工业4.0：物联网与智能工厂的革命

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"Industry 4.0: The Industrial Internet of Things" 是一本由 Alasdair Gilchrist 撰写的书籍，深入探讨了制造业和生产行业的现状以及如何通过技术革新来重振曾经的工业强国。本书关注的重点是工业互联网（Industrial Internet）和工业物联网（Industrial Internet of Things，简称IIoT），这两个概念是第四次工业革命的关键驱动力。在书中，读者将了解到： 1. 工业互联网和工业物联网的定义：它们是如何将智能设备、传感器、数据分析和自动化结合在一起，以提高生产效率和制造过程的灵活性。 2. 实现Industry 4.0所需的技术进步：包括物联网（IoT）、大数据（Big Data）、赛博物理系统（Cyber-Physical Systems，CPS）和智能工厂技术等，这些技术有望替代劳动力成本较低地区的传统机械化和人力密集型生产模式。 3. 当前正在发生的转变：书中列举了实际案例，展示如何将这些新技术应用于现实生产环境。 4. 如何应用这些案例研究：为读者提供实施指导，帮助他们在自己的业务中利用这些技术。 5. 制造业的未来潜力与可能的后果：探讨了通过Industry 4.0重新夺回制造业领导地位的可能性，以及这一过程中可能出现的社会经济影响。本书适合以下人群阅读： - 商业未来学家 - 商业策略师 - CEO和CTO - 对IT和商业背景感兴趣的人士 - 关注未来二十年IT、工业和生产领域发展趋势的任何人书中的章节涵盖了从基础介绍到具体技术实现的广泛内容： 1. 工业互联网导论 2. 工业互联网用例分析 3. 工业互联网的技术和商业创新者 4. IIoT参考架构 5. 设计工业互联网系统 6. 访问网络技术和协议的探讨 7. 中间件传输协议的分析 8. 中间件软件模式 9. 软件设计概念 10. IIoT中间件平台 11. IIoT广域网技术和协议 12. 工业互联网的安全保障 13. 引入Industry 4.0的概念 14. 智能工厂 15. 通往Industry 4.0的路线图：从当前状态到未来实现的路径规划通过本书，读者不仅可以理解Industry 4.0的理论框架，还能掌握实际操作中的技术细节，为应对未来的工业变革做好准备。

Chapter 1 | Introduction to the Industrial Internet

of them all, the Industrial Internet of Things, which encompasses a vast amount

of disciplines such as energy production, manufacturing, agriculture, health

care, retail, transportation, logistics, aviation, space travel and many more.

Figure 1-1. Horizontal and vertical aspects of the Internet of Things

In this book to avoid confusion we will follow GE’s lead and use the name

Industrial Internet of Things (IIoT) as a generic term except where we are

dealing with conceptually and strategically different paradigms, in which case it

will be explicitly referred to by its name, such as Industry 4.0 .

Many industrial leaders forecast that the Industrial Internet will deliver unprec-

edented levels of growth and productivity over the next decade. Business

leaders, governments, academics, and technology vendors are feverishly work-

ing together in order to try to harness and realize this huge potential.

From a financial perspective, one market research report forecasts growth

of $151.01 billion U.S. by 2020, at a CAGR of 8.03% between 2015 and 2020.

However, in practical terms, businesses also see that industrial growth can

be realized through utilizing the potential of the Internet. An example of this

is that manufacturers and governments are now seeing the opportunity to

reindustrialize and bring back onshore, industry, and manufacturing, which had

previously been sent abroad. By encouraging reindustrialization, governments

hope to increase value-add from manufacturing to boost their GDPs.

The potential development of the Industrial Internet is not without prece-

dence, as over the last 15 years the business-to-consumer (B2C) sector via

the Internet trading in retail, media, and financial services has witnessed stellar

growth. The success of B2C is evident by the dominance of web-scale giants

Industry 4.0

born on the Internet, such as Amazon, Netflix, eBay, and PayPal. The hope

is that the next decade will bring the same growth and success to indus-

try, which in this context covers manufacturing, agriculture, energy, aviation,

transportation, and logistics. The importance of this is undeniable as industry

produces two-thirds of the global GDP, so the stakes are high.

The Industrial Internet, however, is still in its infancy. Despite the Internet

being available for the last 15 years, industrial leaders have been hesitant to

commit. Their hesitance is a result of them being unsure as to how it would

affect existing industries, value chains, business models, workforces, and ulti-

mately productivity and products. Furthermore, in a survey of industry busi-

ness leaders, 87% claimed in January 2015 that they still did not have a clear

understanding of the business models or the technologies.

This is of course to be expected as the Industrial Internet is so often described

at such a high level it often decouples the complexities of the technologies that

underpin it to an irrelevance. For example, in industrial businesses, they have

had sensors and devices producing data to control operations for decades.

Similarly, they have had machine-to-machine ( M2M ) communications and col-

laboration for a decade at least so the core technologies of the Industrial

Internet of Things are nothing new. For example, industry has also not been

slow in collecting, analyzing, and hoarding vast quantities of data for histori-

cal, predictive, and prescriptive information. Therefore the question industrial

business leaders often ask is, “why would connecting my M2M architecture to

the Internet provide me with greater value?”

What Is the Industrial Internet?

To explain why businesses should adopt the Industrial Internet, we need to

first consider what the IIoT actual is all about. The Industrial Internet pro-

vides a way to get better visibility and insight into the company’s operations

and assets through integration of machine sensors, middleware, software,

and backend cloud compute and storage systems. Therefore, it provides a

method of transforming business operational processes by using as feedback

the results gained from interrogating large data sets through advanced analyt-

ics. The business gains are achieved through operational efficiency gains and

accelerated productivity, which results in reduced unplanned downtime and

optimized efficiency, and thereby profits.

Although the technologies and techniques used in existing machine-to-machine

( M2M ) technologies in today's industrial environments may look similar to the

IIoT, the scale of operation is vastly different. For example, with Big Data in

IIoT systems, huge data streams can be analyzed online using cloud-hosted

advanced analytics at wire speed. Additionally, vast quantities of data can be

stored in distributed cloud storage systems for future analytics performed in

Chapter 1 | Introduction to the Industrial Internet

batch formats. These massive batch job analytics can glean information and

statistics, from data that would never previously been possible because of the

relatively tiny sampling pools or simply due to more powerful or refined algo-

rithms. Process engineers can then use the results of the analytics to optimize

operations and provide the information that the executives can transform to

knowledge, in order to boost productivity and efficiency and reduce opera-

tional costs.

The Power of 1%

However, an interesting point with regard to the Industrial Internet is what

is termed the power of 1% . What this relates to is that operational cost/in-

efficiency savings in most industries only requires Industrial Internet savings

of 1% to make significant gains. For example, in aviation, the fuel savings of

1% per annum relates to saving $30 billion. Similarly, 1% fuel savings for the

gas-fired generators in a power station returns operational savings of $66

billion. Furthermore, in the Oil and Gas industry, the reduction of 1% in capi-

tal spending on equipment per annum would return around $90 billion. The

same holds true in the agriculture, transportation, and health care industries.

Therefore, we can see that in most industries, a modest improvement of 1%

would contribute significantly to the return on investment of the capital and

operational expenses incurred by deploying the Industrial Internet. However,

which technologies and capital expenses are required when initiating an IIoT

strategy?

Key IIoT Technologies

The Industrial Internet is a coming together of several key technologies

in order to produce a system greater than the sum of its parts. The latest

advances in sensor technologies, for example, produce not just more data

generated by a component but a different type of data, instead of just being

precise (i.e., this temperature is 37.354 degrees). sensors can have self-aware-

ness and can even predict their remaining useful life. Therefore, the sensor

can produce data that is not just precise, but predictive. Similarly, machine sen-

sors through their controllers can be self-aware, self-predict and self-compare.

For example, they can compare their present configuration and environment

settings with preconfigured optimal data and thresholds. This provides for

self-diagnostics.

Sensor technology has reduced dramatically in recent years in cost and size.

This made the instrumentation of machines, processes, and even people finan-

cial and technically feasible.

Industry 4.0

Big Data and advanced analytics as we have seen are another key driver

and enabler for the IIoT as they provide for historical, predictive, and pre-

scriptive analysis, which can provide insight into what is actually happening

inside a machine or a process. Combined with these new breed of self-aware

and self-predicting components analytics can provide accurate predictive

maintenance schedules for machinery and assets, keeping them in produc-

tive service longer and reducing the inefficiencies and costs of unnecessary

maintenance. This has been accelerated by the advent of cloud computing

over the last decade whereby service providers like AWS provide the vast

compute, storage, and networking capabilities required for effective Big Data

at low cost and on a pay-what-you-use basis. However, some risk-adverse

companies may prefer to maintain a private cloud, either on their own data

centers or in a private cloud.

Why Industrial Internet and Why Now?

To comprehend why the Industrial Internet is happening today, when its tech-

nologies have been around for a while, we need to look at legacy system

capabilities and inefficiencies.

One assumption is that the complexity of industrial systems has outpaced the

human operator’s ability to recognize and address the efficiencies, thus making

it harder to achieve improvements through traditional means. This can result

in machines operating well below their capabilities and these factors alone are

creating the operational incentives to apply new solutions.

Furthermore, IT systems can now support widespread instrumentation,

monitoring, and analytics due to a fall in the costs of compute, bandwidth,

storage, and sensors. This means it’s possible to monitor industrial machines

on a larger scale. Cloud computing addresses the issues with remote data

storage; for example, the cost and capacity required to store big data sets.

In addition, cloud providers are deploying and making available analytic tools

that can process massive amounts of information. These technologies are

maturing and becoming more widely available, and this appears to be a key

point. The technologies have been around for a while and have been adopted

by IT—cloud adaptation and SaaS are prime examples of this. However, it is

only recently that industrial business leaders have witnessed the stability and

maturity of solutions, tools, and applications within these IT sectors reach a

level of confidence and lessen concerns.

Similarly, the maturity and subsequent growth in networks and evolving

low-power radio wireless wide area networks ( WWAN ) solutions have

enabled remote monitoring and control of assets, which previously were

simply not economical or reliable enough. Now these wireless radio networks

have reached a price point and a level of maturity and reliability that works

Chapter 1 | Introduction to the Industrial Internet

in an industrial environment. Together these changes are creating exciting

new opportunities when applied to industrial businesses, machines, fleets, and

networks.

The decline in the cost of compute, storage, and networks is a result of the

cloud-computing model , which allows companies to gather and analyze much

larger amounts of data than ever before. This alone makes the Industrial

Internet an attractive alternative to the exclusive M2M paradigm.

However, the Industrial Internet has its own issues, which may well act as

severe countermeasures to adoption. These are termed catalysts and precursors

to a successful Industrial Internet deployment.

Catalysts and Precursors of the IIoT

Unfortunately, there are several things an IIoT candidate business simply must

have in place before embarking on a serious deployment, discussed in the

following sections.

Adequately Skilled and Trained Staff

This is imperative if you expect to benefit from serious analytics work as

you will certainly need skilled data scientists, process engineers, and electro-

mechanical engineers. Securing talent with the correct skills is proving to be

a daunting task as colleges and universities seem to be behind the curve and

are still pushing school leavers into careers as programmers rather than data

scientists. This doesn’t seem to be changing anytime soon. This is despite

the huge demand for data scientists and electro-mechanical engineers

predicted over the next decade. The harsh financial reality is that the better

the data analytical skills, the more likely the company can produce the algorithms

required to distil information from their vast data lakes. However, this is not

just any information but information that returns true value, aligned to the

business strategy and goals. That requires data scientists with expert business

knowledge regarding the company strategy and short-medium-long term

goals. This is why there is a new C-suite position called the Chief Data Officer.

Commitment to Innovation

A company adopting IIOT has to make a commitment to innovation , as well

as taking a long-term perspective to the IIoT project’s return on investments.

Funding will be required for the capital outlay for sensors, devices, machines,

and systems. Funding and patience will be required as performing the data

capture and configuring the analytics’ parameters and algorithms might not

result in immediate results; success may take some time to realize. After all,

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