目的 针对包装设备长期连续工作的工作特性，对其滚动轴承的常见失效故障进行分析，提出一种基于DS–PCA模型的滚动轴承故障诊断方法，构建滚动轴承DS–PCA故障诊断模型，实现包装设备中滚动轴承的在线故障诊断。方法 先运用DS证据理论对采集到的滚动轴承径向振动数据和轴向振动数据进行融合，使得信息具有整体完备性，同时对采集信号进行决策规则下的去噪处理，剔除干扰噪声信息;然后利用主成分分析法(PCA)将融合后的振动信号数据进行Q与T²的统计量计算，并通过对故障轴承振动信号的Q与T²统计量计算，确立故障时的经验阈值;最后，依据实时统计量与经验阈值对比，判断滚动轴承是否发生故障。结果 通过对西储大学公开的滚动轴承试验数据分析计算，得到滚动轴承故障诊断准确率达到94%。结论 该方法满足包装设备故障诊断的要求，其应用将有利于提升包装企业的生产质量和效率。

The work aims to propose a rolling bearing fault diagnosis method based on DS-PCA model to analyze the common failure faults of rolling bearings caused by the long-term continuous working characteristics of packaging equipment and construct a DS-PCA fault diagnosis model to realize the online fault diagnosis of rolling bearings in packaging equipment. Firstly, DS evidence theory was used to fuse the collected radial vibration data and axial vibration data of rolling bearings to make the information have overall completeness. At the same time, the collected signals were denoised under the decision rules to eliminate the interference noise information. Then, the principal component analysis (PCA) was used to calculate the Q and T2 statistics of the fused vibration signal data, and the empirical threshold was established by calculating the Q and T2 statistics of the vibration signal of the failed bearings. Finally, according to the comparison between the real-time statistics and empirical threshold, it was judged whether the rolling bearings had failure. Through the analysis and calculation of the experimental data of rolling bearings published by Case Western Reserve University, the accuracy of rolling bearing fault diagnosis reached 94%. The method meets the requirements of packaging equipment fault diagnosis, and its application will help improve the production quality and efficiency of packaging enterprises.