INDUSTRY 4.017 min read

INTRODUCTION

The first article that I deal with in these pages is an article on this new industrial revolution, which is affecting the current industrial sector: INDUSTRY 4.0.

I start with this topic because it is a topic that affects many fields of electronics: automation, robotics, information security, communication, management of large amounts of data (BIG DATA), energy and artificial intelligence. This article will be deliberately descriptive because it is an overview of the fourth industrial revolution . The following articles will delve into the various topics to give substance to the information that this site will contain.

L ‘INDUSTRY 4.0 is called so because it is the fourth industrial revolution, and by analogy we can define the three previous revolutions as well: below an illustration of the four stages of industrial development

INDUSTRY 1.0 [1]

The first industrial revolution took place in the 1700s, with the first mechanical looms and the first steam engines. Steam was the first non-animal source of energy that allowed us to “animate” machines to help man in production.

INDUSTRY 2.0 [2]

The second industrial revolution took place in the second half of the 1800s. This was a period of strong innovation, scientific and otherwise, thanks to the spread of electricity. This gave way to mass production: with the new discoveries, it simplifies the supply of energy that leads to the improvement of production machines, making them more and more complex.

INDUSTRY 3.0 [3]

T he third industrial revolution began in the second half of the ‘900, after the end of the second G uerra M ondiale. The accumulation of scientific and technological knowledge and the most stable political conditions favors Western countries scono economic growth. ( In this period there is the creation of the first telematic network between the Standford Research Institute and the University of Utah, ARPANET ) In a short time the computer becomes a tool that everyone can afford, so the technology that was previously used exclusive to industrial and state giants (such as universities), it is now accessible to all. At the same time the diffusion of industrial automation kicks off.

INDUSTRY 4.0 [4]

The fourth industrial revolution (which forecasts began in 2010) opens up interconnected digital systems.

INTERCONNECTED SYSTEMS

What is meant by “interconnected systems“? With this concept we begin to speak of communication, the communication affects all electronics and dell ‘IT branches, precisely because the only passage of a bit from the CPU to the RAM can be considered a transmission of a given X from one device to another . This transmission takes place with rules that govern the storage of data in the memory, such as the selection of the memory area where to store the data, or if you want to write or read, or to know if the memory is ready to receive the data .

Subsequently, as the distance between the devices increases, certain certainties begin to be lacking, such as the fact that the data has arrived intact and not corrupted. This is why communication protocols have been introduced . By interconnected systems we mean systems that can communicate with each other regardless of the distance they are located, through these predefined protocols.

We can say that the way to this interconnection was given with the advent of the IoT (Internet of Thing – the Internet of things) with which we started to interconnect various devices, such as appliances, devices to monitor the environment or our physiological functions, which collect and send information on their operation, data and much more through our mobile phones and clouding.

To switch to industrial applications, communication had to be made safer, moving from protection against electromagnetic disturbances to data security. From this was born the IIoT (Industrial Internet of Thing – Internet of industrial things) [5] .

It must be premised that until today the industrial machines (except for a few exceptions) have been produced to work autonomously without external interference, if not that of the operator who interacts with the machine to take the finished product and supply it with raw product or raw material and enter the data for processing. A ‘have with the exception of the production lines, where we have several machines in line, with the raw material inserted at the beginning of the line and the finished product or semi-finished product in the end. Here all the machines are in communication with each other (let’s say a basic communication, through inputs and outputs) in order to coordinate to keep the product on the line.

With the implementation of communication (communication protocols) you can get a lot of information regarding the status of the machine, such as the number of pieces produced, machine stops due to emergency or breakdown, the status of the engines or components, the reasons why it produced more or not yesterday, etc … In practice these are all information that will be available for a subsequent analysis or in real time of the machine status. However, this amount of data has a downside: it must be analyzed. This means that in the company there must be personnel specialized in data analysis (BIG DATA).

Moreover, having a real-time analysis of the state of the machine and above all having a lot of information available from other plants (in the cloud), the solution to a possible problem may already be available and this would further reduce downtime. Having professionals able to analyze the data is of vital importance, because implementing a structure like this and then not being able to analyze the data is a loss already at the start.

Starting the path of industry 4.0 is not easy! In addition to the speech of qualified personnel there is also all the technological update to be performed . For this, some guide points have been drawn up that indicate a sure way to reach the goal of Industry 4.0 [6]

1. Set goals according to your level of digitization
2. Start with pilot projects
3. Define the required skills: according to point 2 it is necessary to define the level of technical skills of the personnel
4. Familiarity with data analysis
5. Transform the company into a digital company
6. Interacting with the system (without interaction it makes no sense to talk about Industry 4.0)

Computer security [5] [7] [8]

The spread of IIoT (or rather IoT) involves some pitfalls like energy expenditure and security : two points of fundamental importance.

The security that until or oday was relegated to servers and computers, with industry 4.0 must be implemented in industrial systems, as with communication expanding at all levels, a lack of or incorrect information can undermine productivity creating many problems. Communication must be fast and reliable, because the data arriving at the destination must be complete, but above all not corrupted or modified. When we talk about information theft, we associate the concept with industrial espionage, but in addition to this the importance of security is that the data arrives complete and not modified or corrupted, because a wrong datum can have negative consequences on production, like producing lots wrong, or send undetected malfunction messages . All possibilities that affect business productivity and costs.

Communication [9]

Since communication is the backbone of industry 4.0, it must be fast and reliable: the data must arrive at full destination as quickly as possible, as any data lost or delayed could lead to a wrong action, and therefore a possible machine downtime planned or a chain of delays, slowing down production or producing products that are unsuitable for sale . I l it all translates into a loss of profit.

Communication between devices requires that each individual component of the network be able to interpret various communication protocols, to ensure that these systems can be integrated into various environments and, above all, reduced to a minimum as their energy consumption must be minimal. For this reason, in my opinion, everything will flow towards a single standard communication protocol, reducing the components required for communication and thus reducing energy consumption.

This communication protocol, like all communication protocols, dev and satisfy the following points:

●        security: as previously mentioned, communication must be secure, thus preventing corruption or data theft.

●        low energy consumption: as the devices (sensors or actuators) are ever smaller and more independent, their energy consumption must be very low. This is not only for a matter of energy saving, but also because these small-sized devices can be used in places that are difficult to access, where interventions for renewing the energy source can be reduced to a minimum.

●        robust ness: in the event of problems in the system, communication must always be active, or at least be able to provide the information needed for troubleshooting.

●        easy to implement: it means that the hardware and software components must be easily available and easy to implement, as well as the addition of new nodes or the removal of old ones.

The Clouding [9]

Another factor introduced with industry 4.0 is clouding, ie the sharing of information on the net. This sharing is a fundamental point, as their processing in real time can anticipate and avoid downtime. This implies that for some (if not all) of the problems of the production line or machine, they could already have a solution, giving way to that whole process of resolution and planning of the machine downtime obtaining a consequent reduction of machine downtime due to faults sudden.

Machine downtime is not only due to a fault, but can also arise from incorrect processing or incorrect information. This information is generally entered by the operator and here the user interface (HMI) comes in handy, which in symbiosis with the clouding checks and verifies the correctness in the input of the processing data, anticipating and preventing errors, bringing to zero (in theoretical line) the error introduced by man.

Interfaces (HMI) [9]

Human-machine interfaces are increasingly complex because they have to perform complex tasks, but at the same time simplify visual information and data collection, so that the interpretation of the required data is facilitated to the operator. Increasingly complex interfaces make it possible to automate data entry, machines become increasingly autonomous and production can literally change. It can be assumed that the old concept of spreading tooling costs on production (the more pieces I make in one lot, the less they cost me), can be abandoned to start talk about batches from a product, making customization extreme for the customer. An example of easy intuition can be the assembly line of a car, in which the customer can decide what accessories to have: this level of customization is already present, but a level of extreme variety can be reached, from the choice of color to the number of speakers in the cockpit, all not supplied through packages, which oblige the customer to accept various devices in order to have that particular accessory.

Smart sensors and factories [10] [11] [12]

In order to obtain the automation level mentioned in the previous section, an improvement of the sensor part is required: in fact, without intelligent sensors, many of the functions (especially diagnostics) cannot be performed . Sensors that collect data on the behavior of the machine and anticipate any break (Predictive Maintenance), sensors capable of monitoring and signaling an anomalous variation of one or more values, with in addition a low energy profile so as to be used in poor environments accessible, that are able to communicate with various devices, until you can take advantage of a communication standard for everything: for this it is necessary to develop more or less complex software for these sensors, precisely, defined as intelligent sensors.

Moreover with these intelligent sensors it will be possible to carry out workings aimed at the batch of a component, for example it will be possible to determine the type of bottle and where to send it to pack it.

Of course this means that the decision-making responsibility is no longer tied to the centralized controller, but is delegated to these sensors located in the plant, sensors with predictive software for data analysis, which do not send all the amount of data collected, but only the result of their elaborations (this is always a speech in terms of low energy profile).

These sensors must be managed and the PLC is the tool to perform this task. Today, a PLC is equipped with multiple communication protocols, so it can be integrated into a corporate LAN allowing data sharing and collection. This sharing and collection gives the advantage of having a database available which can be consulted when there are any anomalies, to find a solution and reduce machine downtime.

With industry 4.0 we start talking about an intelligent factory , but besides being an almost utopian discourse, given that the road is still long and full of many hardships, companies are forced to be eclectic, due to the variety / quantity of skills that come into play, and must be able to formalize the decision-making process in order to provide the right solution: an incorrect decision could lead to unscheduled production blocks.

AI – Artificial intelligence

At this point we introduce the concept of artificial intelligence , useful because after all the references made it has always been mentioned that with industry 4.0 an analysis or diagnostics of the data provided and an automation to perform the operations are required. Industry 4.0 uses automation and robotics to perform its operations, but without artificial intelligence and data the IIoT is lame: the AI ​​processes all the data collected by making predictions about the behavior of the machine over time and predicting a failure. This means that machine downtimes can be reduced to zero. Moreover with the AI ​​the industry will no longer have to depend on the collection of data by man, but rather will be autonomous in the collection limiting to zero any errors introduced by man. Here we introduce the concept of predictive diagnosis , which we will address in one of the next articles, which is based on data analysis and by means of predictive algorithms performs a prediction on possible future failures. All this will be possible because the analysis of data over time of a given component, such as increased operating temperatures, increased vibrations, currents, heat dissipation, magnetic fields, allow the software, with the help of the cloud, to determine when the component will fail or arrive at the end of its life.

Starting from the assumption that the error will never be null because man is naturally subject to error, every product of man is potentially affected by congenital errors, so even AI is affected by this problem precisely because it is developed by man. This is why neural networks and machine learning are coming to the fore with artificial intelligence, which will allow the machines to correct their congenital errors, due to man’s programming. Here we enter into philosophical theories about the presence or absence of errors even in neural networks, but which we will face in due course.

Cobot [13]

Cobot or collaborative robots, from research done, is seen from the collaboration between man and machine would reduce non-production time by 85%. Currently the gap between A.I. artificial intelligence is wide, precisely because a worker immediately realizes that a piece is damaged or has changed, while a robot must be instructed or have instructions to manage these inconsistencies, otherwise it will continue to perform its work without noticing the change . But instructions or learning period to manage these eventualities the attention is a long time in which the production is stopped, producing inoperative costs. That said the intelligence gap relegates the cobots to an area of ​​habitual tasks, while the man takes care of those tasks that require more intelligence. This means that cobot and man must work in close contact, and this raises many security problems, which slow down the integration between man and machine. In support of this limit (even if not negative) international standards are emerging which provide for a definition of the requirements for safe operation of cobots, these standards are ISO / TS 15066: 2016, ANSI / RIA R15.06-2012. An aid to the development of cobots can be given by the experience accumulated in the development of robotic prostheses for the disabled, such as Force-Feedback.

Conclusions

in conclusion, in industry 4.0 we talk about communication, artificial intelligence, neural networks, diagnostics and maintenance, IT security, automation clouding, robotics, big data and much more.

All arguments that in these first pages I have only mentioned and, as repeated several times, I will deepen in the next articles. One thing I wanted to highlight is that I didn’t read from many parts is diagnostics. In all the articles we find we talk about security, artificial intelligence, man-machine interface, etc. etc., but little and rarely we find references to diagnostics for fault detection. In my opinion a vital topic in the fourth industrial revolution, because if vo gl iamo minimize downtime, we must have a diagnostic method and anomaly detection so that during the software development are incorporated these concepts and d sensors they will be able to perform more accurate diagnoses.

Another point on which I would like to leverage is the analysis of data, this is also very important, because with these new types of sensors, as already said several times, the data will be in large quantities, precisely because the more data we have / we analyze the better we succeed to have the complete picture of the situation. A complete picture of the situation will make it possible to make more accurate and efficient decisions, increasingly improving the level of production.

BIBLIOGRAPHY – SITOGRAFIA

●        [1] First Industrial Revolution

●        [2]   Second industrial revolution

●        [3] Digital revolution

●        [4] Fourth industrial revolution

●        [5] EOS Book # 31 – Safety in industry 4.0 – author Maurizio Di Paolo Emilio – EOS – Open Source Electronics

●        [6] Firmware 130 – Industry strategies 4.0 – author Slovati EOS – Open Source Electronics

●        [7] Firmware 142 – Connectivity and security in the IoT – author Maurizio Di Paolo Emilio – EOS – Open Source Electronics

●        [8] Firmware 145 – Security in a smart factory 4.0 – author Maurizio Di Paolo Emilio – EOS – Open Source Electronics

●        [9] Firmware 130 – Industry 4.0: From automation to digitization processes – author Roberta Fiorucci – EOS – Open Source Electronics

●        [10] Firmware 130 – The sensors in the industry 4.0 – author Slovati – EOS – Open Source Electronics

●        [11] 145 firmware – MEMS sensors for the digitalization of the Industry – author Maurizio Di Paolo Emilio – EOS – Open Source Electronics

●        [12] Firmware 152 – The needs of future sensors for maintenance: intelligent sensors for condition monitoring – author Analog Devices – EOS – Open Source Electronics

• [13] Firmware 149 Cobot Bridging the gap with artificial intelligence in the industrial automation sector – author Mouser Electronnics – EOS – Open Source Electronics

REVISIONS

• April 13, 2020 Add link to new article on digital comunication
• October 11, 2019 Add link to new article on Diagnostic
• August 19, 2019 Section Cobot
• July 09, 2019 Correct some links
• July 07, 2019               Publication