Precise reliability management: Predictive analytics to maximize asset reliability and performance

Posted: August 29, 2024

Enhancing reliability and efficiency, integrating AI with data infrastructure empowers informed maintenance decisions and operational resilience.

In the realm of AI and predictive maintenance, accuracy is paramount. The ability to reliably predict failures and diagnose issues before they escalate can save companies significant time and money while improving safety and operational efficiency. AVEVA™ Predictive Analytics, integrated with AVEVA™ PI System™, delivers accurate results through advanced sensor fault detection, anomaly detection, fault diagnostics, and time-to-failure forecasting.

AVEVA PI System: A fundamental building block

In our increasingly connected world, AVEVA PI System helps create a fundamental data infrastructure for end-to-end asset reliability. Its data is consolidated and contextualized in AVEVA’s digital twin for asset reliability. Powered by CONNECT, our industrial intelligence platform, the digital twin unifies, organizes, and contextualizes engineering, operations, and asset reliability and maintenance data across the full industrial life cycle for more informed decision-making. Enhanced with AI and machine learning, it ensures that the data used for predictive maintenance is reliable and actionable, enabling optimal scalability and fast ROI.

Predictive maintenance: Ensuring reliable data

Reliable data is the foundation of any predictive maintenance strategy. Sensor malfunctions can lead to inaccurate analyses, unnecessary shutdowns, or missed anomalies that result in unexpected failures. AVEVA Predictive Analytics addresses this challenge with robust sensor fault detection capabilities.

On average, one-quarter of all issues detected are related to sensor malfunctions. Repairing these sensors can take weeks, during which time unreliable data can compromise decision-making. Sensor malfunctions can also cloud your analytics with bad information. AVEVA Predictive Analytics includes configurable sensor analysis rules that signal bad actors and exclude them from calculations and models. Monitoring users can also explicitly identify sensors for maintenance and manage alerts throughout the corrective process, ensuring that only reliable data informs maintenance decisions.

Fault diagnostics: Proactive maintenance

Accurate fault diagnostics are essential for proactive maintenance. AVEVA Predictive Analytics excels at identifying potential failure modes and providing prescriptive actions to confirm impending faults. By integrating these capabilities with AVEVA PI System, it offers a comprehensive view of asset health and performance, facilitating timely and informed maintenance decisions.

Predictive results are written back to AVEVA PI System and displayed in context. This integration provides a holistic view of assets, incorporating predictive analytics fault diagnostics, prescriptive actions, and case library visualizations directly into AVEVA™ PI Vision™ displays. This consistent visualization experience enhances the ability to interpret data and respond to potential issues swiftly.

Time-to-failure forecasting: Planning for the future

Knowing when an asset is likely to fail is invaluable for maintenance planning. AVEVA Predictive Analytics includes robust time-to-failure forecasting capabilities, allowing users to estimate the time to replacement or overhaul of an asset under current operating conditions. This foresight enables more informed and sustainable maintenance decisions, reducing downtime and minimizing maintenance costs.

As part of the alert analysis process, AVEVA Predictive Analytics can forecast the evolution of sensors based on recent historical conditions. The system evaluates the time range and distance to reach a threshold (e.g., bearing temperature, filter differential pressure, vibrations), and determines the urgency of the risk associated with continuing to run the asset under current conditions. This proactive approach helps prioritize maintenance activities and adjust operation parameters accordingly.

Conclusion:

Accurate results are the cornerstone of effective predictive maintenance. AVEVA Predictive Analytics, deeply integrated with AVEVA PI System, delivers unparalleled accuracy through advanced sensor fault detection, fault diagnostics, prescriptive guidance, and time-to-failure forecasting. By ensuring reliable data and providing actionable insights, AVEVA Predictive Analytics empowers organizations to make smarter, more sustainable maintenance decisions and safeguard their operations for the future.