With the development of intelligent manufacturing and the increasing
complexity of industrial production, root cause diagnosis has gradually become
an important research direction in the field of industrial fault diagnosis.
However, existing research methods struggle to effectively combine domain
knowledge and industrial data, failing to provide accurate, online, and
reliable root cause diagnosis results for industrial processes. To address
these issues, a novel fault root cause diagnosis framework based on knowledge
graph and industrial data, called Root-KGD, is proposed. Root-KGD uses the
knowledge graph to represent domain knowledge and employs data-driven modeling
to extract fault features from industrial data. It then combines the knowledge
graph and data features to perform knowledge graph reasoning for root cause
identification. The performance of the proposed method is validated using two
industrial process cases, Tennessee Eastman Process (TEP) and Multiphase Flow
Facility (MFF). Compared to existing methods, Root-KGD not only gives more
accurate root cause variable diagnosis results but also provides interpretable
fault-related information by locating faults to corresponding physical entities
in knowledge graph (such as devices and streams). In addition, combined with
its lightweight nature, Root-KGD is more effective in online industrial
applications.

基于知识图谱和工业数据的故障根本原因诊断框架 (Root-KGD) 结合领域知识与工业数据特征，通过知识图谱推理进行根本原因识别，相比现有方法在在线工业应用中提供更准确的诊断结果和解释性故障相关信息。

基于知识图谱和工业数据的根因诊断新框架 Root-KGD

Root-KGD: A Novel Framework for Root Cause Diagnosis Based on Knowledge  Graph and Industrial Data

In modern manufacturing, most of the product lines are conforming. Few
products are nonconforming but with different defect types. The identification
of defect types can help further root cause diagnosis of production lines. With
the sensing development, continuous signals of process variables can be
collected in high resolution, which can be regarded as multichannel functional
data. They have abundant information to characterize the process and help
identify the defect types. Motivated by a real example from the pipe tightening
process, we target at detect classification when each sample is a multichannel
functional data. However, the available samples for each defect type are
limited and imbalanced. Moreover, the functions are partially observed since
the pre-tightening process before the pipe tightening process is unobserved. To
classify the defect samples based on imbalanced, multichannel, and partially
observed functional data is very important but challenging. Thus, we propose an
innovative framework known as "Multichannel Partially Observed Functional
Modeling for Defect Classification with an Imbalanced Dataset" (MPOFI). The
framework leverages the power of deep metric learning in conjunction with a
neural network specially crafted for processing functional data. This paper
introduces a neural network explicitly tailored for handling multichannel and
partially observed functional data, complemented by developing a corresponding
loss function for training on imbalanced datasets. The results from a
real-world case study demonstrate the superior accuracy of our framework when
compared to existing benchmarks.

利用深度度量学习和专门设计用于处理多通道和部分观测功能数据的神经网络，我们提出了一种名为 MPOFI 的创新框架，用于在一个不平衡数据集中对缺陷样本进行分类。通过开发相应的损失函数，我们展示了该框架相对于现有基准的卓越准确性。