Using Machine learning algorithms to detect anomalies and early-stage failures
In the digital age and among the Industry 4.0 revolution, industrial companies have more and more data and they are learning how to use their data to analyze the past and now, to predict the future.
Condition-based maintenance (CBM) as a section or discipline inside the broader and newer predictive maintenance field, is considered one where new AI technologies, tools and approaches, and connectivity abilities are put to action and where the acronym CBM is more often used to describe ‘condition Based Monitoring’ rather than the maintenance itself. CBM maintenance is performed after one or more indicators show that equipment is going to fail or that equipment performance is deteriorating [Wikipedia]
Machine learning algorithms build a model based on sample data, known as “training data” or “train set”, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks. [Wikipedia]