How much does equipment failure and process downtime cost you?
When an equipment failed, it disrupt the operation and productivity, and may have adverse effect on the delivery and service level agreement. Furthermore, it may cause health and security issue to the personnel and other organisation's assets.
With Predictive Analytics, you get the help you need to prioritize and plan your maintenance procedures, which can result in:
- reduced maintenance
- reduced overtime and equipment replacement costs
- reduced damage to critical equipment
- avoided unexpected shutdowns and catastrophic failures
- improved safety
Identify and fix equipment problems before they fail
Predictive Analytics can identify impending equipment failures well before they happen—often weeks or months before other systems. With this added lead time, you can transform from reactive maintenance to proactive maintenance and help avoid surprise equipment failure and unscheduled shut-down.
Realize full benefit from existing data infrastructure investment
Predictive Analytics leverages your existing data infrastructure to provide early and actionable warnings of impending equipment and process problems.
More effective workforce fixing problems rather than looking for them
It allows your workforce to focus on fixing problems rather than spending countless paid man-hours looking through data, diagnosing problems and dismantling equipment to test and look for problems.
Confirms machine health and condition to extend maintenance intervals
Predictive Analytics improves the safety of reliability of your process by reducing wrench time on your equipment. Every time someone opens up a piece of machinery for planned or unplanned maintenance you run the risk of injury and introducing errors that lead to startup failures and rework. The Condition monitoring aspect of Predictive Analytics helps confirm the normal operation of machines so you can make calculated decisions regarding extending the service time between time based planned maintenance.
Easy to implement. Easy to use. Easy to maintain. Easy to scale.
The software is easy to implement and use. And, it provides information in a way that you can standardize and leverage across your organization—across all your critical rotating and non-rotating equipment, across your fleet. It can detect the broadest range of equipment problems over the widest variety of assets, load ranges, and failure modes.
Preserve experienced workforce practices and empower developing engineers
Predictive Analytics helps you increase availability, reliability, efficiency, and profitability—and, at the same time, overcome challenges of inexperienced and aging workforces, aging equipment, limited budgets, and data overload.
Many of the largest companies in the world integrate Predictive Analytics in to their performance improvement initiatives from across the industrial landscape: Power Generation, Mining, E & P and Refining, Commercial Aviation, Transportation.
OSA makes this capability now available to mid-sized and small operations providing the same value and return on investment as the large companies. In most cases the ROI is less than a year or even in one avoided failure incident.
How it works
We use a data-driven empirical approach that leverages your existing instrumentation and IT infrastructures. Our software constantly samples data from the Historian and analyzes the data to detect, diagnose, and prioritize impending problems.
Each piece of equipment is unique, as reflected in its own historical data. The advanced modeling, protected by over 40 patents, puts into context the normal operating relationships among all relevant parameters, such as load, temperatures, pressures, vibration readings, and ambient conditions. In real time, the software compares actual sensor readings to that particular machine’s normal, predicted values.
The software detects and identifies events and abnormal behavior by the differences between real-time, actual data and predicted, normal behavior—not by thresholds on actual values. The software then provides exception-based notifications of developing problems to users, along with diagnoses and prioritizations. This enables information management by exception. The software does this automatically, continuously, and relentlessly, 24 hours/day.
The system is readily scalable because of the data-driven approach to modeling and the configurable model templates. By using the data-driven approach, we do not rely on machine-specific, First Principles approaches, such as performance curves. A typical early incident alert occurs 3-10 weeks before detected by other systems such as DCS alarms, OEM condition monitors, or alarm managers.
We provide complete professional support and services to our customers: Implementation, training, maintenance, monitoring, and notification. We can provide full-service deployment, maintenance, and monitoring services.
Engage with us now to determine feasibility and value.
Speed to Value Exercise
- Review existing sensor list versus historical failure types
- Evaluate data infrastructure connectivity, history, and accessibility
- GAP ANALYSIS: what failures can predictive analytics catch using existing sensors and data systems?
- Economic Evaluation: calculate value from Gap Analysis based on past performance and economics.
- Deliver and present a report and proposal