Rolling bearings are critical components in rotating machinery, and their faults can cause severe damage. Early detection of abnormalities is crucial to prevent catastrophic accidents. Traditional and intelligent methods have been used to analyze time series data, but in real-life scenarios, sensor data is often noisy and cannot be accurately characterized in the time domain, leading to mode collapse in trained models. Two-dimensionalization methods such as the Gram angle field method (GAF) or interval sampling have been proposed, but they lack mathematical derivation and interpretability. This paper proposes an improved GAF combined with grayscale images for convolution scenarios. The main contributions include illustrating the feasibility of the approach in complex scenarios, widening the data set, and introducing an improved convolutional neural network method with a multi-scale feature fusion diffusion model and deep learning compression techniques for deployment in industrial scenarios.
翻译:滚动轴承是旋转机械中关键的组成部分,其故障可能导致严重损害。早期检测异常对于预防灾难性事故至关重要。传统方法和智能方法已被用于分析时间序列数据,但在现实场景中,传感器数据往往存在噪声,无法在时间域内准确描述,导致训练模型的模式崩溃。已经提出了二维化方法,如格角场方法(GAF)或区间采样,但它们缺乏数学推导和解释性。本文提出一种改进的GAF结合灰度图像用于卷积场景。主要贡献包括说明该方法在复杂场景中的可行性,扩大数据集,并引入改进的卷积神经网络方法,该方法具有多尺度特征融合扩散模型和深度学习压缩技术,可用于工业场景中的部署。