Recent advances in sensor fault diagnosis: A review
As an essential component of data acquisition systems, sensors have been widely used,
especially in industrial and agricultural sectors. However, sensors are also prone to faults …
especially in industrial and agricultural sectors. However, sensors are also prone to faults …
Aquatic toxic analysis by monitoring fish behavior using computer vision: A recent progress
Video tracking based biological early warning system achieved a great progress with
advanced computer vision and machine learning methods. Ability of video tracking of …
advanced computer vision and machine learning methods. Ability of video tracking of …
Data-driven fault detection for dynamic systems with performance degradation: A unified transfer learning framework
Continuous operations can result in performance degradation of industrial systems, which
naturally increases complexity in fault detection (FD). In this study, a transfer learning …
naturally increases complexity in fault detection (FD). In this study, a transfer learning …
[HTML][HTML] The value of human data annotation for machine learning based anomaly detection in environmental systems
Anomaly detection is the process of identifying unexpected data samples in datasets.
Automated anomaly detection is either performed using supervised machine learning …
Automated anomaly detection is either performed using supervised machine learning …
Monitoring influent conditions of wastewater treatment plants by nonlinear data-based techniques
To operate wastewater treatment plants (WWTPs) with optimized efficiency, influent
conditions (ICs) as initial states of inflow fed to WWTPs were monitored to identify potential …
conditions (ICs) as initial states of inflow fed to WWTPs were monitored to identify potential …
Self-organizing multichannel deep learning system for river turbidity monitoring
K Gu, J Liu, S Shi, S Xie, T Shi… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
This article focuses on proposing a new framework for self-organizing multichannel deep
learning system (SMDLS) to solve the problem of river turbidity monitoring, which is one of …
learning system (SMDLS) to solve the problem of river turbidity monitoring, which is one of …
[HTML][HTML] A fusion feature extraction method using EEMD and correlation coefficient analysis for bearing fault diagnosis
Acceleration sensors are frequently applied to collect vibration signals for bearing fault
diagnosis. To fully use these vibration signals of multi-sensors, this paper proposes a new …
diagnosis. To fully use these vibration signals of multi-sensors, this paper proposes a new …
[HTML][HTML] Prediction of dissolved gas concentrations in transformer oil based on the KPCA-FFOA-GRNN model
J Lin, G Sheng, Y Yan, J Dai, X Jiang - Energies, 2018 - mdpi.com
The purpose of analyzing the dissolved gas in transformer oil is to determine the
transformer's operating status and is an important basis for fault diagnosis. Accurate …
transformer's operating status and is an important basis for fault diagnosis. Accurate …
Status self-validation of sensor arrays using gray forecasting model and bootstrap method
Y Chen, J Yang, Y Xu, S Jiang, X Liu… - IEEE Transactions on …, 2016 - ieeexplore.ieee.org
The reliability monitoring of sensor arrays is a challenging and critical issue that directly
influences the performance of a measurement and control system. In this paper, a novel …
influences the performance of a measurement and control system. In this paper, a novel …
Application of kernel principal component analysis for optical vector atomic magnetometry
JA McKelvy, I Novikova, EE Mikhailov… - Machine Learning …, 2023 - iopscience.iop.org
Vector atomic magnetometers that incorporate electromagnetically induced transparency
(EIT) allow for precision measurements of magnetic fields that are sensitive to the …
(EIT) allow for precision measurements of magnetic fields that are sensitive to the …