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从大数据到智能制造

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德国将工业4.0上升为民族战略,英国强势推出“英国工业2025战略”;“中国制造2025”发布引起全民轰动,第四次工业科技革命呼之欲出。在这场科技革命中,智能制造无疑将成为世界各国竞争的新战场。大量的工业大数据在中国汇集,无疑给中国的智能制造带来好的资源优势。只有充分利用这一资源,才能弥补中国在装备制造和核心零部件等方面的弱势与短板,可以这么说,大数据将是中国搭上第四次科技革命快车的大资本。

本书分为3个篇章:导引篇、案例分析篇和专家访谈篇。在导引篇里,本书从利用大数据从解决问题到避免问题、利用大数据预测隐性问题和利用反向工程来重新定义制造等3个方向入手,清晰地阐述了大数据与智能制造的关系。接下来,按照这3个方向,本书引用了17个案例进行详细分析,使读者在接触概念和理论之后,通过真实、有效的案例能够对大数据推动智能制造的发展有一个更加明确和直观的认识。在专家访谈篇中,本书精心策划了由经济学家、学院派教授、媒体人士、企业代表组成的专家访谈团队,使不同背景、不同领域的专家访谈为读者带来不一样的视角。本书适合对大数据、中国制造领域的研究者们阅读,也适合对这些领域感兴趣的社会人士阅读。

Author Description

李杰(Jay Lee),教授,美国辛辛那提大学(Univ.of Cincinnati)特聘讲座教授,美国国家科学基金会(NSF)智能维护系统研究中心(IMS)主任,自2000年起领导全球工业大数据分析与智能维护系统技术研发;2013年,他担任美国白宫信息物理系统与美国挑战项目顾问,2016年起,担任美国麦肯锡(McKinsey&Company)全球顾问。

李杰教授是美国机械工程学会(ASME)及美国制造工程学会(SME)的会士。目前的研究重点是以工业大数据为主的智能预测技术和产品及服务主控式创新创值设计(Dominant Innovation),自2000年起和全球85家国际公司联合研发。李杰教授突破传统机械设备故障预测的理论、方法和技术,其关键性技术得到国际学术界的认可与高度评价,被美国《财富》杂志誉为“21世纪全球三大热门技术”之一。


倪军,教授,美国密西根大学吴贤铭制造科学冠名教授及机械工程终身教授;上海交通大学校长特聘顾问、密西根学院首任院长,美国密西根大学吴贤铭制造研究中心主任及美国国家科学基金会产学研“智能维护系统中心”共同主任。

倪军教授获得40多项学术成就奖。2002年当选为美国制造工程师学会会士,2004年当选为美国机械工程学会会士;2009年入选中组部首批“千人计划”特聘专家;2009年获美国机械工程学会William T. Ennor 高制造技术奖; 2013年获国际制造工程师学会金奖, 是该奖1955年设立之后首位获此殊荣的华人学者;2013年获中华人民共和国国际科技合作奖;2015年获美国密西根大学工程成就奖。


王安正,教授,中组部“千人计划”专家,上海交通大学致远讲席教授,上海交通大学航空发动机研究院首席科学家。多年来任职于美国GE航空发动机公司,担任航空力学首席工程师,先后领导完成GE公司核心产品CF34、CFM56、GE90、GENx等型号风扇、压气机的气动和结构设计与适航审定,在先进复合材料风扇设计认证方面具有丰富经验。

Catalogue

导引篇

1.1智能制造,是制造还是思维

1.2何谓智能制造的核心

1.3从大数据到智能制造

1.4大数据推动智能制造的三个方向

1.5智能制造在发达国家的转型

1.6未来智慧工厂的无忧虑制造

1.7从产品制造到全生命周期价值创造——给“蛋黄”配“蛋白”

1.8工业大数据的机遇与挑战

案例分析篇

2.1利用大数据分析,实现从解决问题到避免问题

2.2利用大数据预测隐性问题,实现生产系统的自省性

2.3利用知识产生可执行的设计和制造数据的反向工程

2.4基于大数据产品服务系统解决方案

专家访谈篇

李杰教授采访著名经济学家马光远

李杰教授采访航空发动机专家王安正

《福布斯》(中文版)总编康健采访李杰教授

李杰教授采访三一重工副总裁贺东东

李杰教授采访NI 行业市场经理崔鹏

参考文献

Introduction

在西方国家有这样一句话:To live well,a nation mustproduce well,说明制造业是一个国家综合国力最重要的体现,也是决定民众生活质量的重要条件。在经历了互联网泡沫和经济危机之后,世界各国,尤其是发达国家开始重新意识到制造业的重要性,也在重新审视自身竞争力的优劣势。第四次科技革命的到来为各个国家提供了发展和转型的机遇,也使他们面临竞争力格局变化的挑战,智能制造成为世界各国竞争的新战场。无论是德国提出的“工业4. 0 国家战略”,美国提出的“国家制造业创新网络(NNMI)计划”,或是日本的“工业价值链计划(IVI)”等,无不围绕着制造业这个核心。中国改革开放三十多年来,综合国力和人民生活水平的提升过程中,制造业的快速发展起到了决定性的作用,中国成为世界制造业的新中心,也连续几年成为“世界制造力竞争指数”最强的国家。在新一轮的制造业革命中,中国也感受到来自世界各国新技术战略的压力,相继提出“中国制造2025”,“互联网+ ”和“供给侧改革”等多项措施。

每一次制造革命的进步,除了我们能够可见的技术要素以外,更重要的是这背后的制造哲学的进步。现代制造业从第二次科技革命到现在,经历了标准化、合理化+ 规范化、自动化+集成化、网络化+ 信息化四个阶段。这背后的制造哲学可以概括为:以低成本生产高质量的产品;通过全流程改善降低浪费、次品和事故;通过产品全生命周期的数据管理,为用户提供所需要的能力和服务。在以上几个阶段的基础上,现在的制造系统正处在向智能化+ 客制化迈进的阶段,目标是实现零故障和预测型的生产系统,并在无忧的生产环境中以低成本快速实现用户的客制化需求。

那么,如何实现智能制造?有些人说大数据是实现智能制造的核心技术,也有人说要靠互联网、信息物理系统技术(CPS),或是人工智能和机器人等。如果大数据是智能制造的核心驱动力,那么我们该怎么去定义和使用大数据?关于这个问题,我在《工业大数据》这本书中曾表达过一个观点:大数据并不是目的,而是看待问题的一种途径和解决问题的一种手段。通过分析数据,可以预测需求、预测制造、解决和避免不可见问题的风险,和利用数据去整合产业链和价值链,这才是大数据的核心目的。

大数据与智能制造之间的关系可以总结为:制造系统中问题的发生和解决的过程中会产生大量数据,通过对这些数据的分析和挖掘可以了解问题产生的过程、造成的影响和解决的方式,这些信息被抽象化建模后转化成知识,再利用知识去认识、解决和避免问题,核心是从以往依靠人的经验(experiencebased),转向依靠挖掘数据中隐性的线索(evidence based),使得制造知识能够被更加高效和自发地产生、利用和传承。因此,问

题和知识是目的,而数据则是一种手段。今天我们来谈利用大数据实现智能制造,是因为大数据已经成为一个日益明显的现象,而在制造系统和商业环境变得日益复杂的今天,利用大数据去解决问题和积累知识或许是更加高效和便捷的方式。

大数据的目的并不是追求数据量大,而是通过系统式地数据收集和分析手段,实现价值的最大化。所以推动智能制造的并不是大数据本身,而是大数据的分析技术。在新制造革命的转型中,是否能够更加有效地利用好大数据,决定了能否在竞争中脱颖而出。在现在的制造中,存在着许多无法被定量、无法被决策者掌握的不确定因素,这些不确定因素既存在于制造过程中,也存在于制造过程之外的使用过程中。前三次工业革命主要解决的都是可见的问题,例如避免产品缺陷、避免加工失效、提升设备效率和可靠性、避免设备故障和安全问题等。这些问题在工业生产中由于可见可测量,往往比较容易避免和解决。不可见的问题通常表现为设备的性能下降、健康衰退、零部件磨损、运行风险升高等。这些因素由于其很难通过测量被定量化,往往是工业生产中不可控的风险,大部分可见的问题都是这些不可见的因素积累到一定程度所造成的。

因此,我、倪军教授和王安正教授在本书中阐述了大数据推动智能制造的三个方向:第一个方向是利用数据来了解和解决可见的问题;第二个方向是利用数据来分析和预测不可见的问题,从仅仅明白解决问题的“knowhow”,进一步理解问题产生的原因,从而避免可见的问题;第三个方向则是从数据中挖掘新的知识,再利用知识去重新定义问题,使得可见或不可见的问题都可以在制造系统中避免。在第一个方向上,许多国家已经有了比较成熟的积累,也形成了各自独特的制造文化,本书中我们会为读者详细解读这些国家的经验和得失。在第二个和第三个方向上,我们也做了许多年的研究和应用,形成了一套较为完整的体系和方法论,在本书中也会结合案例为读者进行详细介绍。借助本书,我们不仅要向读者介绍大数据和智能制造的技术,更重要的是传达一种思维方式,以及对智能制造的理解、解决问题的逻辑和重新定义制造的思考方式。

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