The invention discloses an elevator traction machine fault diagnosis algorithm based on multi-source information fusion, and the algorithm comprises the following steps: collecting the vibration signal data of a multi-source sensor under a specific fault and a normal working condition, and carrying out the denoising processing through an improved adaptive filter, so as to improve the data quality; decomposing the denoised vibration signal into a plurality of intrinsic mode functions by using an empirical mode decomposition technology; the method comprises the following steps: constructing a multi-scale time sequence, calculating a sample entropy of each scale, and obtaining a multi-scale entropy vector of a single intrinsic mode function; on this basis, calculating a covariance matrix of a multi-scale entropy vector, extracting principal components, and selecting the first several principal components with the highest contribution rate as a fusion result; and inputting the tractor signal data with the label into a support vector machine, carrying out supervised learning training, and carrying out pattern recognition and classification on the fused signal data by utilizing a trained model. Through effective fusion of multi-source information, the precision and efficiency of fault diagnosis of the elevator traction machine are remarkably improved, and the method has wide application prospects and important practical significance.
本发明公开了一种基于多源信息融合的电梯曳引机故障诊断算法,包括以下步骤:采集特定故障及正常工况下的多源传感器振动信号数据,并应用改进的自适应滤波器进行去噪处理,以提高数据质量;利用经验模态分解技术,将去噪后的振动信号分解为多个固有模态函数;通过构建多尺度时间序列,计算每个尺度的样本熵,获得单个固有模态函数的多尺度熵向量;在此基础上,计算多尺度熵向量的协方差矩阵,提取主成分,并选择贡献率最高的前几个主成分作为融合结果;将带有标签的曳引机信号数据输入至支持向量机中,进行监督学习训练,利用训练好的模型对融合后的信号数据实施模式识别与分类。本发明通过多源信息的有效融合,显著提升了电梯曳引机故障诊断的精度和效率,具有广泛的应用前景和重要的实际意义。
Elevator traction machine fault diagnosis algorithm based on multi-source information fusion
基于多源信息融合的电梯曳引机故障诊断算法
2024-12-03
Patent
Electronic Resource
Chinese
IPC: | B66B Aufzüge , ELEVATORS |
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