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How to Implement Machine Learning For Predictive Maintenance


As Industry 4.0 continues to generate media attention, many companies are struggling with the realities of AI implementation. Indeed, the benefits of predictive maintenance such as helping determine the condition of equipment and predicting when maintenance should be performed, are extremely strategic. Needless to say that the implementation of ML-based solutions can lead to major cost savings, higher predictability, and the increased availability of the systems. After different ML projects, I wanted to write this article to share my experience and maybe help some of you integrate Machine Learning with predictive maintenance. What is predictive maintenance: In predictive maintenance scenarios, data is collected over time to monitor the state of equipment.

Unsupervised Machine Learning: The Path to Industry 4.0 for the Coal Industry


Power plants can deploy these innovative technologies today to more accurately predict the condition of assets and schedule appropriate maintenance to correct equipment problems before failure.

Escaping the Reactive Maintenance Rut Through Analytics


The ability to forecast when a machine might go down is the Holy Grail of industrial maintenance. Using data collected from sensors within the equipment, the plant engineer determines the best course of action to address the troublesome component or parameter before it leads to asset failure. But many plants hesitate to start the journey toward predictive maintenance. The people responsible for making decisions about machine maintenance "spend much of their time firefighting or troubleshooting, or maybe they're understaffed or being forced to cut expenses," says Melissa Hammerle, business unit manager for Fluke Connect, a maintenance management system of software and logging devices. "The thought of setting up a preventive maintenance program seems like a bridge too far."