Condition-based Maintenance Optimization for Gamma Deteriorating Systems under Performance-based Contracting

With the further development of service-oriented,performance-based contracting(PBC)has been widely adopted in industry and manufacturing.However,maintenance optimization problems under PBC have not received enough attention.To further extend the scope of PBC’s application in the field of maintenance optimization,we investigate the condition-based maintenance(CBM)optimization for gamma deteriorating systems under PBC.Considering the repairable single-component system subject to the gamma degradation process,this paper proposes a CBM optimization model to maximize the profit and improve system performance at a relatively low cost under PBC.In the proposed CBM model,the first inspection interval has been considered in order to reduce the inspection frequency and the cost rate.Then,a particle swarm algorithm(PSO)and related solution procedure are presented to solve the multiple decision variables in our proposed model.In the end,a numerical example is provided so as to demonstrate the superiority of the presented model.By comparing the proposed policy with the conventional ones,the superiority of our proposed policy is proved,which can bring more profits to providers and improve performance.Sensitivity analysis is conducted in order to research the effect of corrective maintenance cost and time required for corrective maintenance on optimization policy.A comparative study is given to illustrate the necessity of distinguishing the first inspection interval or not.

CONDITION-BASED MAINTENANCE DECISION MAKING METHODS FOR CIVIL AEROENGINE

To provide some feasible condition-based maintenance （CBM） decision making methods for civil aeroengine, firstly, the theory of aeroengine CBM decision making is described. The proportional intensity（PI） model is established based on the reliability and condition monitoring data. According to the model, the decision making methods are proposed for the optimal preventive maintenance（PM） interval and removal. Then, the time on wing （TOW） is predicted by collecting actual data based on the engine age and operating conditions. Finally, an example of a fleet for CF6-80C2 engines is illustrated. It shows that sufficient engine operation data are the key of accurate decision making. Results indicate that the CBM decision making methods are helpful for engineers in airlines to control engine maintenance actions and TOW, thus decreasing risks and maintenance costs.

Condition-based maintenance policy for gamma deteriorating systems

A condition-based maintenance model for gamma deteriorating system under continuous inspection is studied. This methodology uses a gamma distribution to model the material degradation, and the impact of imperfect maintenance actions on the system reliability is investigated. The state of a degrading system immediately after the imperfect maintenance action is assumed as a random variable and the maintenance time follows a geometric process. Furthermore, the explicit expressions for the long-run average cost and availability per unit time of the system are evaluated, an optimal policy （ε^＊） could be determined numeri- cally or analytically according to the optimization model. At last, a numerical example for a degrading system modeled by a gamma process is presented to demonstrate the use of this policy in practical applications.

Review of Condition-Based Maintenance Strategies for Offshore Wind Energy

The existing maintenance strategies of offshore wind energy are reviewed including the specific aspects of condition-based maintenance, focusing on three primary phases, namely, condition monitoring, fault diagnosis and prognosis, and maintenance optimization. Relevant academic research and industrial applications are identified and summarized. The state of art, capabilities,and constraints of condition-based maintenance are analyzed. The presented research demonstrates that the intelligent-based approach has become a promising solution for condition recognition, and an integrated data platform for offshore wind farms is significant to optimize the maintenance activities.

Condition-based maintenance optimization for continuously monitored degrading systems under imperfect maintenance actions

Condition-based maintenance(CBM) is receiving increasing attention in various engineering systems because of its effectiveness. This paper formulates a new CBM optimization problem for continuously monitored degrading systems considering imperfect maintenance actions. In terms of maintenance actions,in practice, they scarcely restore the system to an as-good-as new state due to residual damage. According to up-to-data researches, imperfect maintenance actions are likely to speed up the degradation process. Regarding the developed CBM optimization strategy, it can balance the maintenance cost and the availability by the searching the optimal preventive maintenance threshold.The maximum number of maintenance is also considered, which is regarded as an availability constraint in the CBM optimization problem. A numerical example is introduced, and experimental results can demonstrate the novelty, feasibility and flexibility of the proposed CBM optimization strategy.

Condition-Based Maintenance Decision-Making for Equipment

At present, condition monitoring and fault diagnosis technology and their application in engineering have been widely studied. Relatively little attention has been paid to condition-based maintenance decision-making for equipment. In this paper,based on the decision-making policy in traditional condition-based maintenance,the connotation of condition-based maintenance for equipment was defined, and its characteristics were analyzed.Working contents of condition-based maintenance for equipment were provided,which were divided into three stages. The influence factors in condition-based maintenance for equipment were analyzed. The key links of equipment maintenance contents and decision-making process were proposed. The condition-based maintenance decision-making policy presented in this paper can provide a practical reference for equipment maintenance.

Joint optimization of sampling interval and control for condition-based maintenance using availability maximization criterion

Most of the maintenance optimization models in condition-based maintenance(CBM) consider the cost-optimal criterion, but few papers have dealt with availability maximization for maintenance applications. A novel optimal Bayesian control approach is presented for maintenance decision making. The system deterioration evolves as a three-state continuous time hidden semi-Markov process. Considering the optimal maintenance policy, the multivariate Bayesian control scheme based on the hidden semi-Markov model(HSMM) is developed, the objective is to maximize the long-run expected average availability per unit time. The proposed approach can optimize the sampling interval and control limit jointly. A case study using Markov chain Monte Carlo(MCMC)simulation is provided and a comparison with the Bayesian control scheme based on hidden Markov model(HMM), the age-based replacement policy, Hotelling’s T2, multivariate exponentially weihted moving average(MEWMA) and multivariate cumulative sum(MCUSUM) control charts is given, which illustrates the effectiveness of the proposed method.

Maintenance and Condition-Based Maintenance

The evolution of maintenance management is briefly introduced in this paper, from corrective maintenance to preventive maintenance. First, a range of condition monitoring and fault diagnosis techniques developed in different industries are surveyed; Second, many methods of condition monitoring are presented; Third, mathematical methods used in condition monitoring are given; Then the merits and shortcomings are discussed. Efficient maintenance policies are of fundamental importance in system engineering because of their fallbacks into the safety and economics of plant operation. Applying condition-based maintenance to a system can reduce the cost and extend the availability of facilities. With the advent of personal computers as fast and cost effective machines for data acquisition and processing of multiple signals some shortcomings mentioned in condition monitoring could be solved or reduced to some extent. These PCs can be a solution as a condition monitoring based maintenance system.

Overview of the Importance of Intelligent Approaches on Machinery Faults Diagnosis and Prediction Based on Prognostic and Health Management/Condition-Based Maintenance

Condition monitoring is increasingly used to anticipate and detect failures of industrial machines.Failures of machines can cause high maintenance or replacement costs.If neglected,it may result in catastrophic accidents leading to production shrinkage.The potential failure would negatively affect the profitability of the company,including production shut down,cost of spare parts,cost of labor,damage of reputation,risk of injury to people and the environment.In recent years,condition-based maintenance( CBM) and prognostic and health management( PHM) are developed and formed a strong connection among science,engineering,computer,reliability,communication,management,etc.Computerized maintenance management systems( CMMS) store a lot of data regarding the fault diagnosis and life prediction of the machinery equipment.It's too necessary to uncover useful knowledge from the huge amount of data.It's vital to find the ways to obtain useful and concise information from these data.This information can be of great influence in the decision making of managers.This article is a review of intelligent approaches in machinery faults diagnosis and prediction based on PHM and CBM.

Overview of Condition-Based Maintenance Decision-Making Optimization Modeling of Multi-component System

Overview about three key contents of condition-based maintenance decision-making of a multi-component system is analyzed based on maintenance optimization and modeling. The component deterioration model, the correlation between the degraded components and the system configuration are analyzed separately in the deterioration model of multi-component system.For the maintenance polices,the opportunistic maintenance( OM)policy and the grouping maintenance( GM) policy are analyzed and summarized in combination with the condition-based maintenance( CBM) modeling of multi-component system. It is put forward that CBM modeling of multi-component system should be further researched based on the inspection interval and the maintenance threshold of multi-component system in availability.

Analysis of Condition-Based Maintenance for High-Valtage Electric Power

The development process of high-voltage electric power equipment maintenance was introduced. It is pointed out that the trend of high-voltage electric power equipment maintenance is so called condition-based maintenance. With the development of computer technology and sensors, on-line monitoring of high-voltage electric power equipment has developed rapidly. By introducing the main principle of Schering bridge to measure tanδ, the way of on-line monitoring of high-voltage electric power equipment was explained. Difference methods of on-line monitoring of insulation parameters for 35 kV substation were discussed. Finally, the shortcomings as well as its tendency of on-line monitoring were analyzed.

Role of Surgery on Growth of Tricuspid Valve in Pulmonary Atresia with Intact Ventricular Septum:Mid-Term Results of Modified Right-Ventricular Overhauling Procedure

Objectives:To access the effectiveness of our modified right-ventricular overhauling procedure on tricuspid valve(TV)growth in patients with pulmonary atresia with intact ventricular septum(PAIVS).Methods:We retrospectively reviewed 21 patients with PAIVS who underwent modified right ventricular overhauling(mRVoh)between 2008 and 2019 at two institutions.Our mRVoh consisted of wide resection of hypertrophied infundibular and trabecular muscle,peeling off fibrotic endocardial tissue in the right ventricle(RV)cavity,surgical pulmonary valvotomy,and Blalock-Taussig shunt or banding of ductus arteriosus under cardiopulmonary bypass.The TV annulus sizes were measured and analyzed using echocardiography before and after mRVoh.Results:No mortalities were observed during a median follow-up of 3 years(interquartile range:1.3–4.7 years)of follow-up were noted.mRVoh was performed at a median age of 163.5 days(range:21–560 days),including seven neonates and two infants(<60 days).During follow-up,the median TV annular z-score increased significantly from−2.24 to−1.15 before and after mRVoh(p=0.004).In ten patients with a prior history of percutaneous interventions for RV outflow tract(RVOT)widening at least 6 months before mRVoh,the TV annular z-score significantly changed during the period after mRVoh(−2.03 to−1.61,p=0.028)compared with the period before mRVoh(−2.51→–2.03,p=0.575)after percutaneous intervention only.Conclusions:mRVoh in PAIVS patients was positively associated with TV annular growth,and it was more effective than percutaneous RVOT widening interventions without mRVoh.

不确定环境下考虑多技能人力资源的复杂产品MRO服务资源调度

针对复杂产品维护、维修、大修(MRO)服务资源调度中存在人力资源多技能异质而导致任务实际执行工期随着指派方案的不同而变化的问题,将多技能工调度与人员学习效应相结合,建立“任务技能人员”的匹配关系。以最小化服务完成时间、最小化人员冗余和最大化资源性能成本比为目标,构建资源调度时间参数不确定条件下的随机机会约束规划数学模型。并提出一种基于随机模拟、BP神经网络和改进NSGA-II的混合智能算法进行求解。通过具有不同参数特征的对比实验案例和敏感性分析,验证了所提模型和算法的有效性和适用性。

国内首座“架空+上盖开发”大架修基地设计创新

研究目的:“架空+上盖开发”的大架修车辆基地综合开发空间形态在国内没有先例,需要解决车辆基地建设对城市土地和空间的割裂、地面层空间利用、提高盖上物业开发强度、减振降噪、设备基础和管线敷设等技术难题。本文通过国内首座“架空+上盖开发”大架修车辆基地设计实践,提出解决以上技术难题的工程方案,供业内同行参考借鉴。研究结论:(1)架空方案能够有效减轻车辆基地建设对城市土地和空间的割裂;(2)通过盖上物业核心筒结构落地,将上盖物业车库置换到地面层,可高效利用地面层空间,提高物业开发强度和收益;(3)在咽喉区、库内检查坑及库外线路大面积采用橡胶弹簧浮置板减振道床等措施可以较好地解决车辆基地振动噪声问题;(4)利用架空板下沉和地面层局部空间,可以解决设备基础和管线敷设难题;(5)本研究成果提出的思路和方法对类似工程建设有参考和借鉴价值。

高水头电站水轮机泥沙磨损评估及抗磨蚀综合措施

对于高水头电站,泥沙磨损的影响在水轮机的设计和参数选择中是一个不可忽视的问题。以科哈拉水电站为例,根据电站的泥沙特性,对水轮机的泥沙磨损进行试验和评估。从水轮机参数选择、水力设计、结构及工艺、材料及表面防护、调度运行等方面提出了针对性的抗泥沙磨蚀综合措施。研究内容可为其他高水头电站水轮机抗磨蚀方案的设计提供参考。

汽车空调的维护与机械故障检修思考

伴随着社会经济与科技的快速发展进步,汽车已成为人们日常生活中必备的一种交通工具,其技术创新能力也显示出不断完善的趋向。然而,同时随之而来的是对汽车机械故障的关注度不断提高。故障的产生原因众多,一般可以依据其各自的特性进行归类。因此,必须掌握汽车空调维护以及机械故障的产生原因,再结合相对应的维修方法进行修理。基于此,本文将针对上述问题展开探究分析,以促使人们更好地了解汽车空调的相关知识的维修要点。

城市轨道交通车辆检修维护技术应用研究

随着城市轨道交通的快速发展,轨道交通车辆的检修维护技术也变得越来越重要。城市轨道交通系统是现代城市交通的重要组成部分,它不仅能够提供高效、便捷的出行方式,还能够减少交通拥堵和环境污染。然而,随着轨道交通车辆的运营时间的增长,车辆的磨损和故障也逐渐增多,给车辆的安全运营带来了一定的风险。因此,对轨道交通车辆进行定期的检修维护,保证车辆的安全性和可靠性,成为了城市轨道交通运营管理的重要环节。文章主要对城市轨道交通车辆检修维护技术的应用进行探讨,从而提出有效的维护策略和方法。

右心室Overhaul技术在复杂先心病合并右心室发育不良手术中的运用

目的总结右心室Overhaul技术在治疗伴右心室发育不良的复杂先天性心脏病中的运用经验,探讨术中操作要点,评估其近中期疗效。方法回顾性分析我科2006年1月至2021年4月间采用右心室Overhaul技术治疗的合并有右心室发育不良的22例先天性心脏病患者的临床资料,男14例,女8例,其中室间隔完整的肺动脉闭锁15例,房室间隔缺损伴动脉导管未闭2例,三尖瓣下移畸形伴房间隔缺损3例,三尖瓣发育不良伴动脉导管未闭及房间隔缺损2例。所有患者术前均经超声心动图明确具有右心室发育不良。术中使用右心室Overhaul技术扩大右心室腔,并同期处理部分合并的心脏畸形。结果全组患者均顺利完成右心室Overhaul手术及部分合并畸形的矫治,围术期无死亡病例。术后呼吸极辅助通气时间(16.9±13.5)h,术后住院时间(7.2±2.8)d;术后/术前右心室容积比为(2.3±0.9)倍。随访3个月~15年,2例患者分别随访2年及4年后因右心室腔小、明显发绀,再次行右心室Overhaul手术,14例患者随访右心室发育情况良好,再行根治手术。6例患者仍在随访中。所有患者首次手术后末梢氧饱和度及活动耐力较术前明显提高,患者心功能Ⅰ~Ⅱ级,活动耐量良好。结论右心室Overhaul技术可安全用于部分具有双心室结构,但右心室发育不良无法一期行根治手术的复杂先天性心脏病患者,可有效提高末梢氧饱和度、改善患者活动能力,并为可能的双心室根治手术创造条件。

炼化企业停工大检修污水水质特性综合表征

炼化企业停工大检修期间无组织排放大量高浓度污水,对综合污水处理厂的正常运行构成严重冲击。将宏观污染指标与微观污染组成相结合,从污染负荷、体系稳定性、污染组成及生物毒性4方面,对清洗污水和清洗-钝化混合污水两类典型大检修污水进行水质特性综合分析。清洗污水和清洗-钝化混合污水均表现出极高的有机污染负荷、悬浮态和胶体态污染物、体系稳定性以及较强的急性生物毒性。清洗污水和清洗-钝化混合污水的有机污染组成复杂,两者的极性污染物组成均以OxS1类为主,前者以O3S1类污染物相对丰度最高,推测源自磺酸盐类表面活性物质;后者以O4S1类污染物相对丰度最高,推测源自烷基硫酸盐类或硫酸酯盐类表面活性物质。两者的弱极性污染物组成差异较大,清洗污水以醇醚类、醚类和石油烃类为主,而清洗-钝化混合污水则主要是小分子杂环类、芳香烃类和酚类。对两类典型大检修污水水质特性的分析研究可指导大检修污水预处理工艺开发,为炼化企业大检修期间综合污水处理厂的稳定运行提供支持。

基于特征选择的风机检修流程预测性监控方法

针对风机检修业务流程中存在的操作失误和工作延期等问题,应用业务流程预测性监控方法,预测业务的下一事件、下一事件执行时间和剩余时间,以提醒工作人员预防和避免风险的发生。首先,针对不同预测任务,提出一种基于优先级的特征自选取策略,并使用LightGBM(Light Gradient Boosting Machine)算法作为特征选择策略的依托预测模型,得到对预测结果有积极影响的输入特征;然后,针对不同预测任务分别采用LightGBM算法和LSTM(Long Short Term Memory)神经网络构建预测模型;最后,经实验评估和分析,在风机检修业务流程中,特征选择策略能够为不同的预测任务提供有效特征,确保预测的准确率,具有实际应用价值。对于不同预测任务而言,LightGBM算法更适用于下一事件任务预测,LSTM模型更适用于时间方面的任务预测。