Matrix Profile based Anomaly Detection in Streaming Gait Data for Fall Prevention

Gerazov, Branislav; Hadjieva, Elena; Krivošei, Andrei; Sánchez, Fernando; Rostovski, Jakob; Kuusik, Alar; Alam, Mahtab

doi:10.1109/iwssip58668.2023.10180243

Cited by 1 publication

(3 citation statements)

References 21 publications

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“…The MP is a recently developed data-driven time series patterndiscovery method that records and annotates the location (index) and distance to the nearest neighbor of each subsequence of a time series [26]. This information can be used to identify time series motifs and anomalies, or time series discords, which are frequently used in time series data mining [27][28][29][30].…”

Section: Matrix Profile In Time Seriesmentioning

confidence: 99%

“…The versatility and scalability of the MP technique make it a valuable tool for a wide range of time series data-mining tasks [14,15,27,31,32]. Its applications span diverse domains, such as website user data analysis [33], medical diagnostics [34], music analysis [35,36], and critical business applications [37,38].…”

Section: Matrix Profile In Time Seriesmentioning

confidence: 99%

“…Time series discord detection aims to identify anomalies over time. The MP technique can detect unusual patterns in time series, enabling the identification of unexpected events or deviations from the norm in diverse applications such as sensor data analysis, network intrusion detection, and financial time series analysis [26,27].…”

Section: Matrix Profile For Time Series Anomaly Detectionmentioning

confidence: 99%

See 2 more Smart Citations

Anomaly Detection in Gas Turbines Using Outlet Energy Analysis with Cluster-Based Matrix Profile

Ghazvini,

Sànchez-Marrè,

Naderi

et al. 2024

Energies

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Gas turbines play a key role in generating power. It is really important that they work efficiently, safely, and reliably. However, their performance can be adversely affected by factors such as component wear, vibrations, and temperature fluctuations, often leading to abnormal patterns indicative of potential failures. As a result, anomaly detection has become an area of active research. Matrix Profile (MP) methods have emerged as a promising solution for identifying significant deviations in time series data from normal operational patterns. While most existing MP methods focus on vibration analysis of gas turbines, this paper introduces a novel approach using the outlet power signal. This modified approach, termed Cluster-based Matrix Profile (CMP) analysis, facilitates the identification of abnormal patterns and subsequent anomaly detection within the gas turbine engine system. Significantly, CMP analysis not only accelerates processing speed, but also provides user-friendly support information for operators. The experimental results on real-world gas turbines demonstrate the effectiveness of our approach in the early detection of anomalies and potential system failures.

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