Industrial Production: Machine Learning Meets Automation

Industrial automation is one of the most celebrated tech advances today, and it is easy to see why - its ability to promote higher efficiency, increase productivity, and promote safety. New paradigms, such as the Internet of Things (IoT) and Smart Factory, are now opening another high-potential area of machine learning that is waiting to be explored.  Here is a demonstration of how machine learning works in industrial systems, helping to take automation to the next level. 

What is Machine Learning?

Combining machine learning with automation is paramount for sustaining industrial competitiveness.  Machine learning is a branch of artificial intelligence that involves the use of data to train machines to imitate what people do. This implies that instead of having an employee operate the material handling equipment, conveyor or motors manually, a computer program will be in charge of everything. See – it goes beyond automation because the computer learns how a real person operates the machine and follows the same trend. 

Even when there are issues, machine learning will diagnose and note the changes, alerting you to take action early enough. With machine learning, you are able to improve the effectiveness of your facility with a huge margin.

How Machine Learning Works to Improve Industrial Operations 

As industrial facilities evolve in light of data evaluation, it is paramount to think of how your system fits in the new paradigm to be able to internalize and implement machine learning. We must also indicate that this requires more advanced tools in line with the new sophisticated workplace. The most important of these are the embedded sensors, modern computers, and advanced AI algorithms that can analyze big data in real-time. Here is how it happens. 

• The Process Starts with Big Data 

For your smart factory to work, you need to start with the creation of big data infrastructure. This forms the baseline that ensures your machines are trained based on the right data sets. Although you might be capturing big data already, it is prudent to ensure it can be integrated by adopting the right frameworks, such as PyTorch. 

• Training Your Machines to Take over Operations

Once the data for training your machine is gathered, analyzed, and a condition monitoring system is developed, it is moved to a machine learning framework, such as TensorFlow and MATLAB, for further processing. The frameworks start the training process, with the focus being the process and final results. As the algorithm learn about 

• Machine Learning and Automation in Preventive Maintenance 

One of the areas where machine learning applications are common, or, say most industries prefer to start there, is preventive maintenance. By learning how your equipment works, it becomes very easy to note when slight changes are reported. For example, equipment with a high delivery capability of 2000 products per hour will be flagged down when that rate goes down to 1950.

As you can see, machine learning and automation complement each other well to make industrial production more efficient and successful. However, it is important to ensure that all the components for machine learning, especially the computer programs for data processing, are installed properly and maintained in top condition.