Accurate detection and diagnosis of abnormal behaviors such as network
attacks from multivariate time series (MTS) are crucial for ensuring the stable
and effective operation of industrial cyber-physical systems (CPS). However,
existing researches pay little attention to the logical dependencies among
system working states, and have difficulties in explaining the evolution
mechanisms of abnormal signals. To reveal the spatio-temporal association
relationships and evolution mechanisms of the working states of industrial CPS,
this paper proposes a fine-grained adaptive anomaly diagnosis method (i.e.
MAD-Transformer) to identify and diagnose anomalies in MTS. MAD-Transformer
first constructs a temporal state matrix to characterize and estimate the
change patterns of the system states in the temporal dimension. Then, to better
locate the anomalies, a spatial state matrix is also constructed to capture the
inter-sensor state correlation relationships within the system. Subsequently,
based on these two types of state matrices, a three-branch structure of
series-temporal-spatial attention module is designed to simultaneously capture
the series, temporal, and space dependencies among MTS. Afterwards, three
associated alignment loss functions and a reconstruction loss are constructed
to jointly optimize the model. Finally, anomalies are determined and diagnosed
by comparing the residual matrices with the original matrices. We conducted
comparative experiments on five publicly datasets spanning three application
domains (service monitoring, spatial and earth exploration, and water
treatment), along with a petroleum refining simulation dataset collected by
ourselves. The results demonstrate that MAD-Transformer can adaptively detect
fine-grained anomalies with short duration, and outperforms the
state-of-the-art baselines in terms of noise robustness and localization
performance.

本文提出了一种细粒度的自适应异常诊断方法（即 MAD-Transformer）来识别和诊断多元时间序列中的异常行为，并通过比较残差矩阵与原始矩阵来确定和诊断异常，实验证明 MAD-Transformer 可以适应性地检测短时异常，并在噪声鲁棒性和本地化性能方面优于现有的基线模型。

工业网络化系统细粒度自适应异常诊断的无监督时空状态估计

Unsupervised Spatio-Temporal State Estimation for Fine-grained Adaptive  Anomaly Diagnosis of Industrial Cyber-physical Systems

Industrial Cyber-Physical Systems (ICPS) integrate the disciplines of
computer science, communication technology, and engineering, and have emerged
as integral components of contemporary manufacturing and industries. However,
ICPS encounters various challenges in long-term operation, including equipment
failures, performance degradation, and security threats. To achieve efficient
maintenance and management, prognostics and health management (PHM) finds
widespread application in ICPS for critical tasks, including failure
prediction, health monitoring, and maintenance decision-making. The emergence
of large-scale foundation models (LFMs) like BERT and GPT signifies a
significant advancement in AI technology, and ChatGPT stands as a remarkable
accomplishment within this research paradigm, harboring potential for General
Artificial Intelligence. Considering the ongoing enhancement in data
acquisition technology and data processing capability, LFMs are anticipated to
assume a crucial role in the PHM domain of ICPS. However, at present, a
consensus is lacking regarding the application of LFMs to PHM in ICPS,
necessitating systematic reviews and roadmaps to elucidate future directions.
To bridge this gap, this paper elucidates the key components and recent
advances in the underlying model.A comprehensive examination and comprehension
of the latest advances in grand modeling for PHM in ICPS can offer valuable
references for decision makers and researchers in the industrial field while
facilitating further enhancements in the reliability, availability, and safety
of ICPS.

综述了工业领域中大规模建模在预测与健康管理 (HM) 方面的最新进展，为决策者与研究人员提供宝贵参考，同时促进工业领域中 ICPS 的可靠性、可用性和安全性的进一步提升。

工业网络物理系统中预测与健康管理基础模型综述

Survey on Foundation Models for Prognostics and Health Management in  Industrial Cyber-Physical Systems

Cybersecurity of Industrial Cyber-Physical Systems is drawing significant
concerns as data communication increasingly leverages wireless networks. A lot
of data-driven methods were develope for detecting cyberattacks, but few are
focused on distinguishing them from equipment faults. In this paper, we develop
a data-driven framework that can be used to detect, diagnose, and localize a
type of cyberattack called covert attacks on networked industrial control
systems. The framework has a hybrid design that combines a variational
autoencoder (VAE), a recurrent neural network (RNN), and a Deep Neural Network
(DNN). This data-driven framework considers the temporal behavior of a generic
physical system that extracts features from the time series of the sensor
measurements that can be used for detecting covert attacks, distinguishing them
from equipment faults, as well as localize the attack/fault. We evaluate the
performance of the proposed method through a realistic simulation study on a
networked power transmission system as a typical example of ICS. We compare the
performance of the proposed method with the traditional model-based method to
show its applicability and efficacy.

基于数据驱动的框架，结合变分自编码器 (VAE)、循环神经网络 (RNN) 和深度神经网络 (DNN)，用于检测、诊断和定位网络工业控制系统中的隐蔽攻击。通过在网络化的电力传输系统上进行仿真研究，我们评估了该方法的性能，并与传统基于模型的方法进行比较，以展示其适用性和效力。