基于半张量积的模糊关系矩阵技术的齿轮系统状态预测。

IF 6.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions Pub Date : 2025-07-23 DOI:10.1016/j.isatra.2025.07.035

Hong L. Lyu , Wilson Wang , Xiao P. Liu

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引用次数: 0

摘要

多变量模糊预测系统由于其复杂的模糊推理结构和命题，往往给建模带来困难。为了解决这一挑战，本文提出了一种层次模糊状态建模技术，用于构建降维（阶数）模糊关系矩阵（FRM），用于系统状态预测。在处理过程中，首先将高维FRM分解为多个低维FRM模型；其次，利用半张量积建立模糊逻辑框架，减少模糊规则的数量；将所提出的层次模糊模型应用于齿轮系统健康状态预测，对系统参数进行训练，提高模糊推理的精度。通过实验验证了所提出的层次FRM建模和系统识别技术的有效性。

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Semi-tensor product-based fuzzy relation matrix technique for gear system state forecasting

Multiple-variable fuzzy prediction systems are usually difficult for modeling due to their complicated fuzzy reasoning structures and propositions. To tackle this challenge, a hierarchical fuzzy state modeling technique is proposed in this work to construct fuzzy relation matrices (FRM) with reduced dimensions (orders), for system state forecasting. In processing, firstly, the FRM with high dimensions is decomposed into several lower-dimensional FRM models. Secondly, using the semi-tensor product, a fuzzy logic framework is developed to reduce the number of fuzzy rules. The proposed hierarchical fuzzy model is also implemented for gear system health state forecasting, where system parameters are trained to improve the fuzzy reasoning accuracy. The effectiveness of the proposed hierarchical FRM modeling and system identification techniques is verified by experimental tests.

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来源期刊

ISA transactions 工程技术-工程：综合

CiteScore

11.70

自引率

12.30%

发文量

824

审稿时长

4.4 months

期刊介绍： ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.