Research on bearing residual life prediction method of coal mine machinery equipment
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摘要: 煤机设备轴承剩余寿命预测对设备维护具有重要意义。现有的轴承剩余寿命预测方法或难以建立精确的轴承失效数学模型，或预测精度受样本完备性和准确性的制约，且退化特征量通常采用时域、频域指标，受煤机恶劣工作环境影响较大，导致预测精度不高。针对该问题，提出一种基于经验模态分解(EMD)和灰色模型(GM)的煤机设备轴承剩余寿命预测方法：采用EMD对煤机设备轴承振动加速度信号进行滤波处理；提取滤波信号的均方根作为表征轴承健康状态的退化特征量，形成退化特征量序列；采用退化特征量序列训练GM，进而建立GM轴承剩余寿命预测模型来预测退化特征量的变化趋势，以退化特征量达到设定阈值的时间间隔作为剩余寿命预测值。试验台试验和工程应用结果表明，该方法可有效预测煤机设备轴承剩余寿命，预测精度较高，预测结果能指导现场设备维护。Abstract: The bearing residual life prediction of coal mine machinery equipment is of great significance for equipment maintenance.The existing bearing residual life prediction methods are either difficult to establish an accurate mathematical model of bearing failure, or the prediction precision is constrained by the sample completeness and accuracy.And the degradation characteristic quantity usually adopts time domain and frequency domain indicators, which are greatly affected by the harsh working environment of coal mine machinery equipment, resulting in low prediction precision.In order to solve this problem, a bearing residual life prediction method of coal mine machinery equipment based on empirical mode decomposition(EMD)and grey model(GM)is proposed.EMD is used to filter the vibration acceleration signal of coal mine machinery equipment bearings.The root mean square of the filtered signal is extracted as the degraded characteristic quantity representing the bearing health state so as to form the degraded characteristic quantity sequence.The GM is trained with the degraded characteristic quantity sequence, then the GM bearing residual life prediction model is established to predict the change trend of the degraded characteristic quantity, and the time interval when the degraded characteristic quantity reaches the set threshold is used as the residual life prediction value.The test bench and engineering application results show that the method can effectively predict the bearing residual life of coal mine machinery equipment with high prediction precision, and the prediction results can guide field equipment maintenance.
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