Smart anomaly detection in sensor systems: A multi-perspective review
Anomaly detection is concerned with identifying data patterns that deviate remarkably from
the expected behavior. This is an important research problem, due to its broad set of …
the expected behavior. This is an important research problem, due to its broad set of …
Segmentation and feature extraction in medical imaging: a systematic review
CL Chowdhary, DP Acharjya - Procedia Computer Science, 2020 - Elsevier
Image processing techniques being crucial towards analyzing and resolving issues in
medical imaging since last two decades. Medical imaging is a process or technique to find …
medical imaging since last two decades. Medical imaging is a process or technique to find …
Multiscale symbolic fuzzy entropy: An entropy denoising method for weak feature extraction of rotating machinery
The entropy-based method has been demonstrated to be an effective approach to extract
the fault features by estimating the complexity of signals, but how to remove the strong …
the fault features by estimating the complexity of signals, but how to remove the strong …
A fault diagnosis scheme for planetary gearboxes using modified multi-scale symbolic dynamic entropy and mRMR feature selection
Y Li, Y Yang, G Li, M Xu, W Huang - Mechanical Systems and Signal …, 2017 - Elsevier
Health condition identification of planetary gearboxes is crucial to reduce the downtime and
maximize productivity. This paper aims to develop a novel fault diagnosis method based on …
maximize productivity. This paper aims to develop a novel fault diagnosis method based on …
EntropyHub: An open-source toolkit for entropic time series analysis
An increasing number of studies across many research fields from biomedical engineering
to finance are employing measures of entropy to quantify the regularity, variability or …
to finance are employing measures of entropy to quantify the regularity, variability or …
Early fault diagnosis of rolling bearings based on hierarchical symbol dynamic entropy and binary tree support vector machine
Early fault diagnosis of rolling bearings is crucial to operating and maintenance cost
reduction of the equipment with bearings. This paper aims to propose a novel early fault …
reduction of the equipment with bearings. This paper aims to propose a novel early fault …
Time series forecasting with multi-headed attention-based deep learning for residential energy consumption
Predicting residential energy consumption is tantamount to forecasting a multivariate time
series. A specific window for several sensor signals can induce various features extracted to …
series. A specific window for several sensor signals can induce various features extracted to …
Target detection and classification using seismic and PIR sensors
Unattended ground sensors (UGS) are widely used to monitor human activities, such as
pedestrian motion and detection of intruders in a secure region. Efficacy of UGS systems is …
pedestrian motion and detection of intruders in a secure region. Efficacy of UGS systems is …
Adaptive fault detection and diagnosis using an evolving fuzzy classifier
This paper suggests an approach for adaptive fault detection and diagnosis. The proposed
approach detects new operation modes of a process such as operation point changes and …
approach detects new operation modes of a process such as operation point changes and …
Multiscale symbolic diversity entropy: a novel measurement approach for time-series analysis and its application in fault diagnosis of planetary gearboxes
The health condition monitoring of planetary gearboxes has drawn increasing attention due
to the importance for safety operation and failure prevention. A novel diagnosis methodology …
to the importance for safety operation and failure prevention. A novel diagnosis methodology …