Industry 4.0有关介绍文章2.doc

Industry 4.0From Wikipedia, the free encyclopediaThe four industrial revolutionsIndustry 4.0,Industrie 4.0or thefourthindustrial revolution, is the current trend ofautomationand data exchange in manufacturing technologies. It includescyber-physical systems, theInternet of thingsandcloud computing. Industry 4.0 creates what has been called a smart factory. Within the modular structured smart factories, cyber-physical systems monitor physical processes, create a virtual copy of the physical world and make decentralized decisions. Over the Internet of Things, cyber-physical systems communicate and cooperate with each other and with humans in real time, and via the Internet of Services, both internal and cross-organizational services are offered and used by participants of thevalue chain. NameThe term Industrie 4.0 originates from a project in the high-tech strategy of theGerman government, which promotes thecomputerizationof manufacturing. Some have used the term fourth industrial revolution to refer to Industrie 4.0. The use of the term in current context typically involves the creation of a series of supposed revolutions. Such an account will typically claim the firstindustrial revolutionmobilised the mechanization of production using water and steam power; thesecond industrial revolutionthen introduced mass production with the help ofelectric power, followed by thedigital revolutionand the use of electronics and IT to further automate production. However, the term fourth industrial revolution has been applied to significant technological developments several times over the last 75 years The term Industrie 4.0 was revived in 2011 at theHannover Fair. In October 2012 the Working Group on Industry 4.0 presented a set of Industry 4.0 implementation recommendations to the German federal government. The Industry 4.0 workgroup members are recognized as the founding fathers and driving force behind Industry 4.0.Industry 4.0 WorkgroupsCo-Chair Henning Kagermann and Siegfired DaisWG 1 The Smart Factory: Manfred WittensteinWG 2 The Real Environment: Siegfried RusswurmWG 3 The Economic Environment: Stephan FischeWG 4 Human Beings and Work: Wolfgang WahlsterWG 5 The Technology Factor: Heinz DerenbachIndustry 4.0 Workgroup membersReinhold Achatz, Heinrich Arnold, Klaus Trger, Johannes Helbig, Wolfram Jost, Peter Leibinger, Reinhard Floss, Volker Smid, Thomas Weber, Eberhard Veit, Christian Zeidler, Reiner Anderl,de:Thomas Bauernhansl, Michael Beigl, Manfred Brot, Werner Damm, Jrgen Gausemeier, Otthein Herzog, Fritz Klicke, Gunther Reinhart, Bernd Scholz-Reiter, Bernhard Diener, Rainer Platz, Gisela Lanza, Karsten Ortenberg,August Wilhelm Scheer,Henrik von Scheel, Dieter Schwer, Ingrid Sehrbrock, Dieter Spatz, Ursula M. Staudinger, Andreas Geerdeter, Wolf-Dieter Lukas, Ingo Rhmann, Alexander Kettenborn and Clemens Zielinka.On 8 April 2013 at the Hannover Fair, the final report of the Working Group Industry 4.0 was presented. Design principlesThere are four design principles in Industry 4.0. These principles support companies in identifying and implementing Industry 4.0 scenarios. Interoperability: The ability of machines, devices, sensors, and people to connect and communicate with each other via the Internet of Things (IoT) or the Internet of People (IoP). Information transparency: The ability of information systems to create a virtual copy of the physical world by enriching digital plant models with sensor data. This requires the aggregation of raw sensor data to higher-value context information. Technical assistance: First, the ability of assistance systems to support humans by aggregating and visualizing information comprehensibly for making informed decisions and solving urgent problems on short notice. Second, the ability of cyber physical systems to physically support humans by conducting a range of tasks that are unpleasant, too exhausting, or unsafe for their human co-workers. Decentralized decisions: The ability of cyber physical systems to make decisions on their own and to perform their tasks as autonomous as possible. Only in case of exceptions, interferences, or conflicting goals, tasks are delegated to a higher level.MeaningCurrent usage of the term has been criticised as essentially meaningless, in particular on the grounds that technological innovation is continuous and the concept of a revolution in technology innovation is based on a lack of knowledge of the details. Detailed characteristics of the Fourth Industrial Revolution in current discourse refer to the characteristics given for the German governments Industry 4.0 strategy - the strong customization of products under the conditions of highly flexibilized (mass-) production. The required automation technology is improved by the introduction of methods of self-optimization, self-configuration, self-diagnosis, cognition and intelligent support of workers in their increasingly complex work. The largest project in Industry 4.0 at the present time (July 2013) is theBMBFleading-edge cluster Intelligent Technical Systems OstWestfalenLippe (its OWL). Another major project is the BMBF project RES-COM, as well as the Cluster of Excellence Integrative Production Technology for High-Wage Countries.In 2015, the European Commission started the internationalHorizon 2020research project CREMA(Providing Cloud-based Rapid Elastic Manufacturing based on the XaaS and Cloud model) as a major initiative to foster the Industry 4.0 topic.EffectsIn June 2013, consultancy firm McKinsey19released an interview featuring an expert discussion between executives at Robert Bosch - Siegfried Dais (Partner of the Robert Bosch Industrietreuhand KG) and Heinz Derenbach (CEO of Bosch Software Innovations GmbH) - and McKinsey experts. This interview addressed the prevalence of the Internet of Things in manufacturing and the consequent technology-driven changes which promise to trigger a new industrial revolution. At Bosch, and generally in Germany, this phenomenon is referred to as Industry 4.0. The basic principle of Industry 4.0 is that by connecting machines, work pieces and systems, businesses are creating intelligent networks along the entire value chain that can control each other autonomously.Some examples for Industry 4.0 are machines which can predict failures and trigger maintenance processes autonomously or self-organized logistics which react to unexpected changes in production.According to Dais, it is highly likely that the world of production will become more and more networked until everything is interlinked with everything else. While this sounds like a fair assumption and the driving force behind the Internet of Things, it also means that the complexity of production and supplier networks will grow enormously. Networks and processes have so far been limited to one factory. But in an Industry 4.0 scenario, these boundaries of individual factories will most likely no longer exist. Instead, they will be lifted in order to interconnect multiple factories or even geographical regions.There are differences between a typical traditional factory and an Industry 4.0 factory. In the current industry environment, providing high-end quality service or product with the least cost is the key to success and industrial factories are trying to achieve as much performance as possible to increase their profit as well as their reputation. In this way, various data sources are available to provide worthwhile information about different aspects of the factory. In this stage, the utilization of data for understanding current operating conditions and detecting faults and failures is an important topic to research. e.g. in production, there are various commercial tools available to provideOverall Equipment Effectiveness(OEE) information to factory management in order to highlight theroot causesof problems and possible faults in the system. In contrast, in an Industry 4.0 factory, in addition to condition monitoring and fault diagnosis, components and systems are able to gain self-awareness and self-predictiveness, which will provide management with more insight on the status of the factory. Furthermore, peer-to-peer comparison and fusion of health information from various components provides a precise health prediction in component and system levels and force factory management to trigger required maintenance at the best possible time to reach just-in time maintenance and gain near zero downtime. ChallengesChallenges which have been identified include IT security issues, which are greatly aggravated by the inherent need to open up those previously closed production shops Reliability and stability needed for critical machine-to-machine communication (M2M), including very short and stable latency times Need to maintain the integrity of production processes Need to avoid any IT snags, as those would cause expensive production outages Need to protect industrial knowhow (contained also in the control files for the industrial automation gear) Lack of adequate skill-sets to expedite the march towards fourth industrial revolution Threat of redundancy of the corporate IT department General reluctance to change by stakeholders loss of many jobs to automatic processes and IT-controlled processes, especially for lower educated parts of societyRole of big data and analyticsModern information and communication technologies like Cyber-Physical Systems,big dataorcloud computingwill help predict the possibility to increase productivity, quality and flexibility within the manufacturing industry and thus to understand advantages within the competition.Big Data Analytics consists of 6Cs in the integrated Industry 4.0 and Cyber Physical Systems environment. The 6C system comprises:1. Connection (sensor and networks)2. Cloud (computing and data on demand)3. Cyber (model & memory)4. Content/context (meaning and correlation)5. Community (sharing & collaboration)6. Customization (personalization