基于设备衰退机制的预知性维护策略及生产排程集成研究
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摘要
随着现代化工业技术的蓬勃发展,确保设备可靠性和维护水平,保证系统正常运行已成为制造企业生存的必要条件。设备维护管理作为制造系统理论的重要组成部分,是保证设备生产效率和降低制造成本的基础和关键。目前虽有不少学者针对设备维护策略进行了研究,但大多为基于时间的设备维护策略,很少研究设备衰退特性对维护策略的影响。因此,有必要探讨设备运行状况及衰退趋势,发展一类以设备状况驱动、不依赖以往统计数据的设备预知性维护策略。此外,考虑到设备维护对制造系统内生产排程会产生重要影响,有必要在传统生产排程模型中引入设备维护概念,基于设备衰退趋势,实现对考虑设备运行状况的生产排程的科学化描述,提出集成设备预知性维护的生产排程优化模型。
本论文以单机生产设备及其生产过程为研究对象,立足于过去的二十年国内外针对设备维护策略和生产排程建模相关研究的基础上,综合运用运筹学、统计学和现代控制理论及优化等多学科理论和方法以及信息科学技术,通过分析设备运行状况,从而揭示设备的衰退规律,为维护管理层提供了有效计划和安排维护活动所需的实际信息,为设备预知性维护策略的实施提供决策依据。同时,探讨设备运行状况及衰退趋势,集成设备预知性维护信息到生产排程模型,拓展了传统生产排程研究领域,为制造系统科学的成功实施提供有力支持。本论文首先研究了设备性能评估方法,基于统计模式识别概念对设备运行过程中的健康状况进行估计和量化。然后基于量化的设备健康指标,探讨设备运行情况,提出了设备剩余维护寿命预测模型,定性和定量地描述设备衰退趋势。通过对设备剩余维护寿命的预测,发展了基于设备剩余维护寿命的预知性维护策略,拓展和完善制造系统维护管理理论。最后研究设备衰退趋势对生产排程的影响,实现对考虑设备运行状况的生产排程的科学化描述,建立了集成设备预知性维护的生产排程优化模型来帮助企业优化实际生产流程。
基于前人的研究成果和大量实际的工程经验,本论文在基于设备衰退机制的设备预知性维护策略及生产排程集成研究中主要做了以下几方面的研究工作:
(1)采用基于统计模式识别的方法对设备性能进行评估,根据量化的设备健康指标,建立设备剩余维护寿命预测模型
建立了一套完整的基于统计模式识别的设备性能评估系统分析流程。针对数据采集仪器获取的设备状态监测数据,通过数据预处理、特征提取以及特征空间降维方法对原始数据进行有效处理。基于统计模式识别的概念,对设备状态进行识别,并在此基础上,利用卡方检验估计样本间关联度,引入设备健康指标来量化设备性能状态,描述设备实际运行状况,解决了设备健康状况的科学化描述问题。
然后,进一步拓展设备性能评估结果,以设备健康指标作为设备衰退预测的分析依据和基础,定义设备剩余维护寿命,构建了设备剩余维护寿命预测模型,解决了设备衰退预测难以量化的问题,拓展了设备健康指标的应用环境,弥补了以往研究中多用函数分布描述设备衰退情况的不足,定性及定量地揭示了设备性能实际衰退趋势。
(2)考虑设备自身衰退特性,发展基于设备剩余维护寿命的设备预知性维护策略
针对传统狭义设备维护概念,考虑设备衰退特性,发展了基于设备剩余维护寿命的预知性维护策略,避免了以往失效维修或周期预防性维护策略引起系统可靠度不足及制造成本过高的情形。在以顾客需求为决策准则的情况下,给出了包含设备健康指标维护阈值和预知性维护周期两个决策变量的维护决策。通过发展这类以运转状况驱动、不依赖于以往统计数据的设备预知性维护策略,弥补了以往基于时间的设备维护策略的不确定性。
在研究中,预测并优化各维护周期内的设备剩余维护寿命,综合考虑设备维护行为与设备性能的交叉影响,探讨设备维护成本和生产运行成本随设备健康状况和使用时间变化的情况,对设备维护成本和生产运行成本建模,改进和完善了设备预知性维护策略。从而为实际生产过程中的设备维护提供决策依据,满足了企业生产效率和成本的要求,有效地提高了系统整体利用率,为制造企业内设备维护提供了新的维护理论和方法。
(3)基于设备衰退机制,提出集成设备预知性维护的生产排程优化模型
针对传统生产排程研究大多忽略设备可靠性能,假定设备始终正常运行,本论文在传统生产排程中引入设备可用性概念。在提出的设备剩余维护寿命预测模型基础上,考虑设备衰退特性对生产排程的影响,分析了设备维护对生产排程的影响作用,提出了集成设备预知性维护的单机生产排程优化模型,解决了生产排程与设备维护的集成问题,极大地拓展了传统生产排程研究领域。
通过对设备衰退趋势定性与定量地描述,预测设备剩余维护寿命,对生产作业的完工时间和延迟时间建模,在最小化最大生产作业延迟时间的目标下,推导出最佳生产作业与设备预知性维护序列,这既保证了设备可靠性水平与生产效率,还满足了实际生产过程的需要,为实际生产过程中制定生产排程提供了新的理论依据,增加和丰富了生产排程研究理论。
本论文提出的模型和方法可定性和定量地描述设备性能状态及设备衰退趋势,为设备提供考虑设备自身衰退特性的预知性维护策略,并基于此实现集成设备预知性维护的生产排程。本论文的研究可帮助企业提高设备可靠性和维护水平、减少制造成本、提高生产效率以及提升综合竞争力,为发展制造系统内维护管理水平、实现制造系统的全部效能提供有力支持,为提高我国以高新技术为核心的数字化生产管理提供积极有利和科学有效的指导。
With the development of industrial technologies in manufacturing enterprise, it is vital to ensure the machine reliability for smooth production. Maintenance management, as an important part in manufacturing system, is the basic and key to ensure equipment productivity and reduce manufacturing cost. Although there are some studies on maintenance policies, most of them are time-based maintenance policies, which ignores the influence of machine degradation. It is thus essential to study machine operation and degradation to develop predictive maintenance policy. Moreover, by considering the influence of maintenance operation on production scheduling in manufacturing system, maintenance operation should be integrated into the traditional production scheduling models. Due to machine degradation, a production scheduling model integrated with predicitve maintenance is proposed. This paper studies a single repairable machine and its production process. Based on the available research on maintenance policies and production scheduling, this paper discusses machine’s operation process and describes machine degradation, which greatly supports predictive maintenance planning. In addition, this paper integrates predictive maintenance into production scheduling subject to machine degradation, which helps extend traditional production scheduling research. In this paper, a performance assessment method of the machine is studied first. Machine’s health index (HI) is estimated based on statistical pattern recognition (SPR). Then, with machine’s HI, machine’s remaining maintenance life (RML) prediction model is built, which can well describe machine’s deterioration process. Later, according to machine’s RML, one predictive maintenance policy is developed. At last, by considering the influence of machine degradation on production scheduling, a production scheduling model integrated with predictive maintenance is proposed, which can optimize real production process.
Based on the previous research and literature, this paper researches on predictive maintenance policy and integrated production scheduling model based on machine degradation, which focuses on the following parts:
(1) Machine’s HI is estimated based on SPR method. Then, machine’s RML prediction model is built with machine’s HI.
A complete machine’s performance assessment procedure is established based on SPR. In this procedure, the collected data is analyzed by data pre-processing, feature extraction and feature dimension reduction methods. Based on SPR concept, machine condition is identified. Chi-square test is used to estimate machine’s HI to help describe machine’s real health condition. With the machine’s HI information, machine’s RML is defined and machine’s RML prediction model is constructed, which helps describe machine degradation.
This RML prediction model extends the applications of machine’s HI and supports predictive maintenance policy.
(2) By considering machine degradation, predictive maintenance policy based on machine’s RML is developed.
Considering machine degradation, a predictive maintenance policy is developed based on machine’s RML. This predicitve maintenance policy studies the influence of machine degradation to avoid the situations of low machine reliability and high manufacturing cost caused by previous failure-based or time-based maintenance policies. The maintenance level of machine’s HI and predictive maintenance cycles are the two decision variables.
In this research, machine’s RML of each maintenance cycle is predicted and optimized. By considering the interaction of maintenance operation and machine performance, the maintenance cost and the operational cost should be variable if machine’s condition is changed. With the development, this predictive maintenance policy is enhanced to satisfy real manufacturing process.
(3) An integrated production scheduling model considering predictive maintenance is proposed based on machine degradation.
As most previous production scheduling research ignores machine reliability and assumes machine is available all the time, they cannot satisfy real manufacturing process due to machine degradation. This paper studies the influence of machine degradation on production scheduling and the influence of maintenance operation on production scheduling. Based on machine’s RML prediction model, this paper proposes a production scheduling model integrated with predcitve maintenance, which helps extend the traditional production scheduling research.
With machine’s RML prediction model, machine’s RML is predicted to describe job’s completion time and tardiness. Then, with the aim to minimize the maximum tardiness of jobs, the optimal job sequence with predivtive maintenance planning is obtained. This integrated production scheduling model can ensure both machine reliability and productivity, which could be taken as new theory and practical references for production scheduling research.
This research work can give qualitative and quantitative description of machine degradation, and provides a predictive maintenance policy considering machine degradation, finally achieves an improved production scheduling model integrated with predictive maintenance. This research can help enterprise ensure machine reliability, reduce manufacturing cost, increase productivity and enhance its competitiveness. It can provide great support to develop maintenance management and achieve the full performance of manufacturing systems, and provide scientific guidance for digital production management.
本论文以单机生产设备及其生产过程为研究对象,立足于过去的二十年国内外针对设备维护策略和生产排程建模相关研究的基础上,综合运用运筹学、统计学和现代控制理论及优化等多学科理论和方法以及信息科学技术,通过分析设备运行状况,从而揭示设备的衰退规律,为维护管理层提供了有效计划和安排维护活动所需的实际信息,为设备预知性维护策略的实施提供决策依据。同时,探讨设备运行状况及衰退趋势,集成设备预知性维护信息到生产排程模型,拓展了传统生产排程研究领域,为制造系统科学的成功实施提供有力支持。本论文首先研究了设备性能评估方法,基于统计模式识别概念对设备运行过程中的健康状况进行估计和量化。然后基于量化的设备健康指标,探讨设备运行情况,提出了设备剩余维护寿命预测模型,定性和定量地描述设备衰退趋势。通过对设备剩余维护寿命的预测,发展了基于设备剩余维护寿命的预知性维护策略,拓展和完善制造系统维护管理理论。最后研究设备衰退趋势对生产排程的影响,实现对考虑设备运行状况的生产排程的科学化描述,建立了集成设备预知性维护的生产排程优化模型来帮助企业优化实际生产流程。
基于前人的研究成果和大量实际的工程经验,本论文在基于设备衰退机制的设备预知性维护策略及生产排程集成研究中主要做了以下几方面的研究工作:
(1)采用基于统计模式识别的方法对设备性能进行评估,根据量化的设备健康指标,建立设备剩余维护寿命预测模型
建立了一套完整的基于统计模式识别的设备性能评估系统分析流程。针对数据采集仪器获取的设备状态监测数据,通过数据预处理、特征提取以及特征空间降维方法对原始数据进行有效处理。基于统计模式识别的概念,对设备状态进行识别,并在此基础上,利用卡方检验估计样本间关联度,引入设备健康指标来量化设备性能状态,描述设备实际运行状况,解决了设备健康状况的科学化描述问题。
然后,进一步拓展设备性能评估结果,以设备健康指标作为设备衰退预测的分析依据和基础,定义设备剩余维护寿命,构建了设备剩余维护寿命预测模型,解决了设备衰退预测难以量化的问题,拓展了设备健康指标的应用环境,弥补了以往研究中多用函数分布描述设备衰退情况的不足,定性及定量地揭示了设备性能实际衰退趋势。
(2)考虑设备自身衰退特性,发展基于设备剩余维护寿命的设备预知性维护策略
针对传统狭义设备维护概念,考虑设备衰退特性,发展了基于设备剩余维护寿命的预知性维护策略,避免了以往失效维修或周期预防性维护策略引起系统可靠度不足及制造成本过高的情形。在以顾客需求为决策准则的情况下,给出了包含设备健康指标维护阈值和预知性维护周期两个决策变量的维护决策。通过发展这类以运转状况驱动、不依赖于以往统计数据的设备预知性维护策略,弥补了以往基于时间的设备维护策略的不确定性。
在研究中,预测并优化各维护周期内的设备剩余维护寿命,综合考虑设备维护行为与设备性能的交叉影响,探讨设备维护成本和生产运行成本随设备健康状况和使用时间变化的情况,对设备维护成本和生产运行成本建模,改进和完善了设备预知性维护策略。从而为实际生产过程中的设备维护提供决策依据,满足了企业生产效率和成本的要求,有效地提高了系统整体利用率,为制造企业内设备维护提供了新的维护理论和方法。
(3)基于设备衰退机制,提出集成设备预知性维护的生产排程优化模型
针对传统生产排程研究大多忽略设备可靠性能,假定设备始终正常运行,本论文在传统生产排程中引入设备可用性概念。在提出的设备剩余维护寿命预测模型基础上,考虑设备衰退特性对生产排程的影响,分析了设备维护对生产排程的影响作用,提出了集成设备预知性维护的单机生产排程优化模型,解决了生产排程与设备维护的集成问题,极大地拓展了传统生产排程研究领域。
通过对设备衰退趋势定性与定量地描述,预测设备剩余维护寿命,对生产作业的完工时间和延迟时间建模,在最小化最大生产作业延迟时间的目标下,推导出最佳生产作业与设备预知性维护序列,这既保证了设备可靠性水平与生产效率,还满足了实际生产过程的需要,为实际生产过程中制定生产排程提供了新的理论依据,增加和丰富了生产排程研究理论。
本论文提出的模型和方法可定性和定量地描述设备性能状态及设备衰退趋势,为设备提供考虑设备自身衰退特性的预知性维护策略,并基于此实现集成设备预知性维护的生产排程。本论文的研究可帮助企业提高设备可靠性和维护水平、减少制造成本、提高生产效率以及提升综合竞争力,为发展制造系统内维护管理水平、实现制造系统的全部效能提供有力支持,为提高我国以高新技术为核心的数字化生产管理提供积极有利和科学有效的指导。
With the development of industrial technologies in manufacturing enterprise, it is vital to ensure the machine reliability for smooth production. Maintenance management, as an important part in manufacturing system, is the basic and key to ensure equipment productivity and reduce manufacturing cost. Although there are some studies on maintenance policies, most of them are time-based maintenance policies, which ignores the influence of machine degradation. It is thus essential to study machine operation and degradation to develop predictive maintenance policy. Moreover, by considering the influence of maintenance operation on production scheduling in manufacturing system, maintenance operation should be integrated into the traditional production scheduling models. Due to machine degradation, a production scheduling model integrated with predicitve maintenance is proposed. This paper studies a single repairable machine and its production process. Based on the available research on maintenance policies and production scheduling, this paper discusses machine’s operation process and describes machine degradation, which greatly supports predictive maintenance planning. In addition, this paper integrates predictive maintenance into production scheduling subject to machine degradation, which helps extend traditional production scheduling research. In this paper, a performance assessment method of the machine is studied first. Machine’s health index (HI) is estimated based on statistical pattern recognition (SPR). Then, with machine’s HI, machine’s remaining maintenance life (RML) prediction model is built, which can well describe machine’s deterioration process. Later, according to machine’s RML, one predictive maintenance policy is developed. At last, by considering the influence of machine degradation on production scheduling, a production scheduling model integrated with predictive maintenance is proposed, which can optimize real production process.
Based on the previous research and literature, this paper researches on predictive maintenance policy and integrated production scheduling model based on machine degradation, which focuses on the following parts:
(1) Machine’s HI is estimated based on SPR method. Then, machine’s RML prediction model is built with machine’s HI.
A complete machine’s performance assessment procedure is established based on SPR. In this procedure, the collected data is analyzed by data pre-processing, feature extraction and feature dimension reduction methods. Based on SPR concept, machine condition is identified. Chi-square test is used to estimate machine’s HI to help describe machine’s real health condition. With the machine’s HI information, machine’s RML is defined and machine’s RML prediction model is constructed, which helps describe machine degradation.
This RML prediction model extends the applications of machine’s HI and supports predictive maintenance policy.
(2) By considering machine degradation, predictive maintenance policy based on machine’s RML is developed.
Considering machine degradation, a predictive maintenance policy is developed based on machine’s RML. This predicitve maintenance policy studies the influence of machine degradation to avoid the situations of low machine reliability and high manufacturing cost caused by previous failure-based or time-based maintenance policies. The maintenance level of machine’s HI and predictive maintenance cycles are the two decision variables.
In this research, machine’s RML of each maintenance cycle is predicted and optimized. By considering the interaction of maintenance operation and machine performance, the maintenance cost and the operational cost should be variable if machine’s condition is changed. With the development, this predictive maintenance policy is enhanced to satisfy real manufacturing process.
(3) An integrated production scheduling model considering predictive maintenance is proposed based on machine degradation.
As most previous production scheduling research ignores machine reliability and assumes machine is available all the time, they cannot satisfy real manufacturing process due to machine degradation. This paper studies the influence of machine degradation on production scheduling and the influence of maintenance operation on production scheduling. Based on machine’s RML prediction model, this paper proposes a production scheduling model integrated with predcitve maintenance, which helps extend the traditional production scheduling research.
With machine’s RML prediction model, machine’s RML is predicted to describe job’s completion time and tardiness. Then, with the aim to minimize the maximum tardiness of jobs, the optimal job sequence with predivtive maintenance planning is obtained. This integrated production scheduling model can ensure both machine reliability and productivity, which could be taken as new theory and practical references for production scheduling research.
This research work can give qualitative and quantitative description of machine degradation, and provides a predictive maintenance policy considering machine degradation, finally achieves an improved production scheduling model integrated with predictive maintenance. This research can help enterprise ensure machine reliability, reduce manufacturing cost, increase productivity and enhance its competitiveness. It can provide great support to develop maintenance management and achieve the full performance of manufacturing systems, and provide scientific guidance for digital production management.
引文
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