Predict the likelihood of component failure based on fluid health and operating conditions. Statistical and machine learning models enable proactive maintenance planning and reduced downtime.
Use predictive models to determine when equipment may fail and plan service accordingly.
Failure probability models estimate the likelihood of component failure over time. They combine historical failure data with current fluid condition, usage patterns and environmental factors to calculate risk profiles and forecast remaining useful life.
These models empower maintenance managers to prioritize repairs, stock spares and schedule service when risk is high. Integrate failure models with battery life models and telematics signal maps for comprehensive predictive insights.
| Model Type | Description | Use Case |
|---|---|---|
| Weibull Distribution | Statistical model for time‑to‑failure data with varying hazard rates | Predict wear‑out failures |
| Exponential Distribution | Assumes constant failure rate over time | Early life failures |
| Machine Learning | Algorithms like random forests and neural networks trained on sensor and failure data | Complex systems with multiple variables |
| Proportional Hazards | Cox models predicting failure based on covariates | Incorporate fluid condition and load factors |
Creating accurate failure models requires comprehensive data, appropriate modeling techniques and continuous monitoring.
Follow this four‑step process to build and deploy failure probability models for your fleet.
Compile failure histories, maintenance logs, fluid analysis results and telematics data.
Select statistical or machine learning methods and train models on the data.
Evaluate model performance, then deploy the model within your analytics platform.
Continuously monitor predictions, update models with new data and refine thresholds.
Predictive failure models deliver measurable benefits by reducing downtime and optimizing maintenance plans.
Reduction in unexpected failures
Increase in maintenance planning accuracy
Reduction in spare parts inventory
Uptime improvement
"After building failure probability models using our oil analysis and sensor data, we were able to predict gearbox failures weeks in advance. This allowed us to schedule replacements during planned downtime and cut unplanned stoppages by half."
Reliability Engineer
Answers to common questions about developing and using failure probability models.
Collect time‑to‑failure data, maintenance logs, fluid analysis results, telematics signals and operational variables such as load and temperature. The more comprehensive your dataset, the more accurate your models will be.
Update your models whenever you collect significant new data, such as after a major component change or when you observe a shift in failure rates. Many organisations retrain models quarterly or biannually.
Use reliability engineering techniques like Weibull analysis or partner with an analytics vendor that can supplement your data with industry benchmarks. Start with simpler models and refine them as you gather more data.
Absolutely. Integrate oil analysis metrics like viscosity, particle count and TBN along with usage data to improve model accuracy. Fluid health is often a leading indicator of failure.
Use predicted failure dates and risk scores to schedule inspections, order parts and adjust lubrication schedules. Combine model outputs with other condition monitoring tools to prioritise maintenance.
Explore these resources to enhance your predictive maintenance toolkit.
Visualize telematics signals to anticipate fluid and mechanical issues.
View MapSet vibration limits to detect mechanical wear that affects fluids.
View ThresholdsExplore other predictive maintenance categories to build a complete program.
Failure probability models help you anticipate when and why assets will fail. Use them to schedule service and avoid unexpected downtime.
Combine fluid health, telematics and failure history for accurate models.
Predict remaining useful life and plan service at the optimal time.
Prevent failure by replacing or repairing components before they break.