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Anomaly Detection in Battery Devices: Identifying Unusual Battery Voltage Patterns

Lisra, Victor LU and Ritzing, Milton LU (2025) In Master's Thesis in Mathematical Sciences FMSM01 20251
Mathematical Statistics
Abstract
In modern security systems, the widespread deployment of battery-powered IoT devices creates a critical need for accurate battery replacement strategies. Premature replacement leads to unnecessary waste, while delayed replacement risks system failure in the case of an incident. This thesis presents the workflow building on limited knowledge of the data in an unsupervised learning setting to a working model for behavioural classification in battery voltage time series. Using a combination of manual feature engineering, active learning and machine learning—specifically, the XGBoost algorithm—battery behaviours were categorized and analysed. The results show that not all deviations from normal behaviour are anomalous or difficult to classify.... (More)
In modern security systems, the widespread deployment of battery-powered IoT devices creates a critical need for accurate battery replacement strategies. Premature replacement leads to unnecessary waste, while delayed replacement risks system failure in the case of an incident. This thesis presents the workflow building on limited knowledge of the data in an unsupervised learning setting to a working model for behavioural classification in battery voltage time series. Using a combination of manual feature engineering, active learning and machine learning—specifically, the XGBoost algorithm—battery behaviours were categorized and analysed. The results show that not all deviations from normal behaviour are anomalous or difficult to classify. Furthermore, weighting time series with different behaviours differently during prediction model training can improve performance of certain behaviours, although it is ultimately not a requirement for good forecasting. A general result is the useful quality-related insights into the IoT-devices, for example different behaviours' prominence and their difficulty in prediction. (Less)
Please use this url to cite or link to this publication:
author
Lisra, Victor LU and Ritzing, Milton LU
supervisor
organization
course
FMSM01 20251
year
type
H2 - Master's Degree (Two Years)
subject
keywords
XGBoost, anomaly detection, classification, time series, unsupervised learning, supervised learning, active learning, machine learning
publication/series
Master's Thesis in Mathematical Sciences
report number
LUNFMS-3536-2025
ISSN
1404-6342
other publication id
2025:E89
language
English
id
9206963
date added to LUP
2025-07-04 10:20:40
date last changed
2025-07-04 10:20:40
@misc{9206963,
  abstract     = {{In modern security systems, the widespread deployment of battery-powered IoT devices creates a critical need for accurate battery replacement strategies. Premature replacement leads to unnecessary waste, while delayed replacement risks system failure in the case of an incident. This thesis presents the workflow building on limited knowledge of the data in an unsupervised learning setting to a working model for behavioural classification in battery voltage time series. Using a combination of manual feature engineering, active learning and machine learning—specifically, the XGBoost algorithm—battery behaviours were categorized and analysed. The results show that not all deviations from normal behaviour are anomalous or difficult to classify. Furthermore, weighting time series with different behaviours differently during prediction model training can improve performance of certain behaviours, although it is ultimately not a requirement for good forecasting. A general result is the useful quality-related insights into the IoT-devices, for example different behaviours' prominence and their difficulty in prediction.}},
  author       = {{Lisra, Victor and Ritzing, Milton}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Thesis in Mathematical Sciences}},
  title        = {{Anomaly Detection in Battery Devices: Identifying Unusual Battery Voltage Patterns}},
  year         = {{2025}},
}