On the huge promise of predictive maintenance.
In industry, machinery downtime can lead to penalties, degrade a company’s core operations, and cause dire reputational damages.
It’s essential for both small enterprises and the Walmarts of the world to have a well-rounded, well-tested maintenance strategy in place to reduce the likelihood of sudden outages or breakdowns happening and mitigate all associated risks.
A proper approach to maintenance can help enhance, quickly, the overall reliability and performance of a company, while also substantially reducing the operational costs it incurs.
In 2018, Amazon had an outage that lasted for about 60 minutes and lost nearly $100m in sales as a result.
The recent proliferation of connected technologies and predictive machine learning algorithms has had a profound effect on how companies conduct their equipment management; these recent advancements have enabled firms to ditch, almost completely, the practices of traditional reactive (RM) and preventive (PM) modes of maintenance and start embracing data-driven predictive maintenance (PdM) instead.
In this article, we’ll explain accessibly the key differences between RM, PM, and PdM and discuss some of the AI models commonly used for condition-based machinery monitoring.
The post Predictive Maintenance: How Machine Learning Models Help Reduce Prevent/Repair Costs and Break Limitations of Traditional Maintenance Approaches appeared first on IT Consulting Company Perfectial.
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