One of the most appreciated features of Fault Trees (FTs) is their
simplicity, making them fit into industrial processes. As such processes evolve
in time, considering new aspects of large modern systems, modelling techniques
based on FTs have adapted to these needs. This paper proposes an extension of
FTs to take into account the problem of Predictive Maintenance, one of the
challenges of the modern dependability field of study. The paper sketches the
Predictive Fault Tree language and proposes some use cases to support their
modelling and analysis in concrete industrial settings.

该论文提出了一种扩展故障树的方法，以解决预测性维护问题，并提供了一些使用案例来支持在具体工业环境中对其进行建模和分析。

面向预测维护场景的故障树拓展

Towards an extension of Fault Trees in the Predictive Maintenance  Scenario

Previous research has demonstrated that various properties of infectious
diseases can be inferred from online search behaviour. In this work we use time
series of online search query frequencies to gain insights about the prevalence
of COVID-19 in multiple countries. We first develop unsupervised modelling
techniques based on associated symptom categories identified by the United
Kingdom's National Health Service and Public Health England. We then attempt to
minimise an expected bias in these signals caused by public interest -- as
opposed to infections -- using the proportion of news media coverage devoted to
COVID-19 as a proxy indicator. Our analysis indicates that models based on
online searches precede the reported confirmed cases and deaths by 16.7 (10.2 -
23.2) and 22.1 (17.4 - 26.9) days, respectively. We also investigate transfer
learning techniques for mapping supervised models from countries where the
spread of disease has progressed extensively to countries that are in earlier
phases of their respective epidemic curves. Furthermore, we compare time series
of online search activity against confirmed COVID-19 cases or deaths jointly
across multiple countries, uncovering interesting querying patterns, including
the finding that rarer symptoms are better predictors than common ones.
Finally, we show that web searches improve the short-term forecasting accuracy
of autoregressive models for COVID-19 deaths. Our work provides evidence that
online search data can be used to develop complementary public health
surveillance methods to help inform the COVID-19 response in conjunction with
more established approaches.

本研究使用在线搜索频率的时间序列来洞察多个国家的 COVID-19 盛行情况，并开发了基于联合王国国家卫生服务和公共卫生英格兰公共卫生实验室识别的相关症状类别的无监督建模技术。结果表明，基于在线搜索的模型比由报道的确诊病例和死亡事件组成的做出了更早的预测，并证明了在线搜索数据可以用来开发补充公共卫生监测方法，以帮助应对 COVID-19。