Accurate causal discovery is significant for the data-driven root cause diagnosis. A novel framework based on modified temporal convolution and multi-head self-attention (MTCMS) is proposed for causal discovery and root cause diagnosis in multivariate time series. The temporal convolutional network is modified through feature reconstruction and skip connection to heighten the ability of feature extraction, which are further used to mine causalities. The multi-head self-attention is modified via threshold normalization for quantifiable causal inference, elevating the accuracy and generalization performance. Additionally, the calculation rule of root node score based on contrastive causal graph is proposed for root cause diagnosis. The MTCMS network outperforms all ablation structures and comparison methods in the causal discovery experiment based on synthetic data, manifesting its effectiveness and superiority. In the root cause diagnosis experiment based on the Tennessee Eastman benchmark process, the diagnosis result is consistent with the mechanism analysis, which further demonstrates its effectiveness.

准确的因果发现对于数据驱动的根本原因诊断具有重要意义。提出了一种基于改进的时间卷积和多头自我注意（MTCMS）的新框架，用于多变量时间序列中的因果发现和根本原因诊断。通过特征重构和skip connection对时间卷积网络进行修改，以提高特征提取能力，进一步用于挖掘因果关系。多头自注意力通过阈值归一化进行修改，以实现可量化的因果推理，提高了准确性和泛化性能。此外，提出了基于对比因果图的根节点得分计算规则，用于根因诊断。MTCMS网络在基于合成数据的因果发现实验中优于所有消融结构和比较方法，显示出其有效性和优越性。在基于田纳西伊士曼基准流程的根本原因诊断实验中，诊断结果与机理分析一致，进一步证明了其有效性。