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A Rapid Method for Modeling a Variable Cycle Engine

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A New Interval Belief Rule Base Model Based on Hybrid Optimization and Adaptive Reference Intervals for Diesel Engine

Hongming Zheng¹, Bing Xu¹, Motong Zhao²

¹School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China.

Sensors (Basel, Switzerland)

|May 4, 2026

Summary

This study introduces an advanced Interval Belief Rule Base (IBRB) method for diesel engine health assessment, improving accuracy by optimizing intervals and using hybrid optimization. The new IBRB-HOAI method significantly reduces Mean Squared Error (MSE) for better engine diagnostics.

Keywords:

adaptive reference intervals health state assessment hybrid optimization interval belief rule base

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Area of Science:

Engineering
Computer Science

Background:

Accurate diesel engine health assessment is crucial for electromechanical systems.
Engine parameters degrade over time due to wear and aging.
Existing optimization methods face challenges in balancing global and local search efficiency.

Purpose of the Study:

To propose an improved Interval Belief Rule Base (IBRB) method for enhanced diesel engine health assessment.
To address limitations in dynamic parameter changes and optimization efficiency.

Main Methods:

Introduced an adaptive reference interval using K-means clustering and quantile interval estimation.
Combined Particle Swarm Optimization (PSO) with Projected Covariance Matrix Adaptation Evolution Strategy (PCMS-ES) for collaborative parameter optimization.
Developed the Interval Belief Rule Base method based on Hybrid Optimization and Adaptive Intervals (IBRB-HOAI).

Main Results:

The proposed IBRB-HOAI method achieved a 97.5% reduction in Mean Squared Error (MSE) compared to the traditional IBRB.
Demonstrated superior performance over other machine learning methods.
Verified effectiveness using a diesel engine dataset with 2700 sample points.

Conclusions:

The IBRB-HOAI method provides a more accurate and applicable approach to diesel engine health assessment.
Hybrid optimization and adaptive intervals enhance model performance and reliability.
The study validates the proposed method's effectiveness in real-world engine diagnostics.