制系统信息安全集散控制系统（DCS）第1部分防护要求

1、Use Case and Best Practices of Predictive Maintenance for Sino-German Industrie 4.0/ Intelligent ManufacturingSino-German Industrie 4.0/ Intelligent Manufacturing Standardisation Sub-Workong GroupIntroductionEfficient production significantly relies on the availability of the production equipment. I

2、n order to guarantee the intended usage of such equipment and to avoid unplanned downtimes, the status of the equipment and its components further referred to as “asset” need to be monitored and assessed. This process is called Condition Monitoring. Based on the assessment and with knowledge of the

3、intended processes to be carried out, a prediction of the remaining error-free operation of the equipment can be made, and possible activities for maintenance can be planned. This process is called Predictive Maintenance (PdM). Changes of the production workflow can also be initiated, targeting on r

4、e-organization of the equipment usage. Figure 1 shows a principle system structure with condition monitoring and prediction functionFigure 1: Positioning of condition monitoring, prediction, and maintenance scheduling in a production system (principle).Part 1: Use Case Predictive Maintenance of Prod

5、uction AssetOverviewIn the overall community the term “Use Case” is understood and used very differently. In the standardization roadmap Industrie 4.0, Version 3, see 1, it was therefore recommended to distinguish basically three different categories of “Use Cases”:Business scenarios describing a va

6、lue-network of business roles, where each business role is characterized by a business modelUse cases describing the interaction of technical roles with a technical system, where the context of a technical system and high-level requirements are specified, how the technical system interacts within it

7、s contextExample projects, case studies, technical solution concepts, etc.In the scope of Predictive Maintenance Working Group of the Sino-German Standardization Cooperation Commission (TEG PM), the term “Use Case” is understood as in the second of the three categories mentioned. Thus, the second an

8、d last category above will be included in the TEG PM publication “Best Practice of Predictive Maintenance”.IEC TC65 WG23 and especially the task force “Smart Manufacturing Use Cases” have the goal to analyze the impact of “Smart Manufacturing” on standardization. The approach chosen by the task forc

9、e “Smart Manufacturing Use Cases” is the collection and evaluation of use cases. Use cases Predictive Maintenance of Production Asset uses the methodology of IEC TC65 WG23 and extends the scope of Use cases Condition Monitoring of Production Resources in WG23.ObjectiveCompared with the condition of

10、asset, production managers are often more concerned about how long they can be used and how to find the most economical way of maintenance. It requires rich experience, large amounts of data and computing power to meet the demand of production managers, and it is difficult for any stakeholders to co

11、mplete independently. Therefore, standardized definitions, structures, methods, and application example are needed to provide the possibility for the establishment of predictive maintenance data pools.Efficient production significantly relies on the availability of the production equipment. In order

12、 to guarantee the intended usage of such equipment and to avoid unplanned downtimes, the status of the equipment and its components further referred to as “asset” need to be monitored and assessed. Based on the assessment and with knowledge of the intended processes to be carried out, a prediction o

13、f the remaining error-free operation of the equipment can be made, and possible activities for maintenance can be planned. This process is called Predictive Maintenance. Changes of the production workflow can also be initiated, targeting on re-organization of the equipment usage.The core of this Use

14、 Case is to predict the future health status of asset based on condition monitoring, using data-driven or mechanism-based methods, optimize maintenance resource allocation, and provide reasonable recommendations to stakeholders.From a technical point of view, predictive maintenance covers sensing, c

15、ondition monitoring, fault diagnosis, remaining life prediction and maintenance management technologies and so on. Sensing and condition monitoring are input of predictive maintenance, fault diagnosis and remaining life prediction are processes, and maintenance management is output. Fault diagnosis

16、and remaining life prediction can be carried out based on data-driven, mechanism models or mixed method, including neural network, support vector machine, deep learning. A deep understanding of assets and possible faults is a prerequisite for predictive maintenance.The hardware and software that sup

17、port Predictive Maintenance can be called infrastructure，the internal interfaces of infrastructure, and external interfaces to relevant systems shall be standardized. In addition, all components of the infrastructure shall contain functions for common identification, self-description, and management

18、 of components, these functions can be realized using an asset administration shell.Therefore, the roles involved in this Use Case include asset supplier and users (asset operators). Service providers provide services to both parties in their areas of expertise, such as diagnostic and predictive mod

19、el building. Although more and more asset suppliers have the ability to provide predictive maintenance services, the addition of services providers is more conducive to sharing experience and standardizing infrastructure.This Use Case includes service providers. In describing this use case, a descri

20、ption method consistent with the Use Case Management of assets is used.Overview of rolesBusiness contextTechnical perspectiveDescriptionInteractions of rolesAsset development engineer develops predictive maintenance function of asset, during development, service provider can provide support of exper

21、ience, data, and model. The results of predictive maintenance should provide information to the asset management system.Service provider is a professional role engaged in predictive maintenance consulting, solutions, software and hardware equipment, system integration, and data / model support, whic

22、h can be an independent company, or a department in asset supplier or manufacturer.System integration engineer is responsible for connecting the predictive maintenance function with hardware and software of manufacturer to achieve the necessary information communication. If necessary, system integra

23、tion engineer needs asset integration, to provide the required computing capabilities for the health prediction (in the case that the asset itself does not provide appropriate computing capabilities).Predictive maintenance function continuously monitors the usage information provided by the asset an

24、d updates the condition of the asset in the asset management. In critical situations the asset operator, maintenance engineer, production manager and factory manager are notified timely.New compared to todayDue to the real-time requirements of industrial production, one of the development trends of

25、predictive maintenance is edge computing, that is, process data and feedback maintenance information through edge devices with computing capabilities.With the requirements for accuracy of prediction results, another development trend of predictive maintenance is platform computing, that is, relying

26、on the update of communication technology, uploading as much data as possible to the platform for calculation to obtain more computing capabilities. Because platform computing and edge computing are different in their implementation methods, edge computing is usually used to process alarm informatio

27、n, and platform computing is used for prediction function.In addition to the model, the implementation of predictive maintenance also requires the support of experience. Therefore, the data pool is also the development trend of predictive maintenance. Generally, the data of supplier cannot be shared

28、, but with the advent of predictive maintenance service provider, the establishment of a data pool becomes necessary, which also creates a standardized demand.Part 2: ClassificationPdM for Workshop or PlantPdM for AssetPart 3: Best practices of PdM for Workshop or PlantCase 1: 个性化定制生产线的预测性维护Overview

29、个性化定制生产线由三菱电机自动化（中国）有限公司（简称三菱）和机械工业仪器仪表综合技术经济研究所（简称仪综所）共同开发。作为个性化定制生产线的核心装备，数控机床和智能机器人的正常运行对于保障产线运行稳定性至关重要。突发故障会导致数控机床和智能机器人宕机，造成生产停滞，影响生产和演示任务，同时故障也会导致生产成本的提高，降低生产效率。因此对数控机床和智能机器人开展预测性维护是十分必要的，亟需部署预测性维护平台以提高智能装备的经济性和可靠性，提高产线运行稳定性。个性化定制生产线的建设过程中，三菱主要负责产线整体的规划设计建设，仪综所负责产线预测性维护平台的研发与建设，实现对产线关键装备数控机床和智

30、能机器人的预测性维护。开发的预测性维护平台在生产系统架构中的位置如下图所示，覆盖了Level0、Level1、Level2和Level3层级。个性化定制生产线预测性维护平台可以实现产线关键装备数控机床和智能机器人的传感、状态评估、故障诊断、剩余寿命预测、维护管理等功能。如图5所示。Roles预测性维护技术的实施模式多样，本案例的实施模式为服务供应商与系统集成商合作开发预测性维护平台。在产线预测性维护平台建设中仪综所的角色为服务供应商，主要提供预测维护技术、平台建设方案和数据/模型支持；三菱的角色为系统集成工程师，主要负责将预测维护功能与制造商的硬件和软件进行连接，以实现系统集成。System

31、architecture个性化定制生产线预测性维护平台的架构如图所示。其中硬件主要包括数控机床、智能机器人，数据采集装置等。软件主要包括边缘计算管理系统、状态监测与健康管理系统、智能建模与模型验证系统。Functions and methods个性化定制生产线预测性维护平台可实现数控机床主轴状态监测和故障诊断、刀具剩余寿命预测(提前1天提出换刀要求)、滚珠丝杠寿命预测，智能机器人的剩余寿命预测、负载管理、位置精度分析等，主要功能有：边缘计算管理：实现边缘端数据采集与分析功能，包括设备状态、趋势分析、硬件管理、报警管理、系统管理等模块； 预测性维护云平台：主要实现预测性维护算法模型运行环境搭建、

32、数据中心数据的分析和处理，包括流程管理、模型管理、设备管理、接入服务、数据服务、系统管理等模块；智能建模与模型验证：主要实现模型离线训练和有效性验证，包括建模、训练、优化、数据源管理等模块。状态监控与健康评估：主要实现装备状态及预测结果的可视化分析，包括温度趋势分析、电流趋势分析、振动 RMS值趋势分析、振动频谱分析、RUL值显示等模块。Feature highlights个性化定制生产线预测性维护平台的突出特点如下：边缘端智能化IPC：个性化定制生产线部署的边缘端智能化IPC将PLC控制器、网关、运动控制、I/O数据采集、现场总线协议、设备联网等多领域功能集成于一体，兼具“实时控制”和“边缘

33、计算”，具有高速度、大容量的数据传输和处理能力，高精度、低延时的控制性能，省空间、可扩展、易维护的使用特性。PLC与传感器融合采集：个性化定制生产线部署的智能采集终端具有六个测量通道，实现了振动、温度、湿度等传感器数据采集与PLC数据采集的融合，保障了数据的质量和统一。具备预测性维护的智能机器人：个性化定制生产线中的智能机器人具备了RV减速机、皮带、润滑油的状态监测和寿命预测，内嵌的预测性维护功能为智能机器人稳定运行提供了保障。Visualization个性化定制生产线预测性维护平台可以实现数控机床主轴、Y轴、Z轴的温度、电池、电流、转速等参数以及振动加速度、有效值等状态参数的可视化展示。绿色

34、、黄色、红色分别表示设备正常、设备异常、设备故障三种状态，实现可视化显示和预警。可视化界面如图8所示。个性化定制生产线预测性维护平台也可实现智能机器人的J1-J6轴的电流、编码器温度、马达转速、减速机消耗度、润滑油消耗度等参数的可视化展示。绿色、黄色、红色分别表示设备正常、设备异常、设备故障三种状态，实现可视化显示和预警。可视化界面如图9所示。Part 4: Best practices of PdM for AssetCase 1: RV减速机的状态监测与寿命预测OverviewRV减速机是智能机器人核心组件之一,其运行的性能是决定智能机器人能否正常执行并正确实现加工动作的关键。RV减速机结构精密、尺寸精细、且运行在变载变速等恶劣工况条件下，对RV减速机的正常运行带来了极大挑战，亟需开展RV减速机的状态监测与寿命预测，建立RV减速机预测性维护系统。机械工业仪器仪表综合技术经济研究所联合北京天泽智云科技有限公司共同建设了RV减速机预测性维护测试床，搭建了软硬件系统实现对关键资产RV减速机的预测性维护。RV减速机预测性维护测试床实现了系统架构中的监测、诊断与寿命预测，位置如下图所示，覆盖了Level0、Level1、Level2层级。RV减速机预测性维护测试床在RV减速机台上部署了振动、编