Predicting the Unpredictable: Implementation of an Advanced Machine Learning Method to Predict Degradation and Failure using Multivariate Time-Series Forecasting
Analyst firm Aberdeen Research says that 82 percent of companies have experienced unplanned downtime over the past three years and that unplanned downtime can cost a company as much as $260,000 an hour. Of the 82 percent of companies that have experienced unplanned downtime over the past three years, those outages lasted an average of four hours and cost an average of $2 million. Studies have shown nearly $1 trillion a year is lost to machine failures globally.
Of the companies experiencing unplanned downtime, 46% couldn’t deliver services to customers, 37% lost production time on a critical asset, and 29% were totally unable to service or support specific quipment or assets, impacting employee health and safety, customer-market loyalty, time to market, and lost revenue.
Early and accurate prediction of degradation and failure allow for preventative maintenance measures to be employed to help reduce or eliminate machine failure and unplanned downtime.
In the white paper we introduce our (patent pending) novel approach to predicting degradation and failure, demonstrate how our novel solution is able to produce results in a self-supervised solution, and achieve greater accuracy and lead time than was previously possible.
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