Machine learning models may be the realm of a data scientist, but the insight of a process expert is crucial to their success
The ability to determine when a failure is going to occur has emerged as a radical shift from reacting when it does. In the process manufacturing industry, predicting failures before they happen helps engineers improve performance and schedule timely maintenance. Local process experts play a critical role in creating failure prediction models that are reliable.
Failure prediction is a machine learning technique that allows operational experts to take proactive measures and mitigate potential risks. By analyzing historical time-series and contextual data for patterns and other indicators, failure prediction models let plant personnel know when there is about to be a problem. Their uses include detecting anomalies from established monitors or even for forecasting events to schedule preventive maintenance with prescriptive instructions.
Collaborating for Failure Prediction Models
While the development of a failure prediction model predominantly falls within the realm of data scientists and central data teams, operational experts have an intricate understanding of the plant’s manufacturing processes, equipment, operating conditions, and failure modes. This domain knowledge is instrumental in identifying the relevant data sources, features, and variables that affect equipment performance and failure events.
When collaborating on a data science project, the engineer’s role then is to ensure that the data being used will achieve the desired outcome. As such, there are three areas where the expertise of the operational expert is necessary for a reliable failure prediction model.
