Industrial Sensors and AI: What Lays in the Gap Between MSMEs and Industry 4.0?
Sensors are devices that can detect (or sense, as the name indicates) changes in an environment.
Affine
Manufacturing Practice
Global industrial automation is transcending boundaries at breakneck speed. In 2020, the market for industrial automation was pegged at USD 175 billion and is touted to grow at a rate of 9% by 2025. This has been a product of the Industry 4.0 wave.
Further, unlocking the full capabilities of IoT and AI within different workflows and frameworks has been a breakthrough trend for industries across all major sectors.
One pertinent aspect of this growth has been the role of sensors in manufacturing industries. They have been a keen driver in the shift to “smart” machinery. But before we delve further into their significance, there’s some jargon we need to get out of the way:
Making Sense of Non-IoT, IoT, and IIoT Sensors
Sensors are devices that can detect (or sense, as the name indicates) changes in an environment.
Consider a manufacturer of sanitary napkins. Their factory floor would incorporate a wide range of sensors for different environmental conditions in their manufacturing processes. There are three possible kinds of sensors they could use. One can be a conventional humidity sensor that merely indicates the humidity in the areas where raw materials are stored. It displays the humidity level, and that’s all; there is no interaction of this data point with any other piece of data.
An IoT-based humidity sensor can track and remotely change the levels or give real-time alerts because it connects to the network. It stores humidity level recordings on a daily, weekly, and monthly basis for you to gain key business insights related to your manufacturing processes and optimize them accordingly for that factory floor.
Now consider this usage of sensor data across thousands of factory floors on a large scale. Businesses can compile all recordings in a central system and visualize the data as required.
Now that’s a step further than IoT; it’s IIoT, i.e., Industrial Internet of Things.
Automating Workflows Intelligently, Using AI
The sheer amount of data that sensors can generate may be daunting.
You might not know how to visualize this data to make it actionable.
Machine learning models are dependent on repositories of data that they can train with. This is where an IoT sensor comes into the picture as it collects and communicates all the data it measures and run AI algorithms to deliver real-time actionable insights.
Let’s use the manufacturing industry example once more.
AI solutions can bring together all the data collected from various sensors, such as temperature data, humidity recordings, moisture content, and vibrations from machinery inside the manufacturing unit. It could even have defined thresholds to detect anomalies in the products and flag them for mitigation. AI tools can make sense of all these data streams to determine optimal conditions at each stage of the production of sanitary napkins.
The business can use IoT sensor-generated data to calibrate its workflows, make the product more consistent improve quality, and hence the revenue.
This will be dependent on capitalizing on the data collected through robust AI models.
Some Inhibiting Factors in Taking a Leap Forward
Integration roadblocks: Legacy systems were never meant to connect to the internet and involving such systems in the IoT or IIoT network may require adopting a network-agnostic platform. The conventional non IoT sensors cannot collect data that can be aggregated and visualized.
Limitations in Technology Maturity: Synchronizing various sensors and devices on an IoT platform will require niche AI expertise.
Capex Constraints: Many organizations are worried on making considerable hardware investment and allocation of budget towards IoT sensors to adopt AI advantage.
Does That Mean Businesses with Non-IoT Sensors Will Be Left Behind?
No!
While it is crucial to have equipment with sensors that communicate data for AI tools to study and recognize patterns, you can achieve the same result even with legacy systems and non-IoT sensors.
You do not have to make heavy investments in IoT sensors to capitalize on sensor data. While an eventual transition to IoT or IIoT sensors from non-IoT sensors may be needed to keep up with AI advancements, below are the few techniques that help you plan for with your existing setup:
Use IO-Link: This can collect information from your sensor through the IO-Link Master and activate communication at the sensor level. Instead of using an IoT sensor, you integrate IoT into your traditional sensors using an IO-Link. It receives signals and data from the sensors and exports it to the manufacturing site. This process helps in predictive maintenance, thus reducing extended downtimes or overburdening of human capital; all the benefits of IoT sensors, but with conventional sensors.
Source: Omron
Adopt Edge Technology: Manufactures can bring system integrators that connect HMI systems using edge architecture. This minimizes interoperability issues between legacy systems and AI and other monitoring tools.
Traditional Manufacturers Have Begun their AI Journey!
The legacy systems are getting a fresh lease of life with technology interventions and manufacturers are gaining competitive advantage in the market.
Consider this.
Here is the case of a business that provided tools for grain moisture control. These products would help farmers prevent spoilage of grain due to environmental conditions.
But a majority of farmers did not have access to the latest technology. They relied on traditional equipment with conventional moisture and temperature sensors. Their grain monitoring systems were not compatible with modern IoT sensors and cables.
Therefore, these farmers needed alternative methods to make most of these grain moisture control products.
The goal was to integrate their legacy equipment with alternate monitoring methods to achieve the same benefits of IoT systems and sensors.
The solution came in the form of a controller that incorporated a Programmable Logic Controller (PLC) system using edge-forward middleware architecture. It was connected to the farmers’ traditional sensors. Now, even without IoT sensors, the farmers could remotely monitor their grain warehouse site and track parameters such as grain spoilage, temperatures, energy costs, and more. This helped them reduce wastage and gain higher returns on their crop.
As illustrated, the potential of alternate methods that interact with legacy systems to generate data is endless. If you are a small organization with limited cloud capabilities or an MSME with traditional equipment, these solutions can help you make the most out of your non-IoT equipment.
The Stage is Set, It’s Time to Act
Traditional manufacturing industries that do not necessarily have the equipment or financial resources to invest in IoT sensors can still capitalize on AI technology and gain high returns. Start looking at your legacy systems as cost-efficiencies rather than inhibitors using alternative interventions!
If you want to be at par with those businesses that are making headway with AI powered growth while retaining your current setup, talk to us.
About Author
Affine Manufacturing Practice is a dedicated team responsible to continuously innovate manufacturing solutions and services powered by AI, AE & Cloud capabilities. Our enterprise-grade solutions are new and futuristic, delivering the next big thing in Industry 4.0.
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