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A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

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A relevance vector machine-based approach with application to oil sand pump prognostics.

Jinfei Hu¹, Peter W Tse

¹The Smart Engineering Asset Management Laboratory (SEAM), Department of Systems Engineering and Engineering Management (SEEM), City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong 999077, China. Peter.W.Tse@cityu.edu.hk.

Sensors (Basel, Switzerland)

|September 21, 2013

Summary

This study introduces a new method using relevance vector machines (RVMs) to predict the remaining useful life (RUL) of oil sand pump impellers, reducing maintenance costs and downtime in mining operations.

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Area of Science:

Mining Engineering
Mechanical Engineering
Predictive Maintenance

Background:

Oil sand pumps face severe wear due to abrasive solids and liquids in mining.
Pump wear leads to substantial maintenance costs and unscheduled operational downtime.
Accurate prediction of remaining useful life (RUL) is crucial for efficient asset management.

Purpose of the Study:

To develop an advanced model for predicting the RUL of field pump impellers.
To introduce a novel feature extraction technique for vibration data analysis.
To improve the accuracy of RUL predictions compared to existing methods.

Main Methods:

A hybrid approach combining relevance vector machines (RVMs) with a sum of two exponential functions.
A novel feature extraction process designed for field vibration data to capture impeller damage progression.
Validation using two distinct field datasets from mining operations.

Main Results:

The developed RVM-based model demonstrated superior performance in predicting pump impeller RUL.
The novel feature extraction method effectively captured damage-related changes in vibration data.
The proposed approach significantly outperformed standalone exponential fitting models.

Conclusions:

The RVM-based approach offers a more accurate and reliable method for predicting pump impeller RUL.
This predictive maintenance strategy can significantly reduce operational costs and enhance equipment lifespan in the mining industry.
The findings provide a valuable tool for optimizing maintenance schedules and preventing unexpected failures.