Planetary gearbox fault classification based on tooth root strain and GAF pseudo images

IF 6.3 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
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引用次数: 0

Abstract

Traditional signal processing methods based on acceleration signals can determine whether a fault has occurred in a planetary gearbox. However, acceleration signals are severely affected by interference, causing difficulties in fault identification. This study proposes a gear fault classification method based on root strain and pseudo images. Firstly, fiber optic sensors are employed to directly acquire strain data from the ring gear root. Next, the strain signals are preprocessed using resampling and a time-domain synchronous averaging algorithm. The processed signals are encoded into two-dimensional images using Gramian Angular Fields (GAF). Then, CN-EfficientNet with contrast learning is proposed to analyze and extract deeper fault features from the image texture features. In the classification experiments for different types of faults, the accuracy reached 96.84%. The results indicate that the method can effectively accomplish the task of fault classification in planetary gearboxes. Comparative experiments with other common classification models further indicate the superior performance of the proposed learning model. Visualization based on Grad-CAM provides interpretability for the fault recognition network’s results and reveals the underlying mechanism for its excellent classification performance.

基于齿根应变和 GAF 伪图像的行星齿轮箱故障分类。
基于加速度信号的传统信号处理方法可以确定行星齿轮箱是否发生故障。然而,加速度信号受干扰影响严重,给故障识别带来困难。本研究提出了一种基于根应变和伪图像的齿轮故障分类方法。首先,采用光纤传感器直接获取环形齿轮根部的应变数据。然后,使用重采样和时域同步平均算法对应变信号进行预处理。处理后的信号使用格拉米安角场(GAF)编码成二维图像。然后,提出了具有对比度学习功能的 CN-EfficientNet,以从图像纹理特征中分析和提取更深层次的故障特征。在不同类型故障的分类实验中,准确率达到 96.84%。结果表明,该方法能有效完成行星齿轮箱的故障分类任务。与其他常见分类模型的对比实验进一步表明了所提出的学习模型的优越性能。基于 Grad-CAM 的可视化为故障识别网络的结果提供了可解释性,并揭示了其优异分类性能的内在机制。
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来源期刊
ISA transactions
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.
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