Industrial Automation

Connecting Industrial Protocols and the Cloud
admin
November 22, 2022
Big Data, Cloud Computing

Why Connect Industrial Protocols with Cloud

Industrial protocols are conversations between industrial automation products for data collection or control. At the beginning of industrial automation, communications were a competitive differentiator, and automation vendors developed communication protocols to leverage technical advantage and lock in their customer base. It has changed with time; today, vendors have extended their protocols and even designated them industry standards to boost adoption. Vendors acknowledged that suppliers with the largest ecosystem of products to choose from, would have a better livelihood of winning parts of a project, if not the complete project. Vendors also learned that it is challenging to be a specialist in all areas of automation. Let’s find out different industrial protocols and those that can be compatible with cloud applications.

Different Types of Industrial Protocols

With time, the manufacturing marketplace has become prevailing by a set of protocols, possibly from the leading suppliers of automation products. Before examining the best-suited for the cloud, let’s know some of the most common industrial protocols. These include protocols such as Modbus, Profinet, CC-Link, Ethernet IP, etc. Many of these are present in different forms to acknowledge varying topologies and purposes eg-dedicated wires vs. Ethernet.

Attempt to bring standardization over the years fetched technology from the OPC Foundation, which was originally Microsoft technology-based, using COM and DCOM Windows technologies for communications between applications. Hence, OPC (OLE for Process Control – OLE that is, Object Linking and Embedding – the technology after COM) is delivered.

1: OPC

OPC obtained standards for accessing data, either subscribing or polling, and the purpose of different data types and how to manage them (Analog and Discrete variables, History Data, Alarms, and Events, among others).

In time, this standardization endeavor shifted from windows technology-centric to operating system-agnostic to aiding Linux and delivering functionality that would be useful to Internet-based communications.

2: OPC UA

The new standard was recognized as OPC UA- with OPC now representing Open Process Communications and UA representing Unified Architecture, one standard to supersede the previous standards that had developed.

3: MQTT

Another technology that is more concentrated on the transfer of messages and less on the content of messages generated out of the need for a very distributed infrastructure with limited bandwidth, as found in the upstream oil and gas market. This protocol is known as MQTT. It is used in the industrial automation marketplace, specifically for cloud communications, and has become very popular in recent years.

4: BACnet

The vertical market shows unique requirements and has supported the requirement for unique developments. BACnet is the leading protocol in the Building Automation Systems (BAS) space. In the Power Generation and Distribution Space, several protocols like IEC-61850, 60870, and DNP-3.

Over time, these protocols have survived on various topologies, and today most of them offer Ethernet compatibility.

Why is the Cloud So Important?

The advantages of cloud computing are numerous and stimulating. They possess:

  • Transformation of capital expenditures to operational expenditures
  • No need to concentrate on infrastructure management
  • Benefiting a constantly scalable architecture
  • Furnishing accessibility to your absolute organization, anywhere and anytime
  • Benefiting services from domain experts (security, upgrades, solution development)

The cloud can endure different forms, from a solution delivery by industry leaders like Microsoft and Amazon to more scaled offerings for targeted markets. Ultimately, there are hosted solutions, pushing on-premise servers to virtual servers in the cloud, but fully controlled by the IT staff of the organization.

The objective of cloud computing is to provide a lower total cost of ownership by reducing expenses in system management and hardware ownership and the capability to take advantage of solutions offered by others. These third-party solutions are usually built for market purpose and provides multi-tenant capability, letting the service provider handle many customers whilst offering data and user isolation. The concept of cloud computing, specifically for the industrial marketplace, is still in its initial stage, and businesses are fighting with cloud connectivity and the idea of hosting their data to the outside world.

However, the benefits are convincing as it reduces operating costs, and domain experts have developed vertical market applications that require connectivity to the correct data. Additionally, service providers can utilize knowledge gained over their extensive array of customers and offer great value to an individual customer. So, the failure mode of a product in an environment can be predicted by learning about the failure mode in other environments. It helps in potential predictive analytics tuned by the results and anonymization of data from a similar ecosystem of users. While connecting to the cloud, evaluating which industrial protocols best suit the application is necessary.

Things to Consider When Connecting to the Cloud

The best attributes offered by cloud-based solutions fall into two main categories:

  1. Security ( including access security and cybersecurity)
  2. Transmission (the quality and reliability of data) 

Security is mainly managed using VPNs (Virtual Private Networks). It is an excellent way for bi-directional and ad-hoc communications as it is designed for remote troubleshooting. Using VPNs for ad hoc access, customers can use solutions to secure and broker access to endpoints in a very organized and controlled way. It includes approval processes, windows of access and time limitations, and extra levels of authentication. 

For information communication to the cloud, it is becoming more prevalent to utilize public-subscribe models and connection brokers to enhance security. Remote sites will share data to a tight and secure connection. The users of data and cloud applications will subscribe to the data through a broker, eradicating application knowledge of remote communication details that illustrate an exposure. Microsoft IoT Hub is the best example of this technology. 

Industrial Protocols for Cloud Connectivity

It is optional that all industrial protocols are compatible. Without knowing each protocol and determining if it can be integrated into a cloud, a complete solution to the connectivity issue is to add edge device technology. It will manage the communications to the IT and OT environment and the need for cloud data transfer. Their devices are now covering the market with specific cloud connectivity or a toolkit approach that can be eased their configuration. Most of them are designed with data transfer as their primary function, whereas others support data modeling, visualization, and analytics, in addition to data transfer.

Ethernet is also improving with time in both topology and performance. A more visible improvement is device synchronization and the power to shape traffic. These attributes and other things are Ethernet enhancement called TSN (Time Sensitive Networking). TSN promises the skills to prioritize communications on Ethernet and control traffic bandwidth.

Connecting Safely and Securely

With the expansion of industrial protocols in the market, it is now feasible and easy to connect virtually any automation solution to the cloud with complete privacy, directly or using edge gateways.

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admin
October 13, 2022
Artificial Intelligence, Technology

How can Artificial Intelligence Boost the Manufacturing Industry?

Today, most of the Giant industries, around 83 percent, believe that AI delivers better outcomes; however, only 20 percent have embraced this technology. It is pretty clear that a stronghold on the domain is important for successfully adopting artificial intelligence in the manufacturing industry.

Domain expertise is important for successfully adopting artificial intelligence in the manufacturing industry. Jointly, they form Industrial AI that uses machine learning algorithms in domain-specific industrial applications. AI can be potentially used in the manufacturing industry through machine learning, deep learning, and computer vision.

Let’s check out some of the critical needs in artificial intelligence technologies in the manufacturing industry to obtain a better picture of what one should do to keep the business up-to-date and seamless.

AI Is a Broad Domain

Artificial intelligence is not the correct way to describe all the technologies, and we’ll discuss that they have applications in manufacturing industries. AI is a big subject with different methods and techniques that fall under it.
There are robotics, natural language processing, machine learning, computer vision, and many other technologies that also need attention.

Keeping this in mind, let’s begin with artificial intelligence applications in the manufacturing industry. So here are some industrial uses of AI.

The Goal of AI in Manufacturing

Artificial intelligence studies how machines can process information and make decisions without human interference. The best way to understand this is that AI aims to mimic how humans think but not necessarily. We all know that humans are better and more efficient in performing certain tasks, and in certain tasks, they are not. The best type of AI is one that can think and make decisions rationally and accurately. The best way to explain this is that we all know that humans are not efficient enough to process data and the complex patterns that appear within large datasets.

However, AI can easily do this job using sensor data of a manufacturing machine and pick out outliers in the data that provide information about the machine that will require maintenance in a few weeks. Artificial Intelligence can perform this in a fraction of a human’s time analyzing the data.

Robotics: The foundation of Modern Manufacturing

Many applications of artificial intelligence include software in place of hardware. However, robotics is mainly focused on highly specialized hardware. As per Global Market Insights, Inc, the industrial robotics market is expected to grow more than $80 billion by 2024. In many factories, for instance, Japan’s Fanuc Plant, the robot-to-human ratio is approx 14:1. This reflects that its possible to automate a large part of the factory to reduce product cost, improve human safety and enhance efficiency.

Industrial robotics demands specific hardware and artificial intelligence software to assist the robot in accurately performing its tasks. These machines are specialized and are not in the business of making decisions. They can run under the supervision of technicians, and if not even, they make very few mistakes compared to humans. Since they make very few mistakes, the overall efficiency of a factory improves when integrated with robotics.

When artificial intelligence is integrated with industrial robotics, machines can automate tasks like material handling, assembly, and inspection.

Robotic Processing Automation:

Robotic processing automation, artificial intelligence, and robotics are among the most familiar. It is important to understand that this process is not related to hardware machinery but software.

It involves the principles of assembly line robots for software applications like data extraction, file migration, form completion and processing, and more. However, these tasks do not play very important roles in manufacturing; they are significant in inventory management and other business tasks. It becomes more important if the production operation requires software installations on each unit.

Computer Vision: AI Powering Visual Inspection

Quality control is the manufacturing industry’s most significant use case for artificial intelligence. Even industrial robots can make a mistake, though the possibility is less than humans. It can be a huge loss if a defective product reaches the consumer by mistake. Humans can manually monitor assembly lines and identify defective products, but no matter how attentive they stay, some defective products will always slip through the cracks. Therefore artificial intelligence can help the manufacturing process by reviewing products for us.

Adding hardware like cameras and IoT sensors, products can be interpreted by AI software to catch defects automatically. The computer can then automatically decide what to do with defective products.

Natural Language Processing: Improving Issue Report Efficiency

Chatbots driven by natural language processing is an important manufacturing AI trend that makes factory issue reporting and helps requests more efficiently. It is a domain of AI that specializes in mimicking natural human conversation. Suppose workers can access the devices to communicate and report their issues and questions to chatbots. In that case, artificial intelligence can support them in filing proficient reports more promptly in an easy-to-interpret format. It makes workers more accountable and decreases the load for both workers and supervisors.

Web Scraping:

Manufacturers can use NLP for an improved understanding of data collected with the help of a task called web scraping. AI can check online sources for appropriate industry benchmark information and transportation, labor, and fuel costs. It can help in boosting business operations.

Emotional Mapping:

Machines are quite poor when it comes to emotional communication. It is very challenging for a computer to understand the context of a user’s emotional inflection. However, natural language processing is enhancing this area through emotional mapping. This brings a wide variety of opportunities for computers to understand the feelings of customers and operators.

Machine Learning, Neural Networks, and Deep Learning

The three technologies used in the manufacturing industry are machine learning, neural networks, and deep learning, which are artificial intelligence techniques used for different solutions:

  • Machine Learning: It is an artificial intelligence technique in which an algorithm learns from training data to decide and identify patterns in collected real-world data.
  • Neural Networks: Using ‘artificial neurons,’ neural networks accept input in an input layer. The input is passed to hidden layers that increase the weight of the input and direction to the output layer.
  • Deep Learning: It is a machine learning method where the software mimics the human brain like a neural network, but the information goes from one layer to the next for higher processing.

Future of AI in Manufacturing

What will be the next role of artificial intelligence in manufacturing? There are so many thoughts and visions coming from science and technology. The most visible change will be an increased focus on data collection. AI technologies and techniques used in manufacturing can do so much work independently. As the Industrial Internet of Things grows with increased use and effectiveness, more data can be gathered and then used by AI platforms to improve different tasks in manufacturing.

However, with the advancement in AI in the coming years, we may observe completely automated factories and product designs made automatically with less human interference. However, reaching this point is only possible through continuous innovation. All it requires is an idea- it can be the unification of technologies or using technology in a new case. Those innovations alter the manufacturing market landscape and help businesses stand out.

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How can IoT Sensors Improve Productivity in Manufacturing
admin
April 18, 2022
IoT

How can IoT Sensors Improve Productivity in Manufacturing?

Internet of Things has been reaching out to almost every sector, and as a result, it is expected that the global IIoT market will reach $103.38 billion by 2026. Today IoT devices are more affordable, and many manufacturers invest in smart factory technology. One of the significant parts of smart factory technology is IoT sensors. It is essential to gather the necessary information and send data to the cloud for analysis in manufacturing. Businesses analyze data collected from the sensors to produce the most fitting solution to enhance productivity, avoid unplanned downtime, and cut manufacturing expenses.

IoT Sensors

In the Industrial Internet of Things, sensors are able to detect different types of external information and change it into data or signals that humans and machines can comprehend. Data is stored in a database which is managed either on the cloud or within the building for processing and analysis. 

IoT sensors employ different types of technology like optics, infrared and thermal to catch the required information. Sensors can also collect one or many kinds of data. Sensors include measuring distance, levels, pressure, environment changes, or anomalies in production line batches.

Types of IoT Sensors

Vision Sensors:

Images are caught by a camera and processed using software to know parts’ presence, orientation, and accuracy. Adoption of vision sensors ensures product quality and consistency throughout batches. It is used chiefly on automotive, food and beverage, and general manufacturing production lines.

Proximity Sensors:

This sensor is used to calculate the distance between two objects. It is used primarily in manufacturing, where machines must know distances between products or measurements for assembly robots.

Pressure Sensors:

This sensor is used to measure the pressure of fluids or gases in an industrial environment. It is vital to maintain the correct pressure for the product quality or safety of the crew.

Temperature Sensors:

The temperature of the component indicates if they are failing or overheating. This can allow the maintenance crew to replace the fault before it results in expensive mechanical failure. Temperature sensors also monitor the ambient temperature to assure the quality of the product or food safety. Instant alert of a cooler going over-regulated temperature helps in saving the unplanned cost of food waste.

Humidity Sensors:

Balanced moisture can be an essential factor contributing to the final product quality. Monitoring the moisture guarantees that quality standards are always fulfilled. 

Humidity also degrades equipment, so this sensor can inform the team if the humidity level gets disturbed. It is crucial to maintain the required moisture to enhance sensitive equipment’s life. 

Level Sensors:

Level sensors alert the team if the fluid or solids level goes down. In this way, it ensures that hoppers are filled before they run out, and production time is not lost.

Acceleration and Vibration Sensors:

It is crucial to monitor the movement of equipment to know the accuracy or need for machinery maintenance—excessive vibration in the machine indicates loose bolts or worn-out bearings or motors that are about to fail.

Sound Sensors:

The pitch of some machinery also indicates whether it is operating correctly or not. By observing the machine’s pitch, the maintenance crew can be informed if the machine is running too high or low and needs repair or replacement.

With the evolvement of IIoT technology, major industrial sensor manufacturers are designing “smart” sensors. These sensors are easy to implement than analogue ones (as it requires PLCs to process and interpret data protocol). A smart sensor is able to process data within the sensor and transmit it directly back to the managing platform. This causes data transmission to be more versatile and saves bandwidth by just sending helpful information.

IoT Sensor Connectivity

IIoT deployment may involve a few or thousands of sensors monitoring and controlling a single machine or an entire production line. Sensors need to be connected to send back the data to the network and cloud software. This connection can be wireless or wired, and each of them comes with some form of pros and cons. 

Many manufacturing plants opt to hardwire their IoT devices using industrial Ethernet cables. Hardwiring can guarantee a reliable connection, but the distance between sensors, I/O blocks and PLCs can limit its function. There is also the risk of damage to the cable, which comes with the cost of replacing it. 

Nowadays, wireless IoT sensors are in trend as wireless are more powerful and reliable. It can cover a much larger area and distance. It is more scalable as many sensors can be deployed through this. 

For instance, a single private LTE network can wirelessly connect many devices on a factory floor and provide seamless functioning.

How Are Manufacturers Improving Productivity?

Here are some examples that prove that IoT sensors play an essential role in helping manufacturers save costs and improve productivity.

Enhanced Product Quality:

In machines already connected to the cloud platforms, it is easy to store data such as temperature and pressure to track in production batches digitally. Machine vision via high-resolution cameras is another way of tracking products through a production line. Vision sensors with software can monitor product quality. Hence, this technology can reduce poor-quality products from reaching consumers who can imperil the company.

Minimize Unplanned Downtime with Predictive Maintenance:

The accessibility to real-time data and cloud-based analytics allows engineers and maintenance crew to spot inefficiencies in machinery. It is more valuable than scheduled maintenance, in which programs can analyze data collected from sensors to predict if an unplanned breakdown will occur or not. This ultimately helps technicians replace components before they fail, dodging any accident or expensive repair.

Warehouse Management and Asset Tracking:

In a smart warehouse, the IoT sensors can help track the flow of assets throughout the factory. Autonomous robots can pick or move or pack orders without human interference. Automating these tasks can allow employees to focus on other priority tasks.

Improve Procurement and Forecasting:

Sensors can also be helpful for procurement managers. Sensors installed on the production line can watch the assembly of products, help control raw materials usage, and reduce waste. It also alerts the crew when the supply goes down. Thus monitoring these essential items using sensors can reduce waste and enhance forecasting.

Product Development:

We all know that product development is one of the most critical and costly processes in manufacturing. Manufacturers can reduce the sum and make a better decision before concluding on total production.
One of the best ways is to gather data through sensors on the production floor and advanced manufacturing analytics to reduce the time consumed in the R&D process.

In fact, sensors on products can be implemented to collect data in real-life scenarios. Collecting data in real-time allows engineers to make rapid changes to get a more efficient product.

Summary

It is apparent that sensors plan a crucial role in daily operations throughout factories. Data collected using sensors can help develop a more efficient production line, machine operation, and safety.

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