1. Institute of Sound and Vibration Research, Hefei University of Technology, Hefei, Anhui 230009, China
2. Automotive NVH Engineering & Technology Research Center Anhui Province, Hefei, Anhui 230009, China
Abstract：To solve the problem that traditional fault diagnosis methods of rolling bearing rely on artificial feature extraction and expert experience, and it is difficult to self-adapt to extract weak fault features of strong noise signals, an intelligent diagnosis method combining histogram equalization and convolutional neural network (CNN) is proposed. First, the one-dimensional vibration signal collected by the sensor is transformed into a two-dimensional vibration image that is easy to be recognized by the model through Transversal interpolation. Histogram equalization technology is used to stretch the dynamic range of gray value difference between pixels, highlight texture details and contrast, and enhance periodic fault characteristics. Then, a deep CNN model is constructed, and the optimization technology is used to reduce the model parameters, and the complex mapping relationship between monitoring data and fault state is learned layer by layer. The experimental results show that this method has a high accuracy of more than 99%, and still has a high identification accuracy and generalization ability for fault signals under different loads.
［1］时培明, 苏翠娇, 赵娜, 等. 基于AMD和自适应随机共振的旋转机械故障诊断方法研究［J］. 计量学报, 2017, 38(1): 112-116.
Shi P M, Su C J, Zhao N, et al. Study on Fault Diagnosis Method for Rotating Machinery Based on Adaptive Stochastic Resonance and AMD ［J］. Acta Metrologica, Sinica, 2017, 38(1): 112-116.
［2］孟宗, 岳建辉, 邢婷婷, 等. 基于最大幅值变分模态分解和均方根熵的滚动轴承故障诊断［J］. 计量学报, 2020, 41(4): 455-460.
Men Z, Yue J H, Xing T T, et al. Rolling Bearing Fault Diagnosis Based on Maximum Amplitude Variational Mode Decomposition and Root Mean Square Entropy ［J］. Acta Metrologica Sinica, 2020, 41(4): 455-460.
［3］陈剑,黄凯旋,吕伍佯,等. 基于VMD和卷积神经网络的变工况轴承故障诊断方法［J］. 计量学报, 2021, 42(7): 892-897.
Chen J, Huang K X, Lv W Y, et al. Bearing Fault Diagnosis Method Based on VMD and Convolutional Neural Network Undervarying Operation Conditions［J］. Acta Metrologica Sinica, 2021, 42(7): 892-897.
［3］时培明, 梁凯, 赵娜, 等. 基于分形维数和GA-SVM的风电机组齿轮箱轴承故障诊断［J］. 计量学报, 2018, 39(1): 61-65.
Shi P M, Liang K, Zhao N, et al. Fault Diagnosis of Wind Turbine Gearbox Bearing Based on Fractal Dimension and GA-SVM［J］. Acta Metrologica Sinica, 2018, 39(1): 61-65.
［4］汪朝海, 蔡晋辉, 曾九孙. 基于经验模态分解和主成分分析的滚动轴承故障诊断研究［J］. 计量学报, 2019, 40(6): 1077-1082.
Wang C H, Cai J H, Zeng J S, et al. Research on Fault Diagnosis of Rolling Bearing Based on Empirical Mode Decomposition and Principal Component Analysis ［J］. Acta Metrologica Sinica, 2019, 40(6): 1077-1082.
［5］Young T, Hazarika D, Poria S, et al. Recent trends in deep learning based natural language processing［J］. IEEE Computational Intelligence Magazine, 2017, 13(3): 55-75.
［6］曲建岭, 余路, 袁涛, 等. 基于一维卷积神经网络的滚动轴承自适应故障诊断算法［J］. 仪器仪表学报, 2018, 39(7): 134-143.
Qu J L, Yu L, Yuan T, et al. Adaptive fault diagnosis algorithm for rolling bearings based on one-dimensional convolutional neural network［J］. Chinese Journal of Scientific Instrument. 2018, 39(7): 134-143.
［7］时培明, 赵娜, 苏冠华, 等. 变载荷齿轮箱故障信号智能检测方法［J］. 计量学报, 2018, 39(6): 847-851.
Shi P M, Zhao N, Su G H, et al. Intelligent Detection Method of Variable Load Gearbox Fault Signal［J］. Acta Metrologica Sinica, 2018, 39(6): 847-851.
［8］宫文峰, 陈辉, 张泽辉, 等. 基于改进卷积神经网络的滚动轴承智能故障诊断研究［J］. 振动工程学报, 2020, 33(2): 400-413.
Gong W F, Chen H, Zhang Z H, et al. Intelligent fault diagnosis for rolling bearing based on improved convolutional neural network ［J］. Journal of Vibration Engineering, 2018, 31(5): 883-891.
［9］Choi W. Huh H, Tama B A, et al. A neural network model for material degradation and diagnosis using microscopic images［J］. IEEE Acess, 2019, 7: 92151-92160.
［10］Gao L, Li X Y, Wen L, et al. A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method［J］. IEEE Transactions on Industrial Electronics, 2018, 65(7): 5990-5998.
［11］Chen Z Q, Li C, Sanchez R V. Gearbox fault identification and classification with convolutional neural networks［J］. Shock and Vibration, 2015(2):1-10.
［12］Hinton G E. Visualizing high-dimensional data using t-SNE［J］. Vigiliae Christianae, 2008, 9(2): 2579-2605.
［13］Kang S, Mad, Wang Y, et al. Method of assessing the state of a rolling bearing based on the relative compensation distance of multiple-domain features and locally linear embedding［J］. Mechanical Systems & Signal Processing, 2017, 86: 40-57.
2022, Vol. 43 
