Improving reliability, performance, and safety are among the top priorities for industrial organizations and businesses today. They are focusing efforts and resources on controlling costs and maximizing value from existing investments and assets. State-of-the-art Predictive Asset Analytics technology helps organizations gain the highest return on critical assets by supporting predictive maintenance (PdM) programs.
InSource leverages AVEVA’s PRiSM Predictive Asset Analytics software to deliver early warning notification and diagnosis of equipment issues days, weeks or months before failure. This helps asset-intensive organizations reduce equipment downtime, increase reliability, and improve performance while reducing operations and maintenance expenditures.
AVEVA’s PRiSM incorporates a proprietary algorithm called OPTiCS that uses Advanced Pattern Recognition (APR) and machine learning technology. The algorithm uses artificial neural network technology and allows users to create operational profiles with a specific set of inputs and outputs to test how the outputs will evolve in the future through data playback. The result is that the system predicts costly failures that human subject matter experts would normally miss. Ultimately, machines don't have to fail. The modern industrial enterprise is generating massive amounts of data. Predictive Asset Analytics turns that data into actionable insight to help avoid equipment failure and keep machines at optimal performance.
Condition Based Maintenance for Improved Asset Performance
Predicting Equipment Asset Failures to Reduce Downtime
Covestra on the Value of Predictive Analytics
Increase Asset Reliability and Performance