OPTRAMS
From curve generation to predictive analysis of failures and downtime
OptRAMs is an EYF-developed solution that combines complementary modules to support different stages of reliability analysis. From historical data processing and statistical curve generation to downtime prediction and long-term asset degradation assessment, each module addresses a specific analytical need within a comprehensive workflow.
Curve Fitting
Complete module for data processing, custom filters, outlier detection, failure clustering, and statistical curve fitting to support more consistent reliability analysis and asset behavior assessment.
Data Treatment | Filters | Clustering
Failure Prediction
Module focused exclusively on machine learning for training and running advanced predictive models to forecast and anticipate stoppages based on time series and covariates.
Machine Learning | Time Series | Forecasting
Degradation Module
Advanced reliability and simulation: Weibull/Exponential modeling, Monte Carlo MTBF simulation, competing failure modes, maintenance strategy comparison, and PDF reports.
Monte Carlo | Weibull | Reliability
EXPERIENCE THE FUTURE
Bring greater precision and predictability to your maintenance decisions.
Support industrial decisions with advanced historical data analysis
- Failure and downtime analysis
- Maintenance planning
- Asset behavior analysis
- Reliability and availability assessment
- Failure pattern identification
- Production variability analysis
- Lead time analysis
- Quality deviation analysis
- Resource and spare parts planning
- Maintenance strategy evaluation
Advanced statistical modeling
OptRAMs uses historical data to generate statistical curves that help provide a clearer understanding of asset behavior, failures, and downtime. By evaluating different statistical models, the solution identifies those that best represent the analyzed data and provides a more consistent basis for analysis.
The solution also identifies different behavior patterns, groups similar records, and detects outliers that could affect results. Combined with indicators such as MTBF, MTTR, and availability, these capabilities provide deeper insights to support more precise and data-driven decisions.
Predictive and degradation analysis
OptRAMs uses predictive models to analyze historical data and estimate future downtime, providing greater visibility into how asset behavior may evolve over time and supporting more informed planning.
The solution also evaluates degradation patterns and multiple possible scenarios to compare maintenance strategies based on factors such as expected costs, availability, and risk of unplanned failures. These analyses provide a more consistent basis for assessing alternatives and supporting maintenance decisions.
Technical differentiators for advanced analysis:
- Statistical curve generation
- Multiple distribution fitting
- Weibull and exponential analysis
- Automated model comparison
- Outlier detection and analysis
- Pattern and cluster identification
- Predictive downtime modeling
- Monte Carlo simulation
- MTBF, MTTR and availability indicators
- Maintenance strategy analysis
- Asset degradation analysis
- Automatic predictive model selection
OPTRAMS
Identify outliers, understand asset behavior, and gain deeper insights into reliability
Faq
Frequently asked questions and relevant information
Explore how OptRAMs uses historical data, statistical modeling, and predictive analysis to identify patterns, outliers, and asset behavior, supporting more precise and data-driven decisions.
OptRAMs supports statistical distribution fitting, curve generation, clustering, outlier detection, and the calculation of indicators such as MTBF, MTTR, and availability. These analyses help characterize failure, downtime, and asset behavior using historical data.
OptRAMs tests multiple statistical distributions, including Weibull and exponential models, and compares their performance using established statistical criteria to identify the curves that best represent the analyzed data.
The solution combines clustering methods to separate different behavior profiles with IQR-based analysis to detect outliers. This helps reduce distortions and provides a more consistent basis for statistical modeling.
OptRAMs uses historical data and predictive models to estimate future downtime. Depending on the available data, models can be automatically selected and evaluated using validation metrics to assess prediction performance.
OptRAMs uses statistical degradation models and Monte Carlo simulation to evaluate how asset behavior may evolve over time. It can also compare maintenance strategies considering factors such as availability, expected costs, and risk of unplanned failure.