Predictive maintenance could be described as the effort to reach a high level of asset reliability while reducing unnecessary costs associated with unplanned events. With proper predictive maintenance in place, you’re able to increase the asset reliability, and reduce downtime, reduce maintenance costs and mitigate safety risks. But traditional approaches are time-consuming, costly, and usually require bringing in data scientists or central processing teams. Traditional methods also tend to involve complex algorithms and models.
In this presentation from Predictive Asset Analytics Online 2020, Data Analytics Engineer Usman Iftikhar discusses common maintenance challenges facing the industry, and how you can overcome them using TrendMiner’s self-service analytics platform.