为了处理航空发动机传感器所采集的多维时序数据，过去的方法常聚焦于单个样本点的识别，其提出的模型中往往含有大量待调参数并且单变量的挖掘方式会忽视状态参数的延时效应对工作模式挖掘的影响。另外，这些方法对大批量处理含过渡态模式数据的情况考虑欠佳。因此，提出一种能快速自动挖掘多维时序数据中隐含工作模式的新方法，即AutoMiner。该方法通过构造模型的编码代价来求解使编码代价最小的断点组合。在构造编码代价过程中利用标记传播的方法来识别每个片段的工作模式，这一方法生成的软标记成功解决了对过渡态模式的识别问题。本文在某型发动机采集的传感器数据上开展了多组实验，同时进行了可视化的展示，实验证明AutoMiner方法在时序分割和模式挖掘方面的评估指标均优于对比算法。另外，AutoMiner方法还支持并行化计算并且能在更复杂的工作模式挖掘场景中具有良好的可迁移性。

To process multi-dimensional time-series data collected from aero-engine sensors， previous methods tend to focus on the identification of a single sample point and the proposed models often contain a large number of parameters to be tuned， while the single-variable mining approach often ignores the influence of the delayed effect of the state parameters on the identification of working models. In addition， these methods poorly consider the case of large-scale processing of data containing transition state patterns. This paper proposes a new method， named AutoMiner， that can quickly and automatically mine the implied working patterns in multi-dimensional time-series data. The method solves for the combination of breakpoints， minimizing the encoding cost by constructing the encoding cost of the model. In the process of the encoding cost construction， the method of marker propagation is adopted to identify the working mode of each segment， generating soft labels to successfully solve the problem of identifying the transition state mode. Multiple experiments are conducted on the sensor data collected from an engine and presented visually. It is demonstrated that the AutoMiner method is superior to the comparison algorithms in terms of evaluation metrics for both temporal segmentation and pattern mining. In addition， the AutoMiner method supports parallelized computation with good transferability in more complex working pattern mining scenarios.