Comprehensive analysis of change-point dynamics detection in time series data: A review
In the ever-evolving field of time series analysis, detecting changes in patterns and
dynamics is paramount for accurate forecasting and meaningful insights. This article …
dynamics is paramount for accurate forecasting and meaningful insights. This article …
Unsupervised anomaly detection in time-series: An extensive evaluation and analysis of state-of-the-art methods
Unsupervised anomaly detection in time-series has been extensively investigated in the
literature. Notwithstanding the relevance of this topic in numerous application fields, a …
literature. Notwithstanding the relevance of this topic in numerous application fields, a …
[HTML][HTML] Multivariate time series anomaly detection with adversarial transformer architecture in the Internet of Things
F Zeng, M Chen, C Qian, Y Wang, Y Zhou… - Future Generation …, 2023 - Elsevier
Many real-world Internet of Things (IoT) systems contain various sensor devices. Operating
the devices generates a large amount of multivariate time series data, which reflects the …
the devices generates a large amount of multivariate time series data, which reflects the …
Predictive maintenance on the machining process and machine tool
This paper presents the process required to implement a data driven Predictive
Maintenance (PdM) not only in the machine decision making, but also in data acquisition …
Maintenance (PdM) not only in the machine decision making, but also in data acquisition …
Real-time change-point detection: A deep neural network-based adaptive approach for detecting changes in multivariate time series data
The behavior of a time series may be affected by various factors. Changes in mean,
variance, frequency, and auto-correlation are the most common. Change-Point Detection …
variance, frequency, and auto-correlation are the most common. Change-Point Detection …
An encoder-decoder based approach for anomaly detection with application in additive manufacturing
We present a novel unsupervised deep learning approach that utilizes an encoder-decoder
architecture for detecting anomalies in sequential sensor data collected during industrial …
architecture for detecting anomalies in sequential sensor data collected during industrial …
[HTML][HTML] An applicable predictive maintenance framework for the absence of run-to-failure data
D Kim, S Lee, D Kim - Applied Sciences, 2021 - mdpi.com
As technology advances, the equipment becomes more complicated, and the importance of
the Prognostics and Health Management (PHM) to monitor the condition of the equipment …
the Prognostics and Health Management (PHM) to monitor the condition of the equipment …
[HTML][HTML] Change point enhanced anomaly detection for IoT time series data
Due to the exponential growth of the Internet of Things networks and the massive amount of
time series data collected from these networks, it is essential to apply efficient methods for …
time series data collected from these networks, it is essential to apply efficient methods for …
Machine learning-based gait anomaly detection using a sensorized tip: an individualized approach
J Otamendi, A Zubizarreta, E Portillo - Neural computing and applications, 2023 - Springer
Lower limb motor impairment affects greatly the autonomy and quality of life of those people
suffering from it. Recent studies have shown that an appropriate rehabilitation can …
suffering from it. Recent studies have shown that an appropriate rehabilitation can …
Weibull recurrent neural networks for failure prognosis using histogram data
Weibull time-to-event recurrent neural networks (WTTE-RNN) is a simple and versatile
prognosis algorithm that works by optimising a Weibull survival function using a recurrent …
prognosis algorithm that works by optimising a Weibull survival function using a recurrent …