SAS Asset Performance Analytics Reviews

Identify trends in operational data that can forecast deterioration and potential failures well ahead of time. By employing accurate failure pattern detection, you can minimize the prevalence of false positives often seen in model-based approaches. With streamlined machine learning techniques, it becomes possible to swiftly distinguish between standard and unusual behaviors, thereby initiating equipment protection in a matter of weeks instead of months. The integration of Aspen Mtell and Aspen Cloud Connect™ facilitates access to devices that support OPC UA. This analytical capability acts as a primary defense against asset deterioration by recognizing patterns in operational data that indicate early signs of failure. Furthermore, the use of AI-driven agent creation enhances existing maintenance protocols, allowing for the quick implementation of autonomous agents across various sites or throughout an entire enterprise. Leveraging precise failure pattern recognition helps to significantly reduce the high incidence of false positives associated with traditional model-based solutions. Through the application of low-touch machine learning, organizations can promptly pinpoint normal and abnormal activities to safeguard their equipment effectively.