Introducing the FailSafe Analyzer: The Future of Equipment Longevity Prediction
Every tool has a story, and the “FailSafe Analyzer” was born out of a unique real-world challenge we encountered.
During an extensive RCA for a client, we stumbled upon an intriguing anomaly: two identical plants displayed vastly different Asset Criticality. The root of this discrepancy? Different teams assessed each plant, leading to inconsistent evaluations. Our investigation revealed that the source of this variability was not from the impact assessment, as one might assume, but from the probability component. This subjective interpretation of probability posed a critical challenge.
Recognizing the pressing need for an objective and consistent measure, we embarked on a mission to eliminate this variability. The result? The FailSafe Analyzer.
Why Choose FailSafe Analyzer?
🔧 Objective Probability Assessment: Say goodbye to subjective assessments. With our tool, you get a standardized probability evaluation, ensuring consistent results across teams and plants.
📊 Precise Weibull Parameters: Detailed beta and eta values for each item offer unparalleled prediction accuracy.
🌐 User-Friendly Design: Navigate and utilize the tool with ease, regardless of familiarity with Weibull distribution.
📈 Optimized Preventive Maintenance: With accurate forecasts, maintenance can be proactive, reducing outages and extending equipment lifespan.
Born from hands-on experience and refined by the challenges of real-world scenarios, the FailSafe Analyzer doesn’t just predict—it solves. Ensure consistency, reduce unpredictability, and embrace the next generation of predictive maintenance tools today.
Please note that: the Beta and Eta parameters are suggested based on Bloch, Heinz P. and Fred K. Geitner, 1994, Practical Machinery Management for Process Plants, Volume 2: Machinery Failure Analysis and Troubleshooting, 2nd Edition, Gulf Publishing Company, Houston, TX and Breinger1.com (this site is down at the moment), These numbers are general and shall be replaced using the user actual historical data based on usage operation profile.
If you have historical failure data and want to estimate the weibull parameters please stay tuned for the next article.
