Health monitoring, fault analysis, and detection are critical for the safe
and sustainable operation of battery systems. We apply Gaussian process
resistance models on lithium iron phosphate battery field data to effectively
separate the time-dependent and operating point-dependent resistance. The data
set contains 29 battery systems returned to the manufacturer for warranty, each
with eight cells in series, totaling 232 cells and 131 million data rows. We
develop probabilistic fault detection rules using recursive spatiotemporal
Gaussian processes. These processes allow the quick processing of over a
million data points, enabling advanced online monitoring and furthering the
understanding of battery pack failure in the field. The analysis underlines
that often, only a single cell shows abnormal behavior or a knee point,
consistent with weakest-link failure for cells connected in series, amplified
by local resistive heating. The results further the understanding of how
batteries degrade and fail in the field and demonstrate the potential of
efficient online monitoring based on data. We open-source the code and publish
the large data set upon completion of the review of this article.

应用高斯过程电阻模型对锂铁磷酸盐电池的实测数据进行分析，实现了对时间依赖性和工作点依赖性电阻的有效分离，进而通过递归时空高斯过程开发了概率性故障检测规则，能够进行高效的在线监测，进一步深入了解电池组在实际应用中的失效过程。

基于高斯过程的锂离子电池系统健康监测与故障分析

Lithium-Ion Battery System Health Monitoring and Fault Analysis from  Field Data Using Gaussian Processes

In this work, we share our experience on tele-knowledge pre-training for
fault analysis, a crucial task in telecommunication applications that requires
a wide range of knowledge normally found in both machine log data and product
documents. To organize this knowledge from experts uniformly, we propose to
create a Tele-KG (tele-knowledge graph). Using this valuable data, we further
propose a tele-domain language pre-training model TeleBERT and its
knowledge-enhanced version, a tele-knowledge re-training model KTeleBERT. which
includes effective prompt hints, adaptive numerical data encoding, and two
knowledge injection paradigms. Concretely, our proposal includes two stages:
first, pre-training TeleBERT on 20 million tele-related corpora, and then
re-training it on 1 million causal and machine-related corpora to obtain
KTeleBERT. Our evaluation on multiple tasks related to fault analysis in
tele-applications, including root-cause analysis, event association prediction,
and fault chain tracing, shows that pre-training a language model with
tele-domain data is beneficial for downstream tasks. Moreover, the KTeleBERT
re-training further improves the performance of task models, highlighting the
effectiveness of incorporating diverse tele-knowledge into the model.

本文介绍了在电信应用的故障分析任务中使用电信知识图谱进行电信知识预训练的经验，同时介绍了 TeleBERT 语言预训练模型以及其增强版 KTeleBERT 的两种知识注入范式和适应性数字数据编码格式，最后在包括根本原因分析、事件关联预测和故障链追踪在内的多个与故障分析相关的任务上进行了评估，证明了预训练电信文本语言模型可以提高下游任务的性能。