Supervised Machine Learning

In manufacturing use cases, supervised machine learning is the most commonly used technique since it leads to a predefined target: we have the input data; we have the output data; and we’re looking to map the function that connects the two variables.

Supervised machine learning demands a high level of involvement – data input, data training, defining and choosing algorithms, data visualizations, and so on. The goal is to construct a mapping function with a level of accuracy that allows us to predict outputs when new input data is entered into the system.

Initially, the algorithm is fed from a training dataset, and by working through iterations, continues to improve its performance as it aims to reach the defined output. The learning process is completed when the algorithm reaches an acceptable level of accuracy.

In manufacturing, one of the most powerful use cases for Machine Learning is Predictive Maintenance, which can be performed using two Supervised Learning approaches: Classification and Regression.

These 2 approaches share the same goal: to map a relationship between the input data (from the manufacturing process) and the output data (known possible results such as part failure, overheating etc.).

Classification

When data exists in well-defined categories, Classification can be used. An example of Classification that we’re all familiar with is the email filter algorithm that decides whether an email should be sent to our spam folder, or not. Classification is limited to a boolean value response, but can be very useful since only a small amount of data is needed to achieve a high level of accuracy.

In machine learning, common Classification algorithms include naive Bayes, logistic regression, support vector machines and Artificial Neural Networks.

Predictive Maintenance makes use of multi-class classification since there are multiple possible causes for the failure of a machine or component. These are possible outcomes that are classified as potential equipment issues, calculated using a number of variables including machine health, risk levels and possible reasons for malfunction.

Regression

Regression is used when data exists within a range (eg. temperature, weight), which is often the case when dealing with data collected from sensors.

In manufacturing, regression can be used to calculate an estimate for the Remaining Useful Life (RUL) of an asset. This is a prediction of how many days or cycles we have before the next component/machine/system failure.

For regression, the most commonly used machine learning algorithm is Linear Regression, being fairly quick and simple to implement, with output that is easy to interpret. An example of linear regression would be a system that predicts temperature, since temperature is a continuous value with an estimate that would be simple to train.