Predictive Maintenance Adoption: Overcoming Common Data Quality Issues
This white paper covers overcoming common data quality issues and the benefits from the superior predictive approach to maintenance.
Predictive maintenance more efficiently determines potential points of failure in an unexpected outage, detects incipient failure before it causes downtime, and maximizes the remaining useful life of machine components. But with so many factors to consider, preventing downtime becomes an elusive goal. There are many common data quality issues, but that is where machine learning can come into play.
Why use machine learning?
Manufacturers turn to machine learning when the amount of data they generate overwhelms traditional analytical methods. For example, in a recent project, just one of the machines we monitored sampled 1,500 data points per second. That’s 5.4 million individual data points per hour. That amount of data is far beyond what a human can manage on their own. Even with the help of Excel and smart pivot tables, finding value in the information would be difficult. And doing all of that evaluation in time to prevent an outage would be nearly impossible. ML synthesizes these vast amounts of data for you so that you can act on it. If you are are interested in learning more on this specifically, check out this white paper.
At a high level, these common data quality issues often prevent organizations in the manufacturing industry from adopting and succeeding with predictive maintenance. But, by combining data best practices with the right application of machine learning, you can overcome the data quality problem and benefit from the superior predictive approach to maintenance.
