Edge AI in IIoT (Industrial Internet of Things)
Today, in the modern Internet of Things (IoT) age, a record amount of information created by linked devices needs to be gathered and evaluated. As a result, massive amounts of data are generated in real-time, necessitating the use of AI systems to interpret this volume. Edge AI refers to the installation of AI software on hardware throughout the real world. Every sector in business is trying to boost automation to enhance workflow, productivity, and security.
With the help of developments in edge AI, robots and gadgets can now function with the “intelligence” of human cognition wherever they may be required to. An industry that is resilient, human-centred, and sustainable is the primary focus of Industry 5.0, which is a complement to Industry 4.0.
The genesis of Industry 5.0 is that this next generation of manufacturing, or producing goods, has to go beyond merely making something consumer-focused because consumers always want something faster, better and cheaper. 5.0 is leveraging technology to not only be consumer-focused, but also create a company that is worker-focused, sustainable, and that is also focused on the country that it is operating in.
Manufacturers worldwide have begun to alter their manufacturing processes with AI at the edge. The manufacturing industry uses AI in its applications, such as predictive maintenance, anomaly detection, real-time quality monitoring, and supply chain optimization. But implementing AI in the manufacturing industry is a challenging process.
Edge AI in Manufacturing Use Cases
Predictive Maintenance: Sensor data may be utilized to discover anomalies early and anticipate when a machine will break, referred to as predictive maintenance. Sensors on equipment scan for defects and notify management if a machine needs to be repaired, allowing the problem to be treated quickly and with minimal downtime. The manufacturer can precisely analyze equipment conditions using sensor data, artificial intelligence, and edge computing, avoiding costly unexpected downtime. Sensor-equipped video cameras, for example, are used in chemical facilities to detect corrosion in pipelines and inform workers before any damage is done.
Quality Control: Detecting flaws is an essential aspect of the manufacturing process. Defects must be caught in real-time when running an assembly line that produces millions of units. Edge computing devices can make microsecond choices, detect faults immediately, and warn workers. This feature gives firms a significant edge by reducing waste and increasing manufacturing efficiency.
Equipment Efficiency: Manufacturers are constantly seeking ways to optimize their operations. When combined with sensor data, Edge computing can be used to evaluate overall equipment effectiveness. For example, in the automobile welding process, producers must follow several regulations to ensure that their welding is of the highest quality. Companies can monitor products in real-time using sensor data and edge computing, catching flaws or safety issues before leaving the facility.
Yield Optimization: Knowing the exact quantity and quality of the components used in the manufacturing process is crucial in food processing plants. Machines can rapidly recalibrate if any parameters need to be modified to generate better quality products using sensor data, AI, and edge computing. There is no requirement for manual oversight or data to be sent to a central location for analysis. The sensors on-site are capable of making real-time decisions to boost yields.
Features of AI in Manufacturing Industry
Customer Satisfaction: It is necessary to provide customer satisfaction to improve the customer experience. Companies must deliver proactive, seamless, and personalized services to their customers to enhance their experience.
Trust: Explanation is required for the decision-maker to trust the system. The degree of confidence in the system to decide and the factors that influence its decision must be explained. A completely transparent system can place the Customer’s faith in their system. It is hard to provide such transparency in opaque models, but some can use alternatives to clarify them.
Fairness: Fairness is a must to remove the bias for the system. It is a central requirement of the system. Most of the time, the system would be biased due to an improper dataset. It should be evaluated to check the fairness. Various measures can be used to make a fair dataset, such as resampling and calibration.
Transparency: Transparency is a major feature that Explainable AI provides. The transparent nature of the AI system helps to understand that and also trust in that.
Challenges of Implementing AI
The challenges of implementing AI in the manufacturing industry are listed below:
- Collect correct data.
- Justifying the working model process.
- Justifying and validating the generated output of the system.
- Strategies Implementation.
- Computing Power.
Most of the AI systems use black-box approaches to get accurate and correct results. But due to the complexity and lack of transparency of the systems, there is a lack of accountability in the decision-making process. The solution to this opaque nature of models is a white-box model. White-box models generate transparency and empower the developer and customer to execute complex projects with confidence and certainty.
Challenges of the AI System
Most commonly used AI systems face certain types of issues and challenges, such as bias, opaqueness, data insecurity, etc. Simultaneously, transparency and privacy are important concerns in healthcare, finance, and law. Some issues are listed below:
Privacy: Most of the data is stored digitally on a single internet. In the digital world, it is hard to control access to data. Therefore the security of data is always at risk while using AI. But in today’s world, data security is at the top.
Manipulation of Behaviour: In AI in surveillance, the use of information or collected to manipulate behavior can reduce autonomous rational choice. It directly attacks the autonomy of individuals.
Opacity: Lack of accountability, auditing, and engagement reduce opportunities for human perception. As well as developers, users are not aware of the process system use to reach the output. This opacity increases the bias in datasets and decision systems.
Bias in decision making: Human beings are sometimes biased against other communities or systems. Unconsciously this bias enters into the AI system. This can be through data or the user’s intention while interpreting the output of the system. This bias against a particular subpopulation can harm that specific group, which faces challenges in society.
Evil genies: AI can work as an evil genie that will obey the order, but the consequences of the way it uses to obey the order can be terrible. But this is only when there is a lack of understanding of full context when the system trains.
Security: TWhen a system is designed, it is first trained then tested in several cases. Only after it launched in the real world, but it may be possible that it may not cover all the examples that systems deal with in the training phase. It can fool a human being by the wrong prediction for a new case in the real world.
Sources:
hhttps://analyticsindiamag.com/edge-ai-and-its-transition-into-industry-5-0/
https://www.akira.ai/blog/ai-in-manufacturing-industry/
https://www.akira.ai/blog/ai-adoption-challenges-and-modern-solutions/
