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- research-articleMarch 2024
Semi-supervised diagnosis of wind-turbine gearbox misalignment and imbalance faults
Applied Intelligence (KLU-APIN), Volume 54, Issue 6Mar 2024, Pages 4525–4544https://doi.org/10.1007/s10489-024-05373-6AbstractBoth wear-induced bearing failure and misalignment of the powertrain between the rotor and the electrical generator are common failure modes in wind-turbine motors. In this study, Semi-Supervised Learning (SSL) is applied to a fault detection and ...
- research-articleFebruary 2024
Research on decision-level fusion method based on structural causal model in system-level fault detection and diagnosis
Engineering Applications of Artificial Intelligence (EAAI), Volume 126, Issue PDNov 2023https://doi.org/10.1016/j.engappai.2023.107095AbstractAt present, system-level fault detection and diagnosis (FDD) research often uses correlation-based machine learning methods combined with multiple heterogeneous diagnosis methods to improve the fault detection rate (FDR), that is, decision-level ...
- research-articleFebruary 2024
Robust and sparse canonical correlation analysis for fault detection and diagnosis using training data with outliers
Expert Systems with Applications: An International Journal (EXWA), Volume 236, Issue CFeb 2024https://doi.org/10.1016/j.eswa.2023.121434AbstractA well-known shortcoming of the traditional canonical correlation analysis (CCA) is the lack of robustness against outliers. This shortcoming hinders the application of CCA in the case where the training data contain outliers. To overcome this ...
- ArticleDecember 2023
Virtual Sensor-Based Fault Detection and Diagnosis Framework for District Heating Systems: A Top-Down Approach for Quick Fault Localisation
- Theis Bank,
- Frederik Wagner Madsen,
- Lasse Kappel Mortensen,
- Henrik Alexander Nissen Søndergaard,
- Hamid Reza Shaker
AbstractFor district heating systems (DHS) to operate cost-effectively, avoid disturbances of loads, and increase overall energy efficiency, faults in DHSs must be detected, located, and rectified quickly. For this purpose, a novel digital twin-based ...
- ArticleSeptember 2023
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- research-articleAugust 2023
Two-view LSTM variational auto-encoder for fault detection and diagnosis in multivariable manufacturing processes
Neural Computing and Applications (NCAA), Volume 35, Issue 29Pages 22007–22026https://doi.org/10.1007/s00521-023-08949-4AbstractProcess monitoring of industrial production has always been one of the main concerns of process industry systems. As artificial intelligence booms, fault detection and diagnosis via deep learning has been widely used in industrial process ...
- research-articleAugust 2023
A novel building heat pump system semi-supervised fault detection and diagnosis method under small and imbalanced data
Engineering Applications of Artificial Intelligence (EAAI), Volume 123, Issue PBAug 2023https://doi.org/10.1016/j.engappai.2023.106316AbstractFaulty heat pump system operating can cause excessive energy consumption and an uncomfortable indoor environment. Data-driven method is one of the most widely studied Fault Detection and Diagnosis (FDD) methods in building heat pump systems. ...
Highlights- A novel integrated end-to-end semi-supervised model was proposed.
- The method was verified on a Variable Refrigerant Flow Heat Pump system.
- The comparison between proposed and existed methods were discussed comprehensively.
- The ...
- research-articleNovember 2022
A multigroup fault detection and diagnosis framework for large-scale industrial systems using nonlinear multivariate analysis
Expert Systems with Applications: An International Journal (EXWA), Volume 206, Issue CNov 2022https://doi.org/10.1016/j.eswa.2022.117859Highlights- A multigroup fault detection and diagnosis (FDD) framework for industrial systems.
In a large-scale industrial system with numerous variables, the relations among variables are often nonlinear and very complicated, due to material, energy and information flows throughout the entire system. In such systems, fault ...
- research-articleJune 2022
Fault detection and diagnosis with a novel source-aware autoencoder and deep residual neural network
Neurocomputing (NEUROC), Volume 488, Issue CJun 2022, Pages 618–633https://doi.org/10.1016/j.neucom.2021.11.067AbstractThe capability of deep learning (DL) techniques for dealing with non-linear, dynamic and correlated data has paved the way for DL-based fault detection and diagnosis (FDD). Among them, autoencoders (AEs) have shown their potential to ...
- research-articleApril 2022
A survey of intelligent transmission line inspection based on unmanned aerial vehicle
Artificial Intelligence Review (ARTR), Volume 56, Issue 1Jan 2023, Pages 173–201https://doi.org/10.1007/s10462-022-10189-2AbstractWith the development of the new generation of information technology, artificial intelligence, cloud computing and big data are gradually becoming powerful engines of the smart grid. In recent years, people have been exploring how to reduce the ...
- research-articleApril 2022
False alarm moderation for performance monitoring in industrial water distribution systems
Advanced Engineering Informatics (ADEI), Volume 52, Issue CApr 2022https://doi.org/10.1016/j.aei.2022.101592AbstractWhile considerable attention has been given to data driven methods that analyse and control energy systems in buildings, the same cannot be said for building water systems. As a result, approaches which support enhanced efficiency in ...
- research-articleMarch 2022
Sparse one-dimensional convolutional neural network-based feature learning for fault detection and diagnosis in multivariable manufacturing processes
Neural Computing and Applications (NCAA), Volume 34, Issue 6Mar 2022, Pages 4343–4366https://doi.org/10.1007/s00521-021-06575-6AbstractThose fault detection and diagnosis (FDD) models can identify various faulty signals in industrial processes by extracting features from process data with high nonlinearity and correlations. However, the diagnostic performance of those models ...
- review-articleJanuary 2022
Computational intelligence for preventive maintenance of power transformers
Applied Soft Computing (APSC), Volume 114, Issue CJan 2022https://doi.org/10.1016/j.asoc.2021.108129AbstractPower transformers are an indispensable equipment in power transmission and distribution systems, and failures or hidden defects in power transformers can cause operational and downtime issues in power supply, resulting in economic and ...
Highlights- We aim to provide a knowledge base that covers more than DGA to inform future research.
- research-articleJanuary 2022
Adaptive residual CNN-based fault detection and diagnosis system of small modular reactors
Applied Soft Computing (APSC), Volume 114, Issue CJan 2022https://doi.org/10.1016/j.asoc.2021.108064AbstractWith the development of Industry 4.0 technology, it is a popular trend to reduce maintenance costs and ensure the safety of novel nuclear systems combined with deep learning (DL) technology. In this paper, an intelligent fault ...
Highlights- A novel approach to diagnosis faults in small modular reactors is presented.
- ...
- research-articleMarch 2021
Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing
Journal of Intelligent Manufacturing (SPJIM), Volume 32, Issue 3Mar 2021, Pages 823–836https://doi.org/10.1007/s10845-020-01591-0AbstractThe development of information technology and process technology have been enhanced the rapid changes in high-tech products and smart manufacturing, specifications become more sophisticated. Large amount of sensors are installed to record ...
- research-articleJanuary 2021
Health Monitoring System for Autonomous Vehicles using Dynamic Bayesian Networks for Diagnosis and Prognosis
Journal of Intelligent and Robotic Systems (JIRS), Volume 101, Issue 1Jan 2021https://doi.org/10.1007/s10846-020-01293-yAbstractAutonomous Vehicles have the potential to change the urban transport scenario. However, to be able to safely navigate autonomously they need to deal with faults that its components are subject to. Therefore, Health Monitoring System is a essential ...
- research-articleOctober 2019
Fault correction of algorithm implementation for intelligentized robotic multipass welding process based on finite state machines
Robotics and Computer-Integrated Manufacturing (RCIM), Volume 59, Issue COct 2019, Pages 28–35https://doi.org/10.1016/j.rcim.2019.03.002Highlights- Modeling the typical welding process via finite state machines for process monitoring.
The intelligentized robotic multipass welding process (IRMWP) involves adjustments of welding parameters, posture adjustments of the welding torch, real-time decision making of the tracking point, etc. It constructs a typical mixed ...
- research-articleApril 2019
Automated bearing fault diagnosis scheme using 2D representation of wavelet packet transform and deep convolutional neural network
Computers in Industry (CIIN), Volume 106, Issue CApr 2019, Pages 142–153https://doi.org/10.1016/j.compind.2019.01.008Highlights- This paper proposes an adaptive deep convolutional neural network (ADCNN).
- ...
Bearings are one of the most crucial components in many industrial machines. Effective bearing fault diagnosis is essential for normal and safe machine operation. Existing fault diagnosis methods are mostly limited to manual feature ...
- research-articleMarch 2019
Sensor fault estimation using LPV sliding mode observers with erroneous scheduling parameters
Automatica (Journal of IFAC) (AJIF), Volume 101, Issue CMar 2019, Pages 66–77https://doi.org/10.1016/j.automatica.2018.10.055AbstractThis paper proposes a linear parameter-varying sliding mode observer for the purpose of simultaneously estimating the system states and reconstructing sensor faults. Furthermore, some of the measured scheduling parameters are also ...
- research-articleJanuary 2019
Collaborative data analytics for smart buildings: opportunities and models
Cluster Computing (KLU-CLUS), Volume 22, Issue 1Jan 2019, Pages 1065–1077https://doi.org/10.1007/s10586-017-1362-xAbstractSmart buildings equipped with state-of-the-art sensors and meters are becoming more common. Large quantities of data are being collected by these devices. For a single building to benefit from its own collected data, it will need to wait for a ...