The health of complex systems continues to decline during extended periods of operation, leading to increasing concerns about reliability and safety across various industries. A newly proposed health assessment method, utilizing the multiexpert belief rule base (BRB-ME), aims to tackle these challenges effectively by integrating knowledge from multiple experts, ensuring more reliable assessments.
Complex systems, commonly found in fields such as aerospace, rail transportation, and energy management, often present unique challenges when assessing health states due to their multi-dimensional nature and the complexity of interrelated components. With these systems accumulating extensive operational hours, the prospect of failure increases, which can significantly disrupt operations and even jeopardize safety.
The BRB-ME model combines semi-quantitative methodologies and belief rule bases to address uncertainty and complexity prevalent among expert knowledge sets. Traditional assessment methods alone face limitations due to either dependency on incomplete data or excessive assumptions needed for physical modeling, leading researchers to seek innovative strategies to improve accuracy and interpretability. The BRB-ME method, presented by the authors of the article, aims to address the inconsistencies and incompleteness of expert knowledge by developing a comprehensive framework capable of fusing insights from various experts.
A major advantage of the BRB-ME model lies in its approach to optimizing the expert information fusion process. Experts independently construct their models before applying the multiexpert knowledge fusion algorithm, which is powered by the evidential reasoning (ER) inference machine — generating recommendations bolstered by multiple perspectives. This innovative model has demonstrated remarkable effectiveness through practical applications, including the assessment of the health state of lithium-ion batteries and flywheels.
The health assessment process with the BRB-ME model begins with collecting baseline operational data, where experts sift through historical data and identify key metrics relevant to health state evaluation. The model employs unique algorithms to fuse these metrics, taking advantage of the strengths of the belief rule base to achieve interpretable outputs. This methodology provides clarity and enhances overall reliability by effectively managing uncertainties associated with different systems.
"The BRB-ME model can fuse multiexpert knowledge and has advantages in terms of the stability and accuracy of assessment results," the authors highlight, emphasizing the significance of collaborative expert input. Continuous optimization mechanisms are then applied to refine the model, resulting in consistent predictions for the health state, positioning the BRB-ME model as superior compared to previous methodologies.
Through comparative studies, the BRB-ME model outperformed other conventional models, as demonstrated by significantly lower metrics for Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) across various tests. The study not only emphasizes the effectiveness of the new approach but also showcases its robustness — reinforcing the idea of relying on collaborative input to strengthen predictions.
Though promising, the authors acknowledge challenges correlational with the complexity of the model. They assert, "To address the limitations of single expert knowledge assessment, we propose a new multiexpert complex system health state assessment model." This reflects their commitment to continual improvements, focusing on both interpretability and complexity, and establishing protocols for effective fusion of expert knowledge.
Looking to the future, the BRB-ME model not only stands as a novel approach to health assessments of complex systems but also encourages the utilization of diverse expert insights to mitigate assessment inaccuracies. The research establishes bare paths for systems requiring high reliability and emphasizes the value of knowledge sharing within expert communities.
Conclusively, the BRB-ME method shows considerable promise for enhancing health assessments across many sectors, ensuring higher accuracy and reliability, which remain pivotal in managing complex systems. Future research directions may include refining optimization techniques and exploring additional fusion methodologies as the need for more adaptable assessment systems grows.