Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review
The rapid development of photovoltaic (PV) technology and the growing number and size of
PV power plants require increasingly efficient and intelligent health monitoring strategies to …
PV power plants require increasingly efficient and intelligent health monitoring strategies to …
Neural networks: An overview of early research, current frameworks and new challenges
This paper presents a comprehensive overview of modelling, simulation and implementation
of neural networks, taking into account that two aims have emerged in this area: the …
of neural networks, taking into account that two aims have emerged in this area: the …
A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing
X Yan, M Jia - Neurocomputing, 2018 - Elsevier
Sensitive feature extraction from the raw vibration signal is still a great challenge for
intelligent fault diagnosis of rolling bearing. Current fault classification framework generally …
intelligent fault diagnosis of rolling bearing. Current fault classification framework generally …
[PDF][PDF] 基于数据驱动的微小故障诊断方法综述
文成林, 吕菲亚, 包哲静, 刘妹琴 - 自动化学报, 2016 - aas.net.cn
摘要能否及时诊断出微小故障是保障系统安全运行并抑制故障恶化的关键,
本文针对微小故障幅值低, 易被系统扰动和噪声掩盖等特点, 从数据驱动的角度对现有研究进行 …
本文针对微小故障幅值低, 易被系统扰动和噪声掩盖等特点, 从数据驱动的角度对现有研究进行 …
Intelligent fault diagnosis of rotating machinery using improved multiscale dispersion entropy and mRMR feature selection
X Yan, M Jia - Knowledge-Based Systems, 2019 - Elsevier
Intelligent fault diagnosis of rotating machinery is essentially a pattern recognition problem.
Meanwhile, effective feature extraction from the raw vibration signal is an important …
Meanwhile, effective feature extraction from the raw vibration signal is an important …
Feature selection and feature learning in machine learning applications for gas turbines: A review
The progress of machine learning (ML) in the past years has opened up new opportunities
to the field of gas turbine (GT) modelling. However, successful implementation of ML …
to the field of gas turbine (GT) modelling. However, successful implementation of ML …
A real-time fault diagnosis methodology of complex systems using object-oriented Bayesian networks
Bayesian network (BN) is a commonly used tool in probabilistic reasoning of uncertainty in
industrial processes, but it requires modeling of large and complex systems, in situations …
industrial processes, but it requires modeling of large and complex systems, in situations …
Ultrasonic signal classification and imaging system for composite materials via deep convolutional neural networks
M Meng, YJ Chua, E Wouterson, CPK Ong - Neurocomputing, 2017 - Elsevier
Automated ultrasonic signal classification systems are finding increasing use in many
applications for the recognition of large volumes of inspection signals. Wavelet transform is a …
applications for the recognition of large volumes of inspection signals. Wavelet transform is a …
Machine learning for durability and service-life assessment of reinforced concrete structures: Recent advances and future directions
WZ Taffese, E Sistonen - Automation in Construction, 2017 - Elsevier
Accurate service-life prediction of structures is vital for taking appropriate measures in a time-
and cost-effective manner. However, the conventional prediction models rely on simplified …
and cost-effective manner. However, the conventional prediction models rely on simplified …
Multiscale cascading deep belief network for fault identification of rotating machinery under various working conditions
X Yan, Y Liu, M Jia - Knowledge-Based Systems, 2020 - Elsevier
Deep learning is characterized by strong self-learning and fault classification ability without
manually feature extraction stage of traditional algorithms. Deep belief network (DBN) is one …
manually feature extraction stage of traditional algorithms. Deep belief network (DBN) is one …