数字制造技术在油料装备领域的应用

数字制造技术可以系统地应用于油料装备领域,使高质量、高效和低成本的油料装备的研制、生产和管理维护成为了可能。基于数字制造技术,对油料装备数字产品、数字协同设计、数字加工、数字维护和管理等技术进行研究。这些技术融入油料装备领域大大节约了油料装备的设计、使用和维护的成本,提升了技术水平,取得了良好的军事和经济效应。     

Integrated maintenance and operations decision making with imperfect degradation state observations

In highly flexible and integrated manufacturing systems, such as semiconductor fabs, strong interactions between the equipment condition, operations executed on the various machines and the outgoing product quality necessitate integrated decision making in the domains of maintenance scheduling and production operations. Furthermore, in highly complex manufacturing equipment, the underlying condition is not directly observable and can only be inferred probabilistically from the available sensor readings. In order to deal with interactions between maintenance and production operations in Flexible Manufacturing Systems (FMSs) in which equipment conditions are not perfectly observable, we propose in this paper a decision-making method based on a Partially Observable Markov Decision Processes (POMDP's), yielding an integrated policy in the realms of maintenance scheduling and production sequencing. Optimization was pursued using a metaheuristic method that used the results of discrete-event simulations of the underlying manufacturing system. The new approach is demonstrated in simulations of a generic semiconductor manufacturing cluster tool. The results showed that, regardless of uncertainties in the knowledge of actual equipment conditions, jointly making maintenance and production sequencing decisions consistently outperforms the current practice of making these decisions separately.     

Hierarchical indices to detect equipment condition changes with high dimensional data for semiconductor manufacturing

During semiconductor manufacturing process, massive and various types of interrelated equipment data are automatically collected for fault detection and classification. Indeed, unusual wafer measurements may reflect a wafer defect or a change in equipment conditions. Early detection of equipment condition changes assists the engineer with efficient maintenance. This study aims to develop hierarchical indices for equipment monitoring. For efficiency, only the highest level index is used for real-time monitoring. Once the index decreases, the engineers can use the drilled down indices to identify potential root causes. For validation, the proposed approach was tested in a leading semiconductor foundry in Taiwan. The results have shown that the proposed approach and associated indices can detect equipment condition changes after preventive maintenance efficiently and effectively.     

Maintenance scheduling in manufacturing systems based on predicted machine degradation

In this paper, we propose a new method for scheduling of maintenance operations in a manufacturing system using the continuous assessment and prediction of the level of performance degradation of manufacturing equipment, as well as the complex interaction between the production process and maintenance operations. Effects of any maintenance schedule are evaluated through a discrete-event simulation that utilizes predicted probabilities of machine failures in the manufacturing system, where predicted probabilities of failure are assumed to be available either from historical equipment reliability information or based on the newly available predictive algorithms. A Genetic Algorithm based optimization procedure is used to search for the most cost-effective maintenance schedule, considering both production gains and maintenance expenses. The algorithm is implemented in a simulated environment and benchmarked against several traditional maintenance strategies, such as corrective maintenance, scheduled maintenance and condition-based maintenance. In all cases that were studied, application of the newly proposed maintenance scheduling tool resulted in a noticeable increase in the cost-benefits, which indicates that the use of predictive information about equipment performance through the newly proposed maintenance scheduling method could result in significant gains obtained by optimal maintenance scheduling.     

基于维护计划的卷烟生产线设备运维管理系统

介绍了一个基于维护计划的卷烟生产线设备运维管理系统,分别从系统功能模型、计划业务流程等角度描述了系统的设计思想。并阐述了系统涉及的设备润滑计划制定、派工、运行维护数据分析、系统开发与应用等关键技术。开发的系统具有一定的可集成性、可扩展性和可重构性,与移动通讯技术、数据采集技术相结合,集成在制造执行系统中,可以实现卷烟生产企业制丝、卷包生产线的实时设备运行与维护保养管理。     

两阶段设备维护策略选择的灰靶决策模型

针对设备的最佳维护策略选择问题,首先提出6种设备运维目标,同时给出两阶段设备运维策略选择的加权智能灰靶决策模型的架构图以及建模算法流程;然后采用德尔菲调查法与层次分析法相结合确定不同目标的权重.结合某半导体面板制造企业的设备运行现场实际数据,对于成本型目标和适中型目标,分别采用相应的效果测度函数计算出设备在不同运行状态下的一致效果测度矩阵以及综合效果测度矩阵.通过运用两阶段设备维护的灰靶决策模型,最终得到设备不同状态下的最佳维护策略.所提出方法对正确选择半导体面板设备维护策略、提高设备运维效率、降低维护成本具有实际指导意义.     

Modern computer-aided maintenance of manufacturing equipment and systems: Review and Perspective

This paper examines various methods in modern computer-aided maintenance including machine monitoring, fault detection and fault diagnostics. A perspective on proactive maintenance by monitoring the degradation of manufacturing equipment and systems is presented and illustrated. If the behavior of manufacturing equipment and systems can be monitored and measured adaptively then an early warning of possible faults can be generated. By doing this, maintenance personnel can perform early diagnostics and part replacement during regular maintenance hours. The paper also addresses the research needs based on the industrial perspective. The author believes that the development of in-process monitoring of machine degradation and faults is one of the most important research tasks for increasing machine uptime and improving production quality.     

远程专家诊断系统的应用研究

随着设备采购日趋国际化、分散化,设备供应商与用户往往分隔两地.传统的设备维护是在故障发生之后维护人员奔赴现场进行维修,无论对供应商还是对用户来讲,都很不经济.加之维护人员到现场后才能根据设备检测数据作出故障诊断,然后开始维修工作,因此相当缺乏效率,而设备远程监控与诊断维护技术的出现,为上述问题的解决提供了有效途径,并已...     

维修时点预测的动态车间调度问题

针对制造业中生产计划的不确定问题,提出一种维修时点预测与自适应的遗传模拟退火算法相结合的优化调度方法。该方法首先利用差分自回归移动平均模型预测设备未来的故障率,然后借助电气设备的威布尔（Weibull）分布模型逆向求出设备未来故障发生时刻,最后将此作为约束条件,利用自适应的遗传模拟退火算法解决传统的生产调度问题。结合工厂实际情况,主要分析了设备有无维修的随机调度问题,以最小化最大完工时间为目标,获取每一个任务的调度计划以及每一台设备的维修时点,确定出最佳调度方案。实验表明自适应的遗传模拟退火算法的性能较好。在河北某工厂的生产车间中,设备在运行调度方法后三个月的平均故障率比运行前相对降低了3.46%。     

物联网技术在智能装备中的应用

为了延长制造型企业大型设备使用寿命,降低维护成本,开发设备综合监控与预警系统.该系统综合运用物联网技术、现场总线技术、无线技术、人工智能技术等,构建覆盖全厂范围的物联网络,实时采集设备信号,对设备故障进行预警.     

现代制造设备远程故障监测与诊断系统研究

对现代制造设备进行故障监民诊断是现代化生产及制造设备自动化,集成化发展的需要,在分析故障监测与诊断基本蝗基础上,提出了建立现代制造设备远程故障监测与诊断系统的设计思想,并结合中德政府合作项目向上海大众汽车有限公司的具体应用给出「实现实例。     

An optimal maintenance policy based on generalized stochastic Petri nets and periodic inspection

Periodic maintenance of equipment is essential for its optimum performance, thereby enabling production efficiency. In the past, studies on preventive maintenance of automated manufacturing systems (AMS) determined the optimal preventive maintenance policy under different performance indexes. Generally, most hypotheses indicate that equipment reliability can be restored to 1.0 through preventive and corrective maintenance. However, in practical application, the implementation of preventive maintenance results in partial deterioration of equipment; moreover, the reliability of equipment cannot be restored to as‐good‐as‐new. In addition, the greater the complexity of connections of the equipment, the greater is the difficulty in determining the timing for preventive maintenance. On account of these characteristics, generalized stochastic Petri nets (GSPN) are well‐suited for the implementation of preventive maintenance. Therefore, this paper applies GSPN for deciding the optimal maintenance policy and constructing models for different levels of maintenance and renewal for an AMS with a serial‐parallel layout. As a result of the application of GSPN, the following optimal maintenance policy for an AMS was obtained in this study: Preventive maintenance conducted at intervals of every 240 hours will reduce cost by 46% as opposed to the practice of replacing defective parts when necessary. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Confidence estimation of feedback information for logicdiagnosis

This paper proposes an estimation method for the confidence level of feedback information (CLFI), namely the confidence level of reported information in computer integrated manufacturing (CIM) architecture for logic diagnosis. This confidence estimation provides a diagnosis module with precise reported information to quickly identify the origin of equipment failure. We studied the factors affecting CLFI, such as measurement system reliability, production context, position of sensors in the acquisition chains, type of products, reference metrology, preventive maintenance and corrective maintenance based on historical data and feedback information generated by production equipments. We introduced the new ‘CLFI’ concept based on the Dynamic Bayesian Network approach and Tree Augmented Naïve Bayes model. Our contribution includes an on-line confidence computation module for production equipment data, and an algorithm to compute CLFI. We suggest it to be applied to the semiconductor manufacturing industry.     

Hidden maintenance opportunities in discrete and complex production lines

In manufacturing systems, many maintenance tasks require equipment to be stopped in order to safely perform them. However, such stoppage cannot last for too long since it might directly result in short-term production losses. In this paper, we investigate how long we can strategically shut down equipment for maintenance during scheduled operations without affecting system throughput. Using the concept of maintenance opportunity windows (MOWs), we estimate such time intervals for various system configurations. Simulations are used to deal with uncertainties in production lines, such as random machine failures, starvations, blockages, etc. Moreover, the proposed MOW algorithms are demonstrated through simulations and real case studies in an automotive assembly plant.     

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.     

考虑生产任务的制造设备关键部件的机会维修优化

为解决制造设备关键部件的维修决策问题,将生产任务信息、视情维修以及机会维修相结合,考虑设备关键部件的剩余价值以及可靠性风险建立维修决策优化模型.在已有关于视情维修和机会维修成果的基础上,考虑关键部件的剩余寿命与下一阶段生产任务时长间的关系,以总成本最小化为目标确定是否在相邻生产任务间利用维修机会,使得在任务顺利进行的条件下降低成本.基于逆高斯过程进行部件退化建模,计算不同维修组合对应的失效概率,进而构建成本最小化目标规划函数并通过仿真算法得到预防性维修的最优值.最后通过数值实验验证所提出维修策略的有效性.     

一种无线振动传感器频响带宽提高方法

高频响无线振动传感器是机械设备预测性维护领域不可或缺的信号采集设备,目前机械设备监测应用中常用的无线振动传感器频率响应较低,限制了其在机械设备预测性维护领域的应用;为了进一步提高无线振动传感器频率响应带宽,提出一种提高无线振动传感器频响范围的方法,从结构设计、振动探头设计、信号调理链路设计和模数转换器设计4个方面对传统无线振动传感器进行了改进;实验结果表明,无线振动传感器带宽提高方法将无线振动传感器频率响应带宽提高了7.96 dB;在振动监测领域具有较强的实用性,对智能传感器制造领域具有较好的指导意义.     

A model for preventive maintenance operations and forecasting

Equipment costs constitute the greatest majority of overall costs for semiconductor manufacturing. Therefore, maintaining high equipment availability has been regarded as one of the major goals in the industry. The ability to forecast correctly equipment preventive maintenance (PM) timing requirements not only can help optimizing equipment uptime but also minimizing negative impacts on manufacturing production efficiency. This research used grey theory and evaluation diagnosis to construct a PM forecasting model for prediction of PM timing of various machines. The results showed significant improvements of PM timing predictions compared to the existing method based on experience and an alternative method proposed by Li and Chang (Semiconductor Manufacturing Technology Workshop 2002: 10–11, pp. 275–277) for the same fab cases. Received: June 2005 / Accepted: December 2005     

A data-driven two-stage maintenance framework for degradation prediction in semiconductor manufacturing industries

To reduce the production costs and breakdown risks in industrial manufacturing systems, condition-based maintenance has been actively pursued for prediction of equipment degradation and optimization of maintenance schedules. In this paper, a two-stage maintenance framework using data-driven techniques under two training types will be developed to predict the degradation status in industrial applications. The proposed framework consists of three main blocks, namely, Primary Maintenance Block (PMB), Secondary Maintenance Block (SMB), and degradation status determination block. As the popular methods with deterministic training, back-propagation Neural Network (NN) and evolvable NN are employed in PMB for the degradation prediction. Another two data-driven methods with probabilistic training, namely, restricted Boltzmann machine and deep belief network are applied in SMB as the backup of PMB to model non-stationary processes with the complicated underlying characteristics. Finally, the multiple regression forecasting is adopted in both blocks to check prediction accuracies. The effectiveness of our proposed two-stage maintenance framework is testified with extensive computation and experimental studies on an industrial case of the wafer fabrication plant in semiconductor manufactories, achieving up to 74.1% in testing accuracies for equipment degradation prediction.     

A predictive maintenance system for multi-granularity faults based on AdaBelief-BP neural network and fuzzy decision making

Predictive maintenance of production equipment is a prerequisite to ensure safe and reliable manufacturing operations. To avoid unexpected shutdown and even casualties caused by faults during production, it is paramount to design an effective predictive maintenance decision system for production equipment. Most of the related research works concentrate on early warn of specific faults but neglect the differentiations of the fault severity. To address the issue, this paper presents an intelligent predictive maintenance system for multi-granularity faults of production equipment based on the AdaBelief-BP (back propagation) neural network and the fuzzy decision making. The characteristics of the system presented in this paper include: (1) The proposed system implements a two-stage framework, integrating the functions of fault type prediction and fault degree prediction, which can provide comprehensive fault information throughout production lifecycles; (2) On the maintenance solution identification stage, fuzzy logic-based decision making is carried out to determine appropriate maintenance solutions based on the practical vague boundary of fault severity. In the system, the design of the AdaBelief-BP neural network can achieve a higher convergence rate and a better generalization capability as well. Meanwhile, to the best of our knowledge, in this research, it is the first time to use the migration of the fuzzy membership degree as the indicator of the changing condition of fault severity to facilitate more accurate maintenance solution identification. To verify the effectiveness of the system, comparison experiments with some popular algorithms are conducted. Benchmarking results show that the developed system can achieve higher prediction accuracy as well as higher efficiency than the comparative methods.