The manufacturing industry, which deals with machines and automation systems, is one of the major sectors contributing to the global economy. According to Business wire, the sector accounts for nearly 16% of the worldwide GDP. As the industry has a significant impact on the economy, it has always gone through technical advancements. Industry 4.0 has been getting lots of hype recently; it is about digitizing using AI technologies.
In the last few years, manufacturing industries have invested considerable money in monitoring systems using Machine learning. However, it is impossible to monitor each processing unit on a large scale for the failure of components and anomalies in production lines.
One of the significant hurdles in achieving this digital transformation is the latency in analyzing the manufacturing operations on the cloud; Edge AI promises to solve this problem, making the process intelligent, efficient, and secure.
What is Edge AI?
Edge AI is the class of ML architecture in which the AI algorithms process the data on the edge of the network (the place where data is generated, i.e., locally) instead of sending it to the cloud. This very nature of the edge architecture makes it a perfect fit for reducing the inefficiencies in the existing systems. Let’s see some of the key advantages of the Edge AI in general.
Edge AI has considerable potential for building the smart manufacturing industry. One of the crucial components in Industry 4.0 is introducing intelligence “on the edge.” Mounting intelligence on edge will allow machines in the production units to make higher-level decisions, act autonomously, and give feedback so that stakeholders can detect flaws.
How will Edge AI work in the Manufacturing units?
Edge AI has a simple architecture in which processing units can deploy a pre-trained model on edge, i.e., near the data source. However, one should note that the model on edge will only score the training part where there is no limitation for the computational power.
The above figure gives the architecture for the edge ai in the manufacturing unit. The process has the following stages.
Data Ingestion: This stage will ingest the real-time sensor data from the monitoring system of machines into the pipeline.
Storage of the Data: The stream of the data will be stored locally and securely as no cloud processing is involved here.
Processing: This stage of the pipeline will process the data according to the need of the pre-trained models.
Analysis: Here the data will be analyzed by the models, and they will give the results.
Results: This stage will compile the results. And after these Results, the Response can be taken by the stakeholders.
What are the Use cases of Edge AI in Manufacturing Industry?
In this section, we will see the use cases of the edge AI in Manufacturing.
Predictive Maintenancerefers to the ability to pre-emptively detect the failure of machines using machine learning predictive algorithms.
Predictive Maintenance has been in the industry for some time, but it has also been difficult to implement. Edge AI can play the role of catalyst to smooth out this process as it can process the data near the edge, making it simpler and efficient to implement.
Real-time fault predictions
Helps in building advanced breakdown strategies.
Cloud-less predictive results.
Manufacturing units face challenges simply trying to fetch the data from their machines, processes, and system. One of the biggest hurdles is that each manufacturing units have their data streams, now firstly, all of these streams, whether of use, are sent to the cloud. Then processing is done. If some initial filtering can be done, then only useful data streams can be utilized in the cloud or locally, this can be achieved with edge ai near the data generation streams.
Condition Monitoring can help increase the revenue as less cost is needed for maintenance.
Condition monitoring makes the processing of these manufacturing more agile.
One can utilize multiple streams to achieve the decisions as processing is on edge.
Precision Monitoring and Control
One of the main goals of industry 4.0 is to use the data from multiple machines, processes, and systems from the manufacturing unit and use them for smart controlling and making precise decisions in real-time. This precision monitoring and controlling system use a large amount of data Machine learning algorithms. Edge Computing is a perfect fit for it as it can collect, aggregate, and filter the data used by the AI/ML algorithms.
Making Industry 4.0 more achievable.
Direct the manufacturing unit more autonomous and sell-tolerant machines, aka smart machines.
Why to choose XenonStack?
XenonStack possesses the experience to develop and deploy such out-of-the-box solutions. These solutions require a team of highly skilled and experienced professionals. XenonStack provides you with a dedicated team for the development of customized solutions that helps you to fulfill your business requirements the way you want.