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Selecting statistical model and optimum maintenance policy: a case study of hydraulic pump
1Department of Statistics, Pabna University of Science and Technology, Pabna, Bangladesh.
Springerplus
|July 19, 2016
Summary
A Weibull-Normal-Exponential mixture model effectively analyzes hydraulic pump failures in mining operations. This statistical approach helps determine optimal maintenance schedules to minimize costs and enhance product reliability.
Area of Science:
- Engineering
- Reliability Engineering
- Statistical Modeling
Background:
- Effective maintenance policies are crucial for assessing product reliability in engineered systems.
- Both preventive and corrective maintenance are essential for the longevity of products, plants, and infrastructure.
Purpose of the Study:
- To analyze hydraulic pump failures in a mining company's excavator fleet.
- To develop statistical models for predicting pump failures and optimizing maintenance strategies.
Main Methods:
- Collected and analyzed failure and censored lifetime data for hydraulic pumps.
- Applied and compared various mixture models (e.g., Weibull-Normal-Exponential) to the maintenance data.
- Utilized the EM algorithm for parameter estimation and employed statistical tests (AIC, AD, KS, RMSE) for model selection.
Main Results:
- A threefold mixture model (Weibull-Normal-Exponential) demonstrated a strong fit for the hydraulic pump failure data.
- Key reliability metrics, including cumulative distribution function and mean time to failure, were estimated.
- The study identified the best-performing statistical models for assessing pump reliability.
Conclusions:
- The Weibull-Normal-Exponential mixture model is suitable for analyzing hydraulic pump failures in this case study.
- Statistical modeling can guide the estimation of optimal maintenance periods to reduce costs and improve pump lifespan.
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