implemented for different applications are standalone instead of a comprehensive system. Furthermore, these approaches have been demonstrated in a laboratory environment without any industrial validations. For these reasons, an intelligent algorithm based CBM platform is proposed in this paper to be applied for rotating machinery easily and effectively. Subsequently, two case-studies are presented in order to evaluate the effectiveness of this platform in industrial applications.     

Simultaneous optimization of repair and control-limit policy in condition-based maintenance

In condition-based maintenance (CBM) planning, collected information from system condition monitoring is the basis of making decision about conducting the maintenance and repair activities. Recently, ample number of studies has been conducted in CBM field especially, in control-limit policy. In control-limit policy, using proportional Hazards model and results of monitoring system condition, one can estimate hazard rate function and its condition’s transition probability matrix. Then, considering replacement costs, optimal control-limit can be determined minimizing the average cost in the long run. The presented model considers repair policy and their implementation cost, and the assumptions of repair during interval inspection is ignored. Then, a model is presented to determine the optimal control-limit and the best repair policy, in which the average total cost per unit time in the long-run, is minimized. At the end, a numerical example is illustrated.     

武器装备基于状态的维修系统设计

为了减少武器装备的故障以及维修时间,提高武器装备的可用度和重要部件的使用寿命,采用基于状态的维修技术与方法已成为当前维修领域研究与应用的热点.从武器装备的维修需求与技术出发,分析了现行装备维修的主要方式及其优缺点,总结了当前基于状态的维修(CBM)研究与应用现状,在此基础上提出了武器装备基于案例的CBM系统框架,给出了CBM适用的条件,并对CBM系统的核心模块进行了分析,给出了CBM系统工作的流程.最后,结合某装备中一齿轮箱的状态检测信息,进行了基于声音的装备故障诊断与案例分析与决策.分析结果表明:基于案例的CBM系统简单实用,能够满足装备维修需求.     

相依多模失效下冷贮备系统最优切换及视情维护决策

针对存在冲击影响的冷贮备系统,研究其最优切换及视情维护决策问题.首先,在系统结构和切换式运行和维护特性分析的基础上,制定基于周期切换和状态检测的切换式离线视情维护策略;其次,建立累积冲击过程影响下系统退化所致的软失效和极端冲击过程所致的硬失效竞争可靠性模型;再次,通过分析两类冲击过程影响下系统运行与备用设备交替使用、维修过程中的状态转移特性,重点推导各检测周期时刻系统状态概率分布的迭代计算模型;然后,以系统平均费用率最小为目标,建立解析决策模型,以求解系统的最优切换周期和维护阈值.最后,以矿井主通风系统为案例验证策略及模型的有效性,并分析模型对参数的灵敏度.结果表明,系统的最优维修策略随机冲击影响的不同而变化显著.     

Equipment PHM using non-stationary segmental hidden semi-Markov model

Health monitoring and prognostics of equipment is a basic requirement for condition-based maintenance (CBM) in many application domains where safety, reliability, and availability of the systems are considered mission critical. As a key complement to CBM, prognostics and health management (PHM) is an approach to system life-cycle support that seeks to reduce/eliminate inspections and time-based maintenance through accurate monitoring, incipient faults. Conducting successful prognosis, however, is more difficult than conducting fault diagnosis. A much broader range of asset health related data, especially those related to the failures, shall be collected. The asset health progression can then be possibly extracted from the congregated data, which has proved to be very challenging. This paper presents a non-stationary segmental hidden semi-Markov model (NSHSMM) based prognosis method to predict equipment health. Unlike previous HSMMs, the proposed NSHSMM no longer assumes that the state-dependent transition probabilities keep the same value all the time. That is, the probability of transiting to a less healthy state does not increase with the age. “Non-stationary” means the transition probabilities will change with time. In the proposed method, in order to characterize a deteriorating equipment, three kinds of aging factor that discount the probabilities of staying at current state while increasing the probabilities of transitions to less healthy states are introduced. The performances of these aging factors are compared by using historical data colleted from three hydraulic pumps. The hazard function (h.f.) has been introduced to analyze the distribution of lifetime with a combination of historical failure data and on-line condition monitoring data. Using h.f., PHM is based on a failure rate that is a function of both the equipment age and the equipment conditions. The state values of the equipment condition considered in PHM, however, are limited to those stochastically increasing over time and those having non-decreasing effect on the hazard rate. The estimated state duration probability distributions can be used to predict the remaining useful life of the systems. With the equipment PHM, the behavior of the equipment condition can be predicted.     

Model-based and Fuzzy Logic Approaches to Condition Monitoring of Operational Wind Turbines

 It is common for wind turbines to be installed in remote locations on land or offshore, leading to difficulties in routine inspection and maintenance. Further, wind turbines in these locations are often subject to harsh operating conditions. These challenges mean there is a requirement for a high degree of maintenance. The data generated by monitoring systems can be used to obtain models of wind turbines operating under different conditions, and hence predict output signals based on known inputs. A model-based condition monitoring system can be implemented by comparing output data obtained from operational turbines with those predicted by the models, so as to detect changes that could be due to the presence of faults. This paper discusses several techniques for model-based condition monitoring systems: linear models, artificial neural networks, and state dependent parameter "pseudo" transfer functions. The models are identified using supervisory control and data acquisition (SCADA) data acquired from an operational wind firm. It is found that the multiple-input single-output state dependent parameter method outperforms both multivariate linear and artificial neural network-based approaches. Subsequently, state dependent parameter models are used to develop adaptive thresholds for critical output signals. In order to provide an early warning of a developing fault, it is necessary to interpret the amount by which the threshold is exceeded, together with the period of time over which this occurs. In this regard, a fuzzy logic-based inference system is proposed and demonstrated to be practically feasible.     

Scheduling preventive maintenance for oil pumps using generalized proportional intensities models

Percy and Alkali presented generalizations of the proportional intensities model introduced by Cox. They identified several features of these models that are particularly relevant for modelling complex repairable systems subject to preventive maintenance (PM). These include the baseline intensity, scaling factors and explanatory variables. We investigate these aspects in detail and apply the models to five sets of reliability data collected from the main pumps at oil refineries. We use likelihood methods to estimate the model parameters and compare how well the models fit the data. Our analyses suggest that a log‐linear baseline intensity function performs well and that an exponential deterministic scaling function is useful for corrective maintenance. The inclusion of explanatory variables to represent the quality of last maintenance and time since last maintenance also proves to be beneficial. We develop algorithms for simulating the reliability behaviour of a complex repairable system into the future, in order to schedule appropriate maintenance activities, identifying special cases that simplify the algebra. Applying these methods to the oil pump data, we derive recommendations for PM plans and demonstrate that adopting this strategy can lead to substantial savings.     

集成状态监测和故障诊断的设备管理系统

提出了离线和在线监测诊断相结合的状态维修策略,在状态维修思想的指导下,建立了系统的工作模型和功能模型。综合采用网络技术、计算机技术和数据库技术等开发出了集成状态监测和故障诊断的设备管理系统,为企业开展状态维修提供了有效的管理工具。     

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.     

A knowledge driven approach to aerospace condition monitoring

Aircraft operators are continually striving to reduce both the amount and the cost of aircraft maintenance. Whilst at the same time ensuring that the aircraft safety, reliability and integrity are not compromised. One solution which has seen a lot of attention is known as condition monitoring. The aim of condition monitoring is to develop the ability to detect, diagnose and locate damage, even predicting the remaining useful life of the structure or system. There are difficulties associated with developing aerospace condition monitoring which transcends technical, financial and regulatory. Aerospace legislation requires that any decisions on maintenance, safety and flightworthiness to be auditable and data patterns to relate to known information. The use of data, physical models and knowledge approaches can individually produce reliable health related decisions, but the fusing of these different solutions within an appropriate framework will enhance the intelligence in the decision making process. This paper reviews such a framework and design methodology being used for the development of knowledge based condition monitoring systems for aircraft landing gear actuators.     

An approach using random forest intelligent algorithm to construct a monitoring model for dam safety

The mechanism of dam safety monitoring model is analyzed; for the dam system comprehensive affected by multi-factor, the mapping relationship between the influence factors and the dam behavior effects domain is usually nonlinear. Synthesizing each kind of factor, 27 parameters are chosen as the main factors which affect the accuracy of the monitoring model. Taking the actual monitoring data as the evaluation factor, the dam safety monitoring model based on the random forest (RF) intelligent algorithm was built with the actual monitoring data to predict uplift pressure. At the same time, test the significance of each variable based on the RF monitoring model and calculate the importance degree of each variable for the model through the importance function. It is indicated that RF model can be relatively fast and accurately predict the uplift pressure of the dam according to the influence factors. The average prediction accuracy is more than 95%. As compared with other intelligent algorithms such as support vector machine, RF has better robustness, higher prediction accuracy, and faster convergence speed. Because of the uniformity of the calculation procedure and the universality of the prediction method, the RF model also has reasonable extrapolation for other dam safety monitoring models (such as crack opening and seepage discharge). Significance test results obtained by the two methods have shown that the impact of reservoir water level and daily rainfall on the uplift pressure is significant, and other factors’ impact on dam deformation is unstable and changes with the external environmental influence.

     

三模冗余系统的可靠性与安全性分析

轨道交通运行控制系统对安全计算机平台的可靠性及安全性要求很高。为此,采用马尔可夫模型,在考虑故障检测率、维修率及共模故障影响的情况下,通过Matlab仿真分析故障检测率及维修率对安全计算机平台可靠性与安全性的影响。结果表明,基于三模冗余的安全计算机平台具有较高的可靠性和安全性。     

基于BP神经网络的PMS电流互感器设备状况评价系统

电流互感器作为变电站重要设备,其运行工况的好坏直接影响变电站的安全运行,电流互感器数量多,在运行中也经常会遇见电流互感器各种各样的缺陷,比如发热、漏油、低油位等。通过对PMS上电流互感器这个庞大的数据,单因素图表法分析电流互感器故障发生与其设备型号、设备生产厂家、设备投运时间之间的关系,多因素联合考虑,建立BP神经网络模型,综合考虑设备型号、设备生产厂家、设备投运时间因素,对其运行工况进行概率预测,同时对每个变电站符合模型要求的所有电流互感器进行预测,对容易发生电流互感器故障的变电站进行预警,运用地图无忧软件对BP模型计算的结果进行可视化展示,方便运维人员掌握电流互感器运行工况,对容易发生故障的电流互感器加强带电检测,提前安排检修,保障供电可靠性。     

加速冲击损伤退化系统剩余寿命预测及预测维修决策

随着检测传感技术的发展,诸如风力发电机叶片等可对其状态进行检测,并依据检测结果进行剩余寿命预测.但此类系统在运行中受环境冲击影响较大,如何对冲击影响下的系统剩余寿命进行预测,并结合预测结果进行经济可靠的维修决策是一个值得研究的问题.对此,针对状态可检测的连续退化系统,研究考虑加速冲击损伤特性下的系统剩余寿命预测及基于预测的维修决策.首先,考虑自然退化和与退化相关的冲击损伤,构建加速冲击损伤退化模型和剩余寿命预测模型;其次,制定基于周期检测的状态维修与预测维修相结合的混合维修策略,并推导不同维修活动的发生概率;然后,构建以长期平均费用率最小为目标,以检测间隔和故障率阈值为决策变量的决策模型,并给出了优化解法;最后,以风力发电机叶片为案例验证模型的适用性和有效性,对系统的参数进行灵敏度分析,并与未考虑加速冲击损伤和未考虑预测的维修决策结果进行对比分析.