Power system reliability and maintenance evolution: A critical review and future perspectives
In the last two decades, the number of strategies for planning the maintenance of power
systems have increased considerably. As societal dependence on power system …
systems have increased considerably. As societal dependence on power system …
A dynamic-Bayesian-network-based fault diagnosis methodology considering transient and intermittent faults
Transient fault (TF) and intermittent fault (IF) of complex electronic systems are difficult to
diagnose. As the performance of electronic products degrades over time, the results of fault …
diagnose. As the performance of electronic products degrades over time, the results of fault …
HMM-Based Filtering for Discrete-Time Markov Jump LPV Systems Over Unreliable Communication Channels
Y Zhu, Z Zhong, WX Zheng… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
In this paper, the filtering problem is investigated for a class of discrete-time Markov jump
linear parameter varying systems with packet dropouts and channel noises in the network …
linear parameter varying systems with packet dropouts and channel noises in the network …
Adaptive transfer learning for multimode process monitoring and unsupervised anomaly detection in steam turbines
Through condition-based maintenance strategy, engineers can monitor the health states of
equipment and take actions based on the sensor data. Limited by the low failure frequency …
equipment and take actions based on the sensor data. Limited by the low failure frequency …
A composite anomaly detection system for data-driven power plant condition monitoring
Data-driven condition monitoring is an essential function for power plant because of its
potential to enhance asset longevity and reduce the operation and maintenance costs. This …
potential to enhance asset longevity and reduce the operation and maintenance costs. This …
Online drift compensation framework based on active learning for gas classification and concentration prediction
Sensor drift is an urgent issue in the machine olfaction community. To date, most studies
have focused on gas classification tasks based on an offline method, while neglecting …
have focused on gas classification tasks based on an offline method, while neglecting …
A dynamic Bayesian network based methodology for fault diagnosis of subsea Christmas tree
P Liu, Y Liu, B Cai, X Wu, K Wang, X Wei, C Xin - Applied Ocean Research, 2020 - Elsevier
A subsea Christmas tree (XT) is an extremely important part of a subsea production system.
The safety-fault of subsea XT indicates that no major safety incidents are difficult to …
The safety-fault of subsea XT indicates that no major safety incidents are difficult to …
POP-CNN: Predicting odor pleasantness with convolutional neural network
Predicting odor's pleasantness with electronic nose can simplify the evaluation process of
odors, and it has potential applications in the perfumes and environmental monitoring …
odors, and it has potential applications in the perfumes and environmental monitoring …
Local discriminant subspace learning for gas sensor drift problem
Sensor drift is one of the severe issues that gas sensors suffer from. To alleviate the sensor
drift problem, a gas sensor drift compensation approach is proposed based on local …
drift problem, a gas sensor drift compensation approach is proposed based on local …
Neighborhood preserving and weighted subspace learning method for drift compensation in gas sensor
This article presents a novel discriminative subspace-learning-based unsupervised domain
adaptation (DA) method for the gas sensor drift problem. Many existing subspace learning …
adaptation (DA) method for the gas sensor drift problem. Many existing subspace learning …