The growing reliance on computer systems, particularly personal computers
(PCs), necessitates heightened reliability to uphold user satisfaction. This
research paper presents an in-depth analysis of extensive system telemetry
data, proposing an ensemble methodology for detecting system failures. Our
approach entails scrutinizing various parameters of system metrics,
encompassing CPU utilization, memory utilization, disk activity, CPU
temperature, and pertinent system metadata such as system age, usage patterns,
core count, and processor type. The proposed ensemble technique integrates a
diverse set of algorithms, including Long Short-Term Memory (LSTM) networks,
isolation forests, one-class support vector machines (OCSVM), and local outlier
factors (LOF), to effectively discern system failures. Specifically, the LSTM
network with other machine learning techniques is trained on Intel Computing
Improvement Program (ICIP) telemetry software data to distinguish between
normal and failed system patterns. Experimental evaluations demonstrate the
remarkable efficacy of our models, achieving a notable detection rate in
identifying system failures. Our research contributes to advancing the field of
system reliability and offers practical insights for enhancing user experience
in computing environments.

通过对系统遥测数据的深入分析，该研究论文提出了一种集成方法来检测系统故障，并结合了长短期记忆网络、隔离森林、单类支持向量机和局部异常因子等多种机器学习技术，有效识别系统故障，从而提升了计算环境中用户体验和系统可靠性。

使用大规模遥测数据的系统故障检测集成方法

Ensemble Method for System Failure Detection Using Large-Scale Telemetry  Data

Non-neural Machine Learning (ML) and Deep Learning (DL) models are often used
to predict system failures in the context of industrial maintenance. However,
only a few researches jointly assess the effect of varying the amount of past
data used to make a prediction and the extension in the future of the forecast.
This study evaluates the impact of the size of the reading window and of the
prediction window on the performances of models trained to forecast failures in
three data sets concerning the operation of (1) an industrial wrapping machine
working in discrete sessions, (2) an industrial blood refrigerator working
continuously, and (3) a nitrogen generator working continuously. The problem is
formulated as a binary classification task that assigns the positive label to
the prediction window based on the probability of a failure to occur in such an
interval. Six algorithms (logistic regression, random forest, support vector
machine, LSTM, ConvLSTM, and Transformers) are compared using multivariate
telemetry time series. The results indicate that, in the considered scenarios,
the dimension of the prediction windows plays a crucial role and highlight the
effectiveness of DL approaches at classifying data with diverse time-dependent
patterns preceding a failure and the effectiveness of ML approaches at
classifying similar and repetitive patterns preceding a failure.

通过评估不同的历史数据量和预测未来时间范围，以预测系统故障的性能表现，研究发现预测窗口的大小对性能起关键作用，并突显深度学习在分类具有不同时间依赖模式的数据方面的有效性，以及机器学习在分类相似和重复模式的数据方面的有效性。

基于多变量时间序列的机器故障预测：一个工业案例研究

Predicting machine failures from multivariate time series: an industrial  case study

In the modern world, we are permanently using, leveraging, interacting with,
and relying upon systems of ever higher sophistication, ranging from our cars,
recommender systems in e-commerce, and networks when we go online, to
integrated circuits when using our PCs and smartphones, the power grid to
ensure our energy supply, security-critical software when accessing our bank
accounts, and spreadsheets for financial planning and decision making. The
complexity of these systems coupled with our high dependency on them implies
both a non-negligible likelihood of system failures, and a high potential that
such failures have significant negative effects on our everyday life. For that
reason, it is a vital requirement to keep the harm of emerging failures to a
minimum, which means minimizing the system downtime as well as the cost of
system repair. This is where model-based diagnosis comes into play.
Model-based diagnosis is a principled, domain-independent approach that can
be generally applied to troubleshoot systems of a wide variety of types,
including all the ones mentioned above, and many more. It exploits and
orchestrates i.a. techniques for knowledge representation, automated reasoning,
heuristic problem solving, intelligent search, optimization, stochastics,
statistics, decision making under uncertainty, machine learning, as well as
calculus, combinatorics and set theory to detect, localize, and fix faults in
abnormally behaving systems.
In this thesis, we will give an introduction to the topic of model-based
diagnosis, point out the major challenges in the field, and discuss a selection
of approaches from our research addressing these issues.

本论文介绍了基于模型的诊断，探讨了该领域的主要挑战，并讨论了一些应对这些问题的研究方法。

高效人工智能方法用于（交互式）调试：不要只治疗症状，查找根本原因！

Don't Treat the Symptom, Find the Cause! Efficient  Artificial-Intelligence Methods for (Interactive) Debugging

As machine learning systems move from computer-science laboratories into the
open world, their accountability becomes a high priority problem.
Accountability requires deep understanding of system behavior and its failures.
Current evaluation methods such as single-score error metrics and confusion
matrices provide aggregate views of system performance that hide important
shortcomings. Understanding details about failures is important for identifying
pathways for refinement, communicating the reliability of systems in different
settings, and for specifying appropriate human oversight and engagement.
Characterization of failures and shortcomings is particularly complex for
systems composed of multiple machine learned components. For such systems,
existing evaluation methods have limited expressiveness in describing and
explaining the relationship among input content, the internal states of system
components, and final output quality. We present Pandora, a set of hybrid
human-machine methods and tools for describing and explaining system failures.
Pandora leverages both human and system-generated observations to summarize
conditions of system malfunction with respect to the input content and system
architecture. We share results of a case study with a machine learning pipeline
for image captioning that show how detailed performance views can be beneficial
for analysis and debugging.

机器学习系统 accountability 评估方法 fail 特点系统 Pandora 优势。