We introduce a data-driven anomaly detection framework using a manufacturing
dataset collected from a factory assembly line. Given heterogeneous time series
data consisting of operation cycle signals and sensor signals, we aim at
discovering abnormal events. Motivated by our empirical findings that
conventional single-stage benchmark approaches may not exhibit satisfactory
performance under our challenging circumstances, we propose a two-stage deep
anomaly detection (TDAD) framework in which two different unsupervised learning
models are adopted depending on types of signals. In Stage I, we select anomaly
candidates by using a model trained by operation cycle signals; in Stage II, we
finally detect abnormal events out of the candidates by using another model,
which is suitable for taking advantage of temporal continuity, trained by
sensor signals. A distinguishable feature of our framework is that operation
cycle signals are exploited first to find likely anomalous points, whereas
sensor signals are leveraged to filter out unlikely anomalous points afterward.
Our experiments comprehensively demonstrate the superiority over single-stage
benchmark approaches, the model-agnostic property, and the robustness to
difficult situations.

介绍了基于制造数据集的异常检测框架，该框架利用异质时间序列数据，提出了一个两阶段的深度异常检测 (TDAD) 框架，该框架在操作周期信号和传感器信号类型上采用了两种不同的无监督学习模型，并结合了其它特征，通过实验综合展示了其对于单级对照方法的优越性、模型无关性和鲁棒性。

利用异构时间序列数据的两阶段深度异常检测

Two-Stage Deep Anomaly Detection with Heterogeneous Time Series Data

Cyber-resilience is an increasing concern in developing autonomous navigation
solutions for marine vessels. This paper scrutinizes cyber-resilience
properties of marine navigation through a prism with three edges: multiple
sensor information fusion, diagnosis of not-normal behaviours, and change
detection. It proposes a two-stage estimator for diagnosis and mitigation of
sensor signals used for coastal navigation. Developing a Likelihood Field
approach, a first stage extracts shoreline features from radar and matches them
to the electronic navigation chart. A second stage associates buoy and beacon
features from the radar with chart information. Using real data logged at sea
tests combined with simulated spoofing, the paper verifies the ability to
timely diagnose and isolate an attempt to compromise position measurements. A
new approach is suggested for high level processing of received data to
evaluate their consistency, that is agnostic to the underlying technology of
the individual sensory input. A combined parametric Gaussian modelling and
Kernel Density Estimation is suggested and compared with a generalized
likelihood ratio change detector that uses sliding windows. The paper shows how
deviations from nominal behaviour and isolation of the components is possible
when under attack or when defects in sensors occur.

该研究关注海上导航系统的网络韧性与多传感器信息融合、异常行为诊断、变化检测等相关问题，提出了一个两阶段的诊断和减轻传感器信号模拟攻击的方法，并建议了一个新的高级数据处理方法、能够诊断和孤立组件异常行为的能力，以提高海上导航的网络韧性。

信息融合与变化检测的海上导航网络安全韧性

Cyber-resilience for marine navigation by information fusion and change detection

Predicting the health of components in complex dynamic systems such as an
automobile poses numerous challenges. The primary aim of such predictive
systems is to use the high-dimensional data acquired from different sensors and
predict the state-of-health of a particular component, e.g., brake pad. The
classical approach involves selecting a smaller set of relevant sensor signals
using feature selection and using them to train a machine learning algorithm.
However, this fails to address two prominent problems: (1) sensors are
susceptible to failure when exposed to extreme conditions over a long periods
of time; (2) sensors are electrical devices that can be affected by noise or
electrical interference. Using the failed and noisy sensor signals as inputs
largely reduce the prediction accuracy. To tackle this problem, it is
advantageous to use the information from all sensor signals, so that the
failure of one sensor can be compensated by another. In this work, we propose
an Artificial Neural Network (ANN) based framework to exploit the information
from a large number of signals. Secondly, our framework introduces a data
augmentation approach to perform accurate predictions in spite of noisy
signals. The plausibility of our framework is validated on real life industrial
application from Robert Bosch GmbH.

本研究提出了基于人工神经网络的框架，能够有效利用大量传感器信号信息来准确预测汽车零部件的健康状态，并引入数据增强方法以消除噪声的影响。该框架已在 Robert Bosch GmbH 的实际应用中得到验证。