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的关键部件，其故障可能造成严重损坏。早期异常检测对预防灾难性事故至关重要。传统方法与智能方法已被用于分析时间序列数据，但在实际场景中，传感器数据往往含有噪声，且无法在时域中准确表征，导致训练模型出现模态坍塌。虽然已提出格拉姆角场法(GAF)或区间采样等二维化方法，但这些方法缺乏数学推导与可解释性。本文提出一种结合灰度图像的改进型GAF方法，适用于卷积场景。主要贡献包括：阐明该方法在复杂场景中的可行性、扩充数据集，并引入一种具有多尺度特征融合扩散模型与深度学习压缩技术的改进型卷积神经网络方法，以部署于工业场景。