发表机构
University of Patras（帕特雷大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出谐波回归ARMA模型，结合显式谐波与ARMA部分，在多模型框架下实现变工况旋转机械故障检测，实验达到98%真阳性率。
AI 中文摘要
变工况下旋转机械的故障检测仍是一个具有挑战性的问题。现有方法主要基于非参数振动信号表示、参数化深度学习模型或参数化ARMA型模型。深度学习方法往往缺乏透明性且需要大量训练数据集，而ARMA型模型依赖于纯有理谱的假设，这对于具有混合频谱特性的旋转机械振动信号可能具有限制性。为解决这些局限性，提出了一种新颖的谐波回归ARMA（HR-ARMA）模型用于旋转机械动力学辨识，该模型将用于表示主要确定性周期分量的显式谐波回归部分与用于剩余宽带随机动力学的ARMA部分相结合。基于该模型并在多模型框架内，实现了变工况下的故障检测。通过单级齿轮箱的仿真和实验研究验证了HR-ARMA建模性能，结果表明其能准确表示周期和宽带动力学，与传统ARMA模型相比具有更好的简约性，且故障检测性能显著提高，在实验研究中实现了至少98%的真阳性率（在5%假阳性率下）。
英文摘要
Fault detection in rotating machinery under varying operating conditions remains a challenging problem. Existing approaches are mainly based either on non-parametric vibration signal representations, parametric deep learning models, or on parametric ARMA-type models. While deep learning approaches often lack transparency and require large training datasets, ARMA-type models rely on the assumption of a purely rational spectrum, which can be restrictive for rotating machinery vibration signals exhibiting mixed spectral characteristics. To address these limitations, a novel Harmonic Regression ARMA (HR-ARMA) model is presented for rotating machinery dynamics identification, which combines an explicit harmonic regression part for representing the dominant deterministic cyclical components with an ARMA part for the remaining broadband stochastic dynamics. Based on this model and within a Multiple Model framework, fault detection is achieved under varying operating conditions. The HR-ARMA modelling performance is validated through simulation and experimental studies on a single-stage gearbox, demonstrating accurate representation of both cyclical and broadband dynamics, improved parsimony and significantly higher fault detection performance compared with conventional ARMA models, achieving at least 98% True Positive Rate at 5% False Positive Rate in the experimental study.
CommentsAccepted for publication in the Proceedings of the International Conference on Noise and Vibration Engineering (ISMA 2026), Leuven, Belgium, 2026. 15 pages, 6 figures
