Build Predictive Models for Failure and Multi-class Failure

Machine learning technology leveraging historical and real-time data from sensors mounted to production equipment as well as PLCs, SCADA, and other sources can accurately flag potential failures of whole machines and/or critical components before they can cause downtime. Failures may be binary in nature; that is, either a failure occurred or not. Failures can also be multi-class and fall into several different categories, including reduced speed, throughput, or quality. Obviously, the more complex the machine (or system), the more necessary it is to use ML models to effectively help prevent failures that impact productivity.