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.
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TQS Integration, an international software consultancy company specializing in the provision of system architecture and application design, engineering, and system integration, today announced that they have been certified as a Google Partner with Google Cloud Partner Advantage.
Achieving Partner status means that TQS and Google can now share their combined knowledge and advanced technologies to provide the perfect end to end experience for their customers, from edge computing to automation, data historian to advanced data analytics and more.
“Obtaining Partner Status is a huge step into the future. It means recognition of our unique advanced technical expertise, and the ability to mutually share our learnings across a larger customer base with the Google team” says Rory Sheehan, Global Strategic Account Manager.
As a start, TQS and Google will focus their partnership on the Pharma / Lifesciences Industry where they have many mutual customers. Combined, their expertise and systems will allow customers to access manufacturing data, R&D data, operational data, and predictive data to streamline operations and quality.
“Our services will allow data sharing in a much wider capacity, both within the business and externally where required. With this partnership program and all our ongoing TQS developments, we are bringing customers, industries and partners into the future of manufacturing and that future is fuelled by data.” says Rory.
TQS has always been recognised for providing customers with an unrivalled service and advanced engineering, and this new Google partnership will continue to advance them into the future of Industry 4.0, IIoT and Edge Computing.
TQS Integration is a global data solution company specializing in the provision of 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 extensive client base in the 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.
Benefits:
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.
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