Up to $500,000. That’s how much oil and gas operators could lose from just one hour of equipment downtime. These losses can add up quickly too. Based on the most recent downtime figures, industry operators face up to 27 days of downtime per year due to faulty equipment, costing potentially $38 million in lost revenue.
If lost money wasn’t problematic enough, a lack of qualified maintenance personnel is exacerbating the issue. Consider this: 50% of energy industry workers are over 45 and expected to retire in the next 5 to 10 years. That’s a lot of subject matter expertise out the door. Meanwhile, equipment continues to age—with four out of ten offshore facilities over 15 years old. It’s a perfect maintenance storm.
What are companies to do when the brain drain of retirement combines with aging assets, resulting in significant downtime and lost revenue? Companies across the industrial landscape have confronted these problems with an assist from AI.
AI prescriptive maintenance provides a framework to optimize asset performance
Given the steep cost of equipment downtime and the need to support a new generation of workers, artificial intelligence-powered prescriptive maintenance solutions are essential tools for extracting peak performance from industrial assets.
Predictive/prescriptive maintenance enables operators to improve asset performance and reliability by automatically:
- Tracking asset health continuously and automatically
- Identifying pending failures days/weeks in advance
- Recommending best actions to remedy emerging issues
- Using system feedback to continuously improve asset performance
Companies have turned to Avathon’s Industrial AI Platform for maintenance help, finding increasing benefits along the way. After implementing Avathon’s AI-powered solution, an oil and gas company streamlined maintenance and realized a 10x ROI improvement in production. In the food and beverage sector, another company used Avathon to avoid $1 million in maintenance costs and unplanned downtime.
“Equipment rarely operates under ideal conditions,” said Avathon’s Director of Solution Architecture Andrea Omidifar. “It’s exposed to multiple repairs, harsh environments and other environmental conditions. AI models are based on understanding real-time conditions, i.e., what is normal today.”
Omidifar lists some common questions customers ask when challenged to minimize asset downtime.
“How can we reduce the time that assets are offline? How can we extract greater savings? How can we fortify the new workforce with information they need to perform at the same levels as more experienced predecessors? And how can we squeeze as much life as possible out of that older equipment that’s still producing?”
AI enabled solutions like the Avathon Industrial AI Platform meet these challenges by empowering companies to efficiently respond to maintenance alerts that matter while minimizing false positives.
Avathon’s Industrial AI platform unlocks three principal asset performance benefits—alert management, explainability and continuous learning—that separate our solution from other approaches to industrial asset maintenance. It’s all about eliminating unplanned downtime, reducing the cost of repairs/asset replacement and improving worker safety and productivity.
Focused predictive maintenance alerts reduce false alarms
Alerts, delivered well in advance of equipment failures, derive from predictive maintenance models built on input from subject matter experts. These models identify and alert on known—as well as previously unknown—-harmful events by exploiting threshold-based deviations that can lead to equipment failure. Such disruptions can knock production offline for hours or days without advance warning. AI models also mitigate noisy alerting processes that can create alert fatigue. Smart alerts and automatic threshold calibration are tuned using SME feedback that categorizes, benchmarks and compares alerts, reducing false positives.
Model explainability validates asset performance recommendations
It’s important to apply AI models to both structured and unstructured datasets to call attention as early as possible to emerging issues. But what happens after that? What is causing the anomalies? How do we understand the progression of an anomaly from the first moment it’s noticed to the present? Industrial AI Platform provides an intuitive user experience for understanding trends and categorizing timely, appropriate responses. It brings contextual intelligence and generative AI tools to bear in comparing previous incidents, deciphering what has gone wrong and how each asset performance issue should be addressed.
Predictive maintenance models must adapt as assets age over time
Analyzing the status of assets as they evolve is as crucial as understanding how they’ve performed in the past. Driven by user knowledge and effective alert management, Avathon’s prescriptive maintenance approach makes it easier to adapt to ‘new normal’ operating states as assets age and maintenance practices and operating environments change. This is also critical to meeting the challenge of maintaining technical expertise as the workforce ages, making knowledge readily available to a new generation of workers.
The innovative technology in Avathon’s Industrial AI Platform enables asset performance data to tell the real-time story of your operating environment so you can stay ahead of downtime and optimize production, delivering the high ROI all industrial customers seek and Avathon’s customers routinely achieve.
To learn more about how Industrial AI Platform identifies and mitigates risks to asset performance over the full asset lifecycle, read our case study and empower your organization to create competitive advantage with predictive/prescriptive asset maintenance.
