为了准确地实现电梯故障诊断，提出基于AO-VMD-BF和多模型融合的电梯故障诊断。首先，利用天鹰优化算法（aquila optimizer algorithm, AO）优化的变分模态分解（variational mode decomposition, VMD）将信号分解为多个模态分量，并利用皮尔逊相关系数去除虚假分量，针对剩余信号仍有噪声的问题，通过巴特沃斯滤波（Butterworth filter, BF）进行二次去噪，对去噪筛选后的模态分量子序列进行重构即可得到去噪后的振动信号。然后提取时域、频域和熵特征，构成多域特征向量集。最后建立以卷积神经网络（convolutional neural network, CNN）、随机森林（random forest, RF）、支持向量机（support vector machine, SVM）和自适应提升（adaptive boosting, AdaBoost）为基模型，极限梯度提升树（extreme gradient boosting, XGBoost）为元分类器的Stacking集成学习的电梯故障诊断模型。实验结果表明，所提的方法能够有效提取电梯轿厢振动信号中的故障特征，对电梯故障进行准确、有效的诊断。

In order to accurately achieve elevator fault diagnosis, an elevator fault diagnosis based on AO-VMD-BF and multi model fusion was proposed. Firstly, the variational mode decomposition(VMD) optimized by the aquila optimizer(AO) algorithm was used to decompose the signal into multiple modal components, and the pearson correlation coefficient was used to remove false components. To address the problem of noise in the remaining signal, the butterworth filter(BF) was used for secondary denoising, the denoised vibration signal could be obtained by reconstructing the filtered modal sub quantum sequence. Then extract time-domain, frequency-domain, and entropy features to form a multi domain feature vector set. Finally, a Stacking ensemble learning elevator fault diagnosis model was established based on convolutional neural network(CNN), random forest(RF), support vector machine(SVM), and adaptive boosting(AdaBoost) models, with extreme gradient boosting(XGBoost) as the meta classifier. The analysis of experimental results shows that the proposed method can effectively extract fault features from elevator car vibration signals, and accurately and effectively diagnose elevator faults.