In order to apply machine learning technology to industry, you must first understand where machine learning is divided into categories, where there are different algorithms, and what is worth noting in practical applications.
According to reports, machine learning algorithms are gradually sneaking into our daily lives, but industrial applications are facing many bottlenecks. They are not as popular as consumer applications. Kathy Applebaum of Inductive Automation pointed out that looking at the current situation of industrial use of machine learning technology to predictive maintenance (PM ) Is the bulk, followed by quality control, demand forecasting and robot training.
There are three main types of machine learning. The first is data analysis. Applebaum pointed out that diagnostic analysis (diagnosTIc analysis) is to find out the cause of the problem, and predictive analysis (predicTIve analysis) predicts the future based on past data. Suggested analysis is based on predictive analysis. How to solve the problem.
As for the types of algorithms, the first is the clustering algorithm (k-means). Kevin McClusky of InducTIve Automation pointed out that the clustering algorithm is not clear about the meaning of each category, and only calculates the shortest distance from each point to the set cluster center. The distance determines the next cluster center, which is very suitable for data classification and defect analysis.
The second algorithm is called a decision tree. Applebaum believes it is very suitable for predictive maintenance and can also be used in conjunction with other algorithms.
The third algorithm is called regression analysis. McClusky believes that it is suitable for adjusting the workflow and forecasting output, such as forecasting output based on current variables. As for the neural network algorithm, it simulates the operation of the human brain. The most common industrial application is in the visual system.
No matter what kind of machine learning application, it is necessary to collect high-quality data first. The first thing is to find suitable data and process it to ensure the quality of the data. McClusky also recommends that when companies implement machine learning projects, they must use capture, transformation, and loading (ETL) to obtain data and automate the data collection process. Applebaum suggests to be brave enough to try different algorithms. Each supplier provides a variety of algorithms such as clustering, neural networks, and regression.
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