How to develop effective plant maintenance and performance optimization strategies for fluid control equipment
Explore the trade-offs between condition monitoring, engineering-based algorithms, and machine learning anomaly detection for industrial fluid control equipment.
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Developing effective maintenance and performance optimization strategies for an entire plant is a complex task that involves a wide range of capabilities, technologies, and trade-offs. To ensure that all assets are functioning at optimal levels, a holistic approach is necessary, which considers not only the immediate maintenance needs but also long-term performance goals. When it comes to industrial fluid control equipment, a there are optimal methods to manage these complexities.
This requires a comprehensive understanding of these assets, how they fit into the overall system, as well as the ability to analyze data from various sources and identify patterns and trends that may affect performance. By leveraging a broad matrix of capabilities, technology, and trade-offs, plant managers can build a maintenance and performance optimization strategy that is tailored to their specific needs and goals.
In this webcast, expert presenter Michael Zimmerman, will discuss best practices around predictive analytics, condition monitoring, and machine learning-based anomaly detection. Developing an understanding of the approach empowers plant managers and their teams to optimize their time effectively and delve more deeply into critical challenges.
- Understand predictive analytics methodologies for industrial flow control equipment
- Learn the benefits and trade-offs between machine learning-based anomaly detection and engineering-based algorithms.
- Explore applications for condition monitoring, predictive analytics, and deep dives with subject matter experts to solve reliability and maintenance challenges.
Michael Zimmerman, Director, Global IoT R&D, Flowserve Corporation
Chris Vavra, Web Content Manager, CFE Media and Technology