Digitization and advances in AI and machine learning, or ML, are transforming Louisiana’s maintenance landscape. Increasingly, maintenance teams are turning to these tools to effectively diagnose and predict equipment failures, reports 10/12 Industry Report in its latest issue.
The continuous monitoring of equipment conditions, along with historical failure history, allows for the real-time assessment of performance—also known as predictive maintenance—with the goal of reducing unplanned failures and associated maintenance costs.
More recently, “prescriptive” maintenance tools have begun taking predictive maintenance inputs and incorporating AI and ML to analyze collected data, identify patterns, and forecast potential failures and propose solutions. Taking it a step further, ML models consider various factors such as machine age, condition, location and recent performance to then suggest possible root-causes of the issues.
More interestingly, perhaps, the prediction accuracy of these tools is improving over time as the models are continuously learning from historical and real-time operational data. For many in Louisiana’s industrial space, it’s having a very real impact on reliability, particularly when combined with a host of other new “disruptive innovations” such as augmented reality, virtual reality, the Internet of Things (IoT), drones, digital twins and others. Read more.
