IIoT refers to industrial IoT, or the Industrial Internet of Things. Standard IoT describes a network of interconnected devices that send and receive data to and from each other through the internet.
IIoT and Smart Manufacturing is the usage of connected devices for industrial applications, such as manufacturing and other industrial processes. It involves the use of things such as machine learning and real-time data to optimize industrial processes through a connected network of sensors, actuators, and software. The implementation of IIoT is referred to as Industry 4.0, or the Fourth Industrial Revolution.
Currently, most conventional industrial processes are still using Industry 3.0 practices. However, with the ongoing development and implementation of IIoT across industries, we are trending towards Industry 4.0 – with manufacturing plants being one of the major recipients of this change.
In order to understand the impact that Industry 4.0 and IIoT and Smart Manufacturing have on manufacturing plants, it is necessary to understand the existing structure that allows a manufacturing plant to operate.
A manufacturing plant has an operational structure of several levels; each of these levels has a certain function and is comprised of equipment, software, or a mixture. This is known as the automation pyramid.
• Level 0 is the field level, containing field devices and instruments such as sensors and actuators.
• Level 1 is the direct control level, containing PLCs (programmable logic controllers) and HMIs (human-machine interfaces). HMIs display parameter values and allow remote control of devices through stop and start instructions, as well as set point adjustment. HMIs are connected to the PLCs, which are then connected to the field devices.
• Level 2 is supervisory control, and contains the SCADA system (supervisory control and data acquisition). The SCADA is a system of software and hardware, and is used for real-time data collection and processing, as well as automatic process control. SCADA collects its data from PLCs and HMIs over communications protocols such as OPC UA and Modbus.
• Level 3 is the planning level, containing the MES (manufacturing execution system). The MES is responsible for monitoring and recording the entire production process from raw materials to finished products.
• Level 4 is the management level, containing the ERP system (enterprise resource planning). ERP is responsible for centralizing all of the information within the organization. It’s used to manage accounting, procurement, and the supply chain, among others – and is more focused on the business aspect rather than the manufacturing aspect.
With an IIoT and Smart Manufacturing system in place, there is an additional layer: the cloud, which is above all the other layers, and implements analytics such as machine learning. the field devices are referred to as edge devices. An edge device has no physical connection to the PLC – it’s instead connected through Wi-Fi. These devices communicate with the PLC over the native protocol, where all the process control is done.
During production, human operators observe the MES system to monitor parameters such as availability, performance, and quality – which are multiplied to give the OEE (overall equipment effectiveness). An OEE of 100% shows perfect production – the goods are manufactured as fast as possible and at the highest quality possible.
If one of the parameters is low, such as the performance (production speed), the operator can instruct the SCADA system to increase the machine speed; this will result in goods being manufactured faster – and a higher performance value.
However, while goods are being produced faster, there also tends to be more waste – so the quality will drop. The operator will have to decide exactly how much to set the machine speed in order to find a good compromise between quality and output. To find the exact balance that maximizes profitability is a difficult task – one which is almost impossible for a human to accomplish.
IIoT and Smart Manufacturing enables all of the devices and systems to be able to send and receive information to and from the same place, in real time, without human intervention. This allows the machine learning to make optimal decisions regarding equipment and parameter set points to make the manufacturing process as efficient as possible.
With this system in place, no humans are required to make complex decisions. This results in optimized decisions to be made as quickly as possible – and conditions that result in the greatest profitability for the manufacturing plant.
The primary method of maintenance is condition monitoring, also known as condition-based maintenance (CbM).
Condition-based maintenance relies on real-time parameters measured by an equipment’s sensors such as temperature, pressure, speed, vibration. Each of these parameters is given a particular range for which the values are acceptable for a given piece of equipment. These parameters are actively monitored, and once a value is measured outside of the acceptable range, maintenance is scheduled.
The issue with condition-based maintenance is that the equipment’s fault is detected after a certain amount of degradation has already taken place. Depending on the rate at which degradation is taking place, this may not leave enough time for timely maintenance to be carried out. The amount of degradation may have also caused damage which is more costly to repair than if it were addressed earlier. The reverse could also be true; a parameter has exceeded a certain boundary, leading to maintenance to be performed immediately. However, there could’ve been a more convenient time, or maybe the machine could’ve carried on running for a considerable amount of time before maintenance being necessary – leading to excessive, unnecessary costs.
With IIoT, the method of maintenance can evolve to predictive maintenance (PdM).
Like condition-based maintenance, predictive maintenance also uses sensors to continuously monitor parameters. However, predictive maintenance also continuously collects and analyzes both historical and real-time data using statistical methods and machine learning. Because data trends are being analyzed instead of absolute values, problems can be detected much earlier, and an accurate failure time is determined – allowing maintenance to be scheduled at the most convenient, effective time.
Without IIoT, every time a new field device is installed in the plant – such as a pressure transmitter, flowmeter, control valve – it needs to be manually wired into a PLC. Then, its tag needs to be added to the PLC, HMI, OPC server, SCADA, and MES. This is a costly and time-consuming process.
When a new device is installed, no complex engineering is required to connect it to the cloud and the existing devices.
The edge devices, PLCs, HMIs, SCADA, MES, ERP, and machine learning all publish their tags and data into the unified namespace – a centralized data repository.
The machine learning allows continuous, real-time collection of data from all of the devices. It can then use this data to run algorithms and publish additional tags into the namespace
In essence, IIoT and Industry 4.0 allow manufacturing plants to address many of the inefficiencies and solve a lot of the challenges that they face. The use of interconnected sensors and machines, along with free-flowing data enables smarter decisions to be made regarding all aspects of production and operations – leading to reduced downtime, faster production, higher-quality production, and increased profitability.
TQS Integration is a global technology consulting and digital systems integrator. We provide you with expertise for the digitization of your systems and the digital transformation of your enterprise.
With clients across the pharmaceutical, process manufacturing, oil and gas, and food and beverage industries, we make your data work for you – so you can maximize its potential to make smarter business decisions.
Please contact us for more information.
TQS Integration announces launch of their latest software release, ODIN, empowering the connectivity of real time data with cloud-based systems, machine learning and AI platforms in an easy, secure, and affordable way. This newly developed software enables TQS to help their customers achieve process manufacturing advances and propel towards industry 4.0.
Equipped with an easy to use, web-based interface, cloud data transfer through ODIN allows for real time and on-demand data egress to enable machine learning and supports multiple source and destination platforms. ODIN is compatible with both OSIsoft PI and Wonderware historians, allowing efficient and secure transfer of valuable information from multiple historians to cloud-based destinations including Amazon S3, Microsoft Azure Data Lake Gen 2, IOT Hubs, Event Hubs, SQL Server and more.
"We are very excited about what ODIN can do for our customers to provide an easy-to-use tool to solve the need for automated data extracts and remove the time-consuming process of extracting and contextualizing data. ODIN will allow that valuable time to be refocused back to data science and empower analytics and Industry 4.0.” says Emmett O’Connor, Global Head of Product Development at TQS."
The complex nature of cloud data transfer is now made easy as ODIN automatically aggregates, filters, contextualizes and delivers formatted real time information efficiently via a user-friendly GUI (Graphic User Interface) that enables ease of monitoring job success. In-built auditing and distributed agent’s nodes also ensure configurations are monitored for compliance, while transfers are seamless, secure, and scalable.
ODIN can help manufacturing companies to prepare compliance and regulatory reports, run predictive advanced analytics, integrate operations information with business information, ultimately gaining valuable insights to make optimized business decisions and cost savings. This tool allows TQS to deliver competitive advantage in digital transformation for their clients.
"ODIN enables companies to make key business decisions and operational improvements through the utilization of existing data – it brings Industry 4.0 and digital strategies to the next level. This further strengthens our position as the industry leader in data intelligence. TQS has seen significant expansion in recent years, more than doubling staff numbers over the past two years, and we are planning to add an additional 50 jobs in the next 12 months to support our continued growth" comments Korie Gleeson, COO at TQS.
About TQS Integration
TQS Integration is a global data intelligence company providing turnkey solutions in system architecture and application design, engineering, system integration, project management, commissioning and 24x7 “follow the sun” support services to valued customers. TQS is at the forefront of data intelligence for over 20 years, working with an extensive client base in the Pharmaceutical, Life Science, Food & Beverage, Energy and Renewables industries. As the go-to partner for data collection, cloud data transfer, contextualization, visualization, analytics, and managed services, we are the main drivers in the world’s leading companies — helping them become leaders in Industry 4.0.
For information, please contact us.
Seeq software applications, Seeq Workbench and Seeq Organizer, enable manufacturing organizations to rapidly analyse, predict, collaborate, and distribute insights to improve production outcomes. Seeq is designed to run on-premise, on Microsoft Azure or Amazon Web Services cloud platforms, or in mixed on-premise and on-cloud deployments encouraging digital transformation.
“We are proud to confirm our partnership with Seeq that brings together expert authority in advanced process analytics. Together we can empower our customers with smart operational insights which improve process manufacturing efficiency for them”, comments Alan Bourke, Global Head of Business Development for TQS.
Being certified partners of Seeq further enhances TQS partner network. This allows them to collaborate with clients to help define their data infrastructure and digitalization roadmap, the cornerstone to any Pharma 4.0 initiative.
“Thought leaders like TQS Integration with local customer and vertical market expertise are critical to the success of Seeq,” adds Will Knight, Head of Partner Sales for Seeq. “Our customers rely upon them to deliver advanced analytics solutions that provide insight and drive results”.
“We have also invested in building out our Data Analytics Team to bring Data Modelling capabilities to all our customers, so they now have the all-encompassing, best-in-class, suite of products, and services to ensure that they overcome their data challenges in their organization through innovative solutions that promote digital transformation”, says Paul Power, Global Head of Engineering for TQS.
Seeq provides in-depth and advance process data applications which are key to deliver accurate diagnostics and predictive analytics. With the help of the right partnerships, TQS is committed to improve process performance and drive operational excellence for their customers.
About TQS Integration
TQS Integration is a global data intelligence company providing turnkey solutions in system architecture and application design, engineering, system integration, project management, commissioning and 24x7 “follow the sun” support services to valued customers. TQS is at the forefront of data intelligence for over 20 years, working with an extensive client base in the Pharmaceutical, Life Science, Food & Beverage, Energy and Renewables industries. As the go-to partner for data collection, contextualization, visualization, analytics, and managed services, we are the main drivers in the world’s leading companies — helping them become leaders in Industry 4.0 and preparing them for digital transformation.
For information, please contact us.
About Seeq Corporation
Founded in 2013, Seeq publishes software applications for manufacturing organizations to rapidly find and share data insights. Oil & gas, pharmaceutical, specialty chemical, utility, renewable energy and numerous other vertical industries rely on Seeq to improve production outcomes, including yield, margins, quality, and safety. Headquartered in Seattle, Seeq is a privately held virtual company with employees across the United States and sales representation in Asia, Canada, Europe, and South America.
For more information about Seeq, please visit www.seeq.com.
TQS Integration is proud to be data partners with four of the global pharmaceutical companies currently at the forefront of the development and manufacturing of a Covid-19 vaccine.
The rapidly-deploy, scale-up and produce model has emerged over the past few months, to facilitate the efficient production of the Covid-19 vaccine. This is a major step forward in the evolution of vaccine technology. At the heart of this evolution is sound data, and a patient centric approach, which includes product quality, safety, and data security. All this rely upon the integrity of vast amounts of big data generated throughout the product lifecycle. “TQS is a key enabler in automated data collection, then getting the relevant data to our scientists.” comments senior Pfizer engineer.
TQS have data intelligence specialists who provide expertise in all areas of data integrity and data analytics to the Life Science sector. They also provide OSI PI software engineering integration services, software development services, and data solutions to each of the top 12 pharmaceutical companies worldwide.
“We are extremely proud to play a part in this fight against the global pandemic. We see our role as fundamental in the quick and efficient manufacturing of these wonderful vaccines which will help protect people all over the world. Our expertise in data and manufacturing excellence has helped to ensure that our clients can deliver on quality, price, and speed.” Says Alan Gallagher, Global Account Manager at TQS.
TQS Integration is a global data intelligence company providing turnkey solutions in system architecture and application design, engineering, system integration, project management, commissioning and 24x7 “follow the sun” support services to valued customers. TQS is at the forefront of data intelligence for over 20 years, working with an extensive client base in the Pharmaceutical, Life Science, Food & Beverage, Energy and Renewables industries. As the go-to partner for data collection, contextualization, visualization, analytics, and managed services, we are the main drivers in the world’s leading companies — helping them become leaders in Industry 4.0.
For information, please contact us.
Have you ever wondered if it were possible to predict process conditions in manufacturing? Know what is likely to happen before it actually happens in your business processes? Digital Twin might just be your answer.
There are several different definitions of Digital Twins or Clones and many use them interchangeably with terms such as Industry 4.0 or the Industrial Internet of Things (IIOT). Fundamentally, Digital Twins are digital representations of a physical asset, process or product, and they behave similarly to the object they represent. The concept of Digital Clones has been around for some time. Earlier models were based on engineering principles and approximations, however they required very deep domain expertise, were time consuming and were limited to a few use cases.
Today Digital Clones are virtual models that are built entirely by using massive historical datasets and Machine Learning (ML) to extract the underlying dynamics. The data driven approach makes Digital Clones accessible for a wide range of applications. Therefore, the potential for Digital Twins is enormous and includes process enhancements\optimization, equipment life cycle management, energy reductions, safety improvements just to name a few.
Building digital clones require:
1. A large historical data set or data historian
2. High data quality and sufficient data granularity
3. Very fast data access
4. A large GPU for the model development and real time predictions
5. A supporting data structure to manage the development, deployment, and maintenance of ML models
The following shows the application of a Digital Twin to a batch process example. The model is built with 30 second interpolated data using a window of past data to predict future (5 min) data points:
So, what’s all the hype of Digital Clones? Well, not only are they able to predict process conditions, they also provide explanatory power on what drives the process - the underlying dynamics. The following dashboard shows a replay of this analysis including the estimate of the model weights:
In summary, the availability of enterprise level data historians and deep learning libraries allow Digital Clones to be implemented on the equipment and process level throughout manufacturing. The technology allows a wide range of applications and offer an insight into the process dynamics that were not previously available, improving data integrity and data access while achieving trust and data transparency with your partners. This helps to digitalize data management and processes to lower risk and improve efficient data sharing with partners.
Please contact us for more information.