Fault diagnosis of rolling bearings under varying speeds based on gray level co-occurrence matrix and DCCNN

共现矩阵 灰度级 断层(地质) 灰色(单位) 基质(化学分析) 工程类 计算机科学 地质学 人工智能 材料科学 地震学 复合材料 医学 图像纹理 图像(数学) 放射科 图像处理
作者
Fang Liu,Liang Chen,Zhihao Guo,Weizheng Zhao,Xinyu Huang,Qihao Zhou,Feiyun Cong
出处
期刊:Measurement [Elsevier BV]
卷期号:235: 114955-114955 被引量:6
标识
DOI:10.1016/j.measurement.2024.114955
摘要

Rolling bearings are widely used in various industries, including rail transit, aerospace, and wind power generation, playing a critical role. However, bearing failures can lead to serious consequences, impacting equipment operation and even causing safety accidents. Hence, the diagnosis of bearing faults is of utmost importance. However, the variable speed conditions experienced during bearing operation pose significant challenges to fault diagnosis. To overcome the limitations of traditional methods in diagnosing bearing faults under variable speed conditions, this paper proposes a fault diagnosis method based on the gray-level co-occurrence matrix (GLCM) and Dual Channel Convolutional Neural Network (DCCNN). The method introduces a two-dimensional grayscale matrix construction (2D-GMC) technique to extract grayscale texture features for fault diagnosis. Additionally, an unconventional kernel design method, based on grayscale image contrast, is proposed to reduce the complexity associated with traditional square kernels. A new DCCNN architecture is developed accordingly. Furthermore, the transfer learning concept is utilized to train the proposed DCCNN model using fault signals at specific rotational speeds. The method intercepts the variable speed into multi-speed short-time series, then constructs gray image under different speed to realize the rapid fault diagnosis of bearings under variable speed conditions.
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