Hu Qingming, Fu Xinjie, Guan Yanqi, Wu Qingtao, Liu Shang
School of Mechanical and Electrical Engineering, Qiqihar University, Qiqihar 161006, China.
The Engineering Technology Research Center for Precision Manufacturing Equipment and Industrial Perception of Heilongjiang Province, Qiqihar University, Qiqihar 161006, China.
Sensors (Basel). 2024 Aug 15;24(16):5285. doi: 10.3390/s24165285.
In recent years, single-source-data-based deep learning methods have made considerable strides in the field of fault diagnosis. Nevertheless, the extraction of useful information from multi-source data remains a challenge. In this paper, we propose a novel approach called the Genetic Simulated Annealing Optimization (GASA) method with a multi-source data convolutional neural network (MSCNN) for the fault diagnosis of rolling bearing. This method aims to identify bearing faults more accurately and make full use of multi-source data. Initially, the bearing vibration signal is transformed into a time-frequency graph using the continuous wavelet transform (CWT) and the signal is integrated with the motor current signal and fed into the network model. Then, a GASA-MSCNN fault diagnosis method is established to better capture the crucial information within the signal and identify various bearing health conditions. Finally, a rolling bearing dataset under different noisy environments is employed to validate the robustness of the proposed model. The experimental results demonstrate that the proposed method is capable of accurately identifying various types of rolling bearing faults, with an accuracy rate reaching up to 98% or higher even in variable noise environments. The experiments reveal that the new method significantly improves fault detection accuracy.
近年来，基于单源数据的深度学习方法在故障诊断领域取得了长足的进步。然而，从多源数据中提取有用信息仍然是一个挑战。在本文中，我们提出了一种新颖的方法，即遗传模拟退火优化（GASA）方法与多源数据卷积神经网络（MSCNN）相结合，用于滚动轴承的故障诊断。该方法旨在更准确地识别轴承故障，并充分利用多源数据。首先，使用连续小波变换（CWT）将轴承振动信号转换为时频图，并将该信号与电机电流信号集成，然后输入到网络模型中。接着，建立了GASA-MSCNN故障诊断方法，以更好地捕捉信号中的关键信息并识别各种轴承健康状况。最后，使用不同噪声环境下的滚动轴承数据集来验证所提出模型的鲁棒性。实验结果表明，所提出的方法能够准确识别各种类型的滚动轴承故障，即使在可变噪声环境下，准确率也能达到98%或更高。实验表明，新方法显著提高了故障检测精度。
