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 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.

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 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.

Proactive Approach for Production and Condition-Based Maintenance Integration Problem in a Deteriorating System

A proactive approach is constructed to cope with the integrated problem of batch production and maintenance in a deteriorating system. The condition of the system is modeled by a proportional hazards model(PHM) which considers both system deterioration state and usage. The deterioration state of system is uncertain and is only observed between batches. An integration model for optimizing production plan and conditionbased maintenance(CBM) policy is proposed, in which the maintenance threshold and production quantity are proactively decided simultaneously. To obtain a robust solution with minimal cost over the planning horizon, a simulation-based iterative algorithm is developed to solve the complicated non-linear model. Numerical results show that the performance of the developed approach is satisfactory under uncertainty.

Condition-Based Spare Ordering Model for a Two-Stage Degrading System

Proper supply of spares is critical to guarantee safe operation,improve service quality and reduce maintenance costs.This paper proposes a condition-based spare ordering model for a two-stage degrading system,which consists of inflection point transfer process and two-stage degradation process with continuous degradation process and random external shocks.External shocks itself does not directly lead to system failure,but it will speed up the degradation process.In turn,degradation can also make the system more vulnerable to shocks.In general,the degradation rate at the defective stage is greater than that at the normal stage.The proposed model depends on system degradation process and spare lead-time.In order to achieve accurate maintenance and deal with emergency maintenance caused by system rapid degradation after inflection point transfer time,the model considers both the regular lead-time and expedited lead-time.Before inflection point transfer time,regular spare ordering policy is performed.After inflection point transfer time,expedited spare ordering policy is implemented.The decision variable of the model is the ordering time.The objective of this study is to determine the optimal ordering time such that the expected cost rate is minimized.Finally,a numerical example is presented to illustrate the proposed model and sensitivity analysis on critical parameters is carried out.

A review of condition-based maintenance: Its prognostic and operational aspects

Condition-based maintenance(CBM)detects early signs of failure and dictates when maintenance should be performed based on the actual condition of a system.In this paper,we first review some of the recent research on CBM under various physical structures and signal data.Then,we summarize several kinds of prognostic models that use monitoring information to estimate the reliability of complex systems or products.Monitoring information also facilitates operational decisions in production planning,spare parts management,reliability improvement,and prognostics and health management.Finally,we suggest some research opportunities for the reliability and operations management communities to fill the research gap between these two fields.

Research on Carbon Emission for Preventive Maintenance of Wind Turbine Gearbox Based on Stochastic Differential Equation

Time based maintenance(TBM)and condition based maintenance(CBM)are widely applied in many large wind farms to optimize the maintenance issues of wind turbine gearboxes,however,these maintenance strategies do not take into account environmental benefits during full life cycle such as carbon emissions issues.Hence,this article proposes a carbon emissions computing model for preventive maintenance activities of wind turbine gearboxes to solve the issue.Based on the change of the gearbox state during operation and the influence of external random factors on the gearbox state,a stochastic differential equation model(SDE)and corresponding carbon emission model are established,wherein SDE is applied to model the evolution of the device state,whereas carbon emission is used to implement carbon emissions computing.The simulation results indicate that the proposed preventive maintenance cannot ensure reliable operation of wind turbine gearboxes but reduce carbon emissions during their lifespan.Compared with TBM,CBM minimizes unit carbon emissions without influencing reliable operation,making it an effective maintenance method.

A Dynamic Maintenance Strategy for Multi-Component Systems Using a Genetic Algorithm

In multi-component systems,the components are dependent,rather than degenerating independently,leading to changes inmaintenance schedules.In this situation,this study proposes a grouping dynamicmaintenance strategy.Considering the structure of multi-component systems,the maintenance strategy is determined according to the importance of the components.The strategy can minimize the expected depreciation cost of the system and divide the system into optimal groups that meet economic requirements.First,multi-component models are grouped.Then,a failure probability model of multi-component systems is established.The maintenance parameters in each maintenance cycle are updated according to the failure probability of the components.Second,the component importance indicator is introduced into the grouping model,and the optimization model,which aimed at a maximum economic profit,is established.A genetic algorithm is used to solve the non-deterministic polynomial(NP)-complete problem in the optimization model,and the optimal grouping is obtained through the initial grouping determined by random allocation.An 11-component series and parallel system is used to illustrate the effectiveness of the proposed strategy,and the influence of the system structure and the parameters on the maintenance strategy is discussed.

Review:Measurement-Based Monitoring and Fault Identification in Centrifugal Pumps

Condition based maintenance(CBM) is one of the solutions to machinery maintenance requirements. Latest approaches to CBM aim at reducing human engagement in the real-time fault detection and decision making. Machine learning techniques like fuzzy-logic-based systems, neural networks, and support vector machines help to reduce human involvement. Most of these techniques provide fault information with 100% confidence. It is undeniably apparent that this area has a vast application scope. To facilitate future exploration, this review is presented describing the centrifugal pump faults, the signals they generate, their CBM based diagnostic schemes, and case studies for blockage and cavitation fault detection in centrifugal pump(CP) by performing the experiment on test rig. The classification accuracy is above 98% for fault detection. This review gives a head-start to new researchers in this field and identifies the un-touched areas pertaining to CP fault diagnosis.

Development of Maintenance Management Strategy Based on Reliability Centered Maintenance for Marginal Oilfield Production Facilities

The present work adopted Reliability Centered Maintenance (RCM) methodology to evaluate marginal oilfield Early Production Facility (EPF) system to properly understand its functional failures and to develop an efficient maintenance strategy for the system. The outcome of the RCM conducted for a typical EPF within the Niger Delta zone of Nigeria provides an indication of equipment whose failure can significantly affect operations at the production facility. These include the steam generation unit and the wellhead choke assembly, using a risk-based failure Criticality Analysis. Failure Mode and Effect Analysis (FMEA) was conducted for the identified critical equipment on a component basis. Each component of the equipment was analyzed to identify the failure modes, causes and the effect of the failure. The outcome of the FMEA analysis aided the development of a robust maintenance management strategy, which is based on an optimized mix of corrective, preventive and condition-based monitoring maintenance for the marginal oilfield EPF.

“看得见的手”应该如何引导企业?──关于国企改革中政府行为的不同观点

“看得见的手”应该如何引导企业？──关于国企改革中政府行为的不同观点毛寿龙政府与企业的关系问题，尤其是政府与国有企业的关系问题，始终是经济体制改革中的一个难点。随着社会主义市场经济的逐步确立和国有企业问题的日渐突出，正确处理政府与企业的关系，＂规范政...

基于条件的近岸风能维修策略综述（英文）

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.

Research on Hidden Failure Reliability Modeling of Electric Power System Protection

Aiming at digital relay protection system, a novel hidden failure Markov reliability model is presented for a single main protection and double main protection systems according to hidden failure and protection function under Condition-Based Maintenance (CBM) circumstance and reliability indices such as probability of protection system hidden failure state are calculated. Impacts of different parameters (containing impacts of human errors) to hidden failure state probability and the optimal measures to improve reliability by variable parameter method are also analyzed. It’s demonstrated here that: Compared to a single main protection, double main protection system has an increased hidden failure probability, thus the real good state probability decreases, two main protections’ reliability must be improved at the same time, so configuration of the whole protection system for the component being protected can’t be complicated. Through improving means of on-line self-checking and monitoring system in digital protection system and human reliability, the real application of CBM can decrease hidden failure state probability. Only through this way can we assure that the protection systems work in good state. It has a certain reference value to protection system reliability engineering.

Improvement of railway performance： a study of Swedish railway infrastructure

The volume of rail traffic was increased by 5 % from 2006 to 2010, in Sweden, due to increased goods and passenger traffic. This increased traffic, in turn, has led to a more rapid degradation of the railway track, which has resulted in higher maintenance costs. In general, degradation affects comfort, safety, and track quality, as well as, reliability, availability, speed, and overall railway performance. This case study investigated the needs of railway stakeholders responsible for analysing the track state and what information is necessary to make good maintenance decisions. The goal is to improve the railway track per- formance by ensuring increased availability, reliability, and safety, along with a decreased maintenance cost. Inter- views of eight experts were undertaken to learn of general areas in need of improvement, and a quantitative analysis of condition monitoring data was conducted to find more specific information. The results show that by implement- ing a long-term maintenance strategy and by conducting preventive maintenance actions maintenance costs would be reduced. In addition to that, problems with measured data, missing data, and incorrect location data resulted in increased and unnecessary maintenance tasks. The conclusions show that proactive solutions are needed to reach the desired goals of improved safety, improved availability, and improved reliability. This also includes thedevelopment of a visualisation tool and a life cycle cost model for maintenance strategies.

Structural Health Monitoring of Offshore Buoyant Leg Storage and Regasification Platform:Experimental Investigations

Offshore platforms are of high strategic importance,whose preventive maintenance is on top priority.Buoyant Leg Storage and Regasification Platforms(BLSRP)are special of its kind as they handle LNG storage and processing,which are highly hazardous.Implementation of structural health monitoring(SHM)to offshore platforms ensures safe operability and structural integrity.Prospective damages on the offshore platforms under rare events can be readily identified by deploying dense array of sensors.A novel scheme of deploying wireless sensor network is experimentally investigated on an offshore BLSRP,including postulated failure modes that arise from tether failure.Response of the scaled model under wave loads is acquired by both wired and wireless sensors to validate the proposed scheme.Proposed wireless sensor network is used to trigger alert monitoring to communicate the unwarranted response of the deck and buoyant legs under the postulated failure modes.SHM triggers the alert mechanisms on exceedance of the measured data with that of the preset threshold values;alert mechanisms used in the present study include email alert and message pop-up to the validated user accounts.Presented study is a prima facie of SHM application to offshore platforms,successfully demonstrated in lab scale.