Predictive maintenance is revolutionizing industries by minimizing downtime and enhancing asset performance. In this insightful video, Sanjeev Heda (@saheda) delves into SAS’s robust capabilities in implementing predictive maintenance to a fleet of assets. Each aspect of predictive maintenance that showcased in this video will show data exploration, data processing and modeling, and how the end results can be consumed by the end users to drive action and value.
Key Highlights:
- Predicting Pending Failures in Real-Time: Create a real-time multi-layer model for predicting diverse pending failures using SAS Event Stream Processing (ESP).
- Remaining Useful Life (RUL): Develop reliability and survival models to estimate Remaining Useful Life with this being personalized per asset using time-series data.
- Power of SAS Analytics of IoT – showcasing use of SAS AIoT Data Model, Explorations, Custom Analytic Framework, along with other SAS tools such as SAS Studio, SAS Visual Data Mining and Machine Learning (VDMML), etc.
This proactive approach not only reduces maintenance costs but also significantly boosts operational efficiency. By predicting potential failures, companies can schedule timely interventions, ensuring uninterrupted production and extending the lifespan of essential machinery.
Check out this video demo to discover how SAS can transform your maintenance strategy and drive your operational success!
