Supervised Machine Learning
What is Supervised Machine Learning?
Machine learning (ML) is a subset of artificial intelligence (AI) which crunches big datasets to spot patterns and then uses them to predict outcomes, classify data, or detect anomalies. ML models need to be trained to recognize the patterns, and that’s usually done through either supervised or unsupervised learning.
Supervised learning is when the ML models are trained on labeled datasets. It requires human oversight to select the training data and assign the correct label to each case. For example, if you want to use supervised learning to train an ML tool to recognize the difference between fully dried-out product and partially moist product, you would feed it a number of representative examples labeled “dried-out product,” and others labeled “partially moist product.” After it’s seen a number of different examples, the model will learn for itself how to tell the difference between them.
When is Supervised Machine Learning Relevant for Process Manufacturing Plants?
Supervised learning is relevant for only a limited number of use cases in process manufacturing. This is because the method relies on having enough labeled historical data examples to use to train the model, but in a process plant, those are extremely rare.
Supervised learning models need at least 30 cases of a specific recurring problem to train the model effectively, but because there are so many moving parts in a process plant, it’s highly unusual for the same event to recur in the exact same circumstances. This makes supervised learning inappropriate for some of the most valuable ML use cases, like predictive maintenance or predictive monitoring.
That said, process plants can use supervised learning models for instances like:
- Forecasting demand
- Predicting the impact of price changes
- Automating purchase orders for raw materials
How Can Process Plants Apply Supervised Machine Learning?
Every kind of machine learning model relies on clean, accurate data. This means it’s important to refine data collection and storage methods for every ML application, whether it uses supervised learning, unsupervised learning, or a combination.
Although supervised learning isn’t relevant for most process plant use cases, it is possible to use an alternative semi-supervised learning approach, which applies the human element of supervised learning without requiring historical data examples. Semi-supervised learning makes it possible to apply predictive analytics solutions even to busy, anomaly-filled process plants.