Taking the project of introducing reliability-centered maintenance( RCM) into maintenance decision in an AP1000 nuclear power plant( NPP) under construction as the research object,an improved RCM methodology was propo...Taking the project of introducing reliability-centered maintenance( RCM) into maintenance decision in an AP1000 nuclear power plant( NPP) under construction as the research object,an improved RCM methodology was proposed, and the application software and an RCM-based maintenance strategies management system were designed. In the pilot project,the RCMbased maintenance decision methodology had been applied to determining the maintenance strategies for two systems. Both the decision process and the results were described in this paper. The achievements of this project promoted the introduction and routinization of an advanced and effective maintenance decision mode in nuclear power field,which could provide valuable reference for new NPPs in China.展开更多
The virtual instruments （VIs）, as a new type of instrument based on computer, has many advanced attractive characteristics. This research is based on Vls, and brings condition monitoring and knowledge-based maintena...The virtual instruments （VIs）, as a new type of instrument based on computer, has many advanced attractive characteristics. This research is based on Vls, and brings condition monitoring and knowledge-based maintenance support together through an integrated （including hate.met, ASP. NET, XML tochnique, Vls） network environme~. Within the enviromnent, machining centers operators, engineers or managers can share real-time data through the browser-based interface and minimize machining centers downtime by providing status monitoring and remote maintenance guiding from service centers.展开更多
Natural decline in various mainstream oilfield reserves and the high investment capital in upstream exploration and project development have promoted attention towards smaller oilfields referred to as Marginal fields....Natural decline in various mainstream oilfield reserves and the high investment capital in upstream exploration and project development have promoted attention towards smaller oilfields referred to as Marginal fields. This provides operators the opportunity to commence exploration and production with minimum requirements of design, installation, and operations. Although the low Capital Expenditure (CAPEX) requirement favors the start-up of marginal oilfield operations, several operators are not able to sustain the field’s operations due to the high Operational Expenditure (OPEX), particularly arising from facilities’ maintenance. The aim of this paper is to review the maintenance strategies adopted in marginal oilfields, assess their effectiveness, and provide a pointer towards efficient and viable maintenance strategies for the sustainability of marginal oilfields. The study showed that time-based preventive maintenance is predominant in the oil industry, which constitutes up to 40% of net operational expenses. In other cases, reactive maintenance is adopted, which often results in an unplanned shutdown, known to be responsible for nearly half of the overall losses of an oil facility. A paradigm shift in maintenance to Reliability Centered Maintenance (RCM) was explored for marginal oilfield, with a comprehensive review of various maintenance strategies, ranging from maintenance optimization strategies, Heuristics and Metaheuristics, Artificial Intelligence (AI), and Data Mining techniques. It was observed that the application of AI best addresses the proposed RCM for marginal oilfields. This was drawn from the recorded limitations of the other concepts from verifiable similar works, where different AI techniques and Data analytics methods have been successfully applied to aid RCM.展开更多
This paper attempts to concentrate on the eight basic principles of reliability-centeredmaintenance, and discusses respectively the differences between old and new maintenanceconcepts in eight aspects, i.e., function ...This paper attempts to concentrate on the eight basic principles of reliability-centeredmaintenance, and discusses respectively the differences between old and new maintenanceconcepts in eight aspects, i.e., function of rework at hard time, potential failure and funcrionalfailure, function of preventive maintenance, change of failure consequence, determinarion ofpreventive maintenance work and formulation of initial preventive maintenance program, andperfection of preventive maintenance program.展开更多
Fault diagnosis and prognosis in mechanical systems have been researched and developed in the last few decades at a very rapid rate. However, owing to the high complexity of machine centers, research on improving the ...Fault diagnosis and prognosis in mechanical systems have been researched and developed in the last few decades at a very rapid rate. However, owing to the high complexity of machine centers, research on improving the accuracy and reliability of fault diagnosis and prognosis via data mining remains a prominent issue in this field. This study investigates fault diagnosis and prognosis in machine centers based on data mining approaches to formulate a systematic approach and obtain knowledge for predictive maintenance in Industry 4.0 era. We introduce a system framework based on Industry 4.0 concepts, which includes the process of fault analysis and treatment for predictive maintenance in machine centers. The framework includes five modules： sensor selection and data acquisition module, data preprocessing module, data mining module, decision support module, and maintenance implementation module. Furthermore, a case study is presented to illustrate the application of the data mining methods for fault diagnosis and prognosis in machine centers as an Industry 4.0 scenario.展开更多
It is especially significant for a manufacturing company to select a proper maintenance policy because maintenance impacts not only on economy, reliability and availability but also on personnel safety. This article r...It is especially significant for a manufacturing company to select a proper maintenance policy because maintenance impacts not only on economy, reliability and availability but also on personnel safety. This article re- ports on research in the backlash error data interpretation and compensation for intelligent predictive maintenance in machine centers based on artificial neural networks （ANNs）. The backlash error, measurement system and prediction methods are analyzed in detail. The result indicates that it is possible to predict and compensate for the backlash error in both forward and backward directions in machine centers.展开更多
文摘Taking the project of introducing reliability-centered maintenance( RCM) into maintenance decision in an AP1000 nuclear power plant( NPP) under construction as the research object,an improved RCM methodology was proposed, and the application software and an RCM-based maintenance strategies management system were designed. In the pilot project,the RCMbased maintenance decision methodology had been applied to determining the maintenance strategies for two systems. Both the decision process and the results were described in this paper. The achievements of this project promoted the introduction and routinization of an advanced and effective maintenance decision mode in nuclear power field,which could provide valuable reference for new NPPs in China.
基金This work was supported by National Key Laboratory Foundation for FMS No. 51458100505JB3501
文摘The virtual instruments （VIs）, as a new type of instrument based on computer, has many advanced attractive characteristics. This research is based on Vls, and brings condition monitoring and knowledge-based maintenance support together through an integrated （including hate.met, ASP. NET, XML tochnique, Vls） network environme~. Within the enviromnent, machining centers operators, engineers or managers can share real-time data through the browser-based interface and minimize machining centers downtime by providing status monitoring and remote maintenance guiding from service centers.
文摘Natural decline in various mainstream oilfield reserves and the high investment capital in upstream exploration and project development have promoted attention towards smaller oilfields referred to as Marginal fields. This provides operators the opportunity to commence exploration and production with minimum requirements of design, installation, and operations. Although the low Capital Expenditure (CAPEX) requirement favors the start-up of marginal oilfield operations, several operators are not able to sustain the field’s operations due to the high Operational Expenditure (OPEX), particularly arising from facilities’ maintenance. The aim of this paper is to review the maintenance strategies adopted in marginal oilfields, assess their effectiveness, and provide a pointer towards efficient and viable maintenance strategies for the sustainability of marginal oilfields. The study showed that time-based preventive maintenance is predominant in the oil industry, which constitutes up to 40% of net operational expenses. In other cases, reactive maintenance is adopted, which often results in an unplanned shutdown, known to be responsible for nearly half of the overall losses of an oil facility. A paradigm shift in maintenance to Reliability Centered Maintenance (RCM) was explored for marginal oilfield, with a comprehensive review of various maintenance strategies, ranging from maintenance optimization strategies, Heuristics and Metaheuristics, Artificial Intelligence (AI), and Data Mining techniques. It was observed that the application of AI best addresses the proposed RCM for marginal oilfields. This was drawn from the recorded limitations of the other concepts from verifiable similar works, where different AI techniques and Data analytics methods have been successfully applied to aid RCM.
文摘This paper attempts to concentrate on the eight basic principles of reliability-centeredmaintenance, and discusses respectively the differences between old and new maintenanceconcepts in eight aspects, i.e., function of rework at hard time, potential failure and funcrionalfailure, function of preventive maintenance, change of failure consequence, determinarion ofpreventive maintenance work and formulation of initial preventive maintenance program, andperfection of preventive maintenance program.
文摘Fault diagnosis and prognosis in mechanical systems have been researched and developed in the last few decades at a very rapid rate. However, owing to the high complexity of machine centers, research on improving the accuracy and reliability of fault diagnosis and prognosis via data mining remains a prominent issue in this field. This study investigates fault diagnosis and prognosis in machine centers based on data mining approaches to formulate a systematic approach and obtain knowledge for predictive maintenance in Industry 4.0 era. We introduce a system framework based on Industry 4.0 concepts, which includes the process of fault analysis and treatment for predictive maintenance in machine centers. The framework includes five modules： sensor selection and data acquisition module, data preprocessing module, data mining module, decision support module, and maintenance implementation module. Furthermore, a case study is presented to illustrate the application of the data mining methods for fault diagnosis and prognosis in machine centers as an Industry 4.0 scenario.
文摘It is especially significant for a manufacturing company to select a proper maintenance policy because maintenance impacts not only on economy, reliability and availability but also on personnel safety. This article re- ports on research in the backlash error data interpretation and compensation for intelligent predictive maintenance in machine centers based on artificial neural networks （ANNs）. The backlash error, measurement system and prediction methods are analyzed in detail. The result indicates that it is possible to predict and compensate for the backlash error in both forward and backward directions in machine centers.
