Artificial Intelligence for Fault Detection and Diagnosis
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".
Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 29412
Special Issue Editors
Interests: artificial intelligence; machine learning; computer vision
Interests: artificial intelligence; image processing
Interests: artificial intelligence; machine learning; data mining; computer visions and their applications
Special Issue Information
Dear Colleagues,
Fault detection and diagnosis (FDD) is very important in manufacturing and mechatronic systems to reduce costs and improve productivity. Traditionally, human beings have manually checked the states of the machines and detected their faults, which is time-consuming and expensive. Therefore, it is desirable to develop intelligent systems to achieve automatic FDD. Typically, an intelligent FDD system includes many processes, such as data collection, data processing, feature extraction, feature selection, feature construction, and classification, where different algorithms can be used.
Artificial intelligence (AI) covers a wide range of algorithms that mimic the human mind, thinking and acting like humans to solve important tasks in different fields. AI methods include deep learning, machine learning, rule-based methods, evolutionary computation, and more. AI has achieved great success in many important areas including computer vision and natural language processing.
Many AI algorithms have been applied to FDD, including data processing, data mining, feature analysis, and classification. In recent years, deep neural networks have shown potential in FDD. Other promising methods include evolutionary computation techniques and fuzzy systems. However, the potential of AI has not been comprehensively investigated in FDD. It remains a challenging task due to many factors, such as changeable equipment working state, incomplete information, lack of sufficient training data, complex relationships between faults and symptoms, and the requirement of domain knowledge.
This Special Issue aims to investigate the use of different AI algorithms involving machine learning, deep learning, and computational intelligence techniques in applications to FDD of different machines. We would like to invite researchers to submit papers on the topic, from all viewpoints, including theoretical issues, algorithms, systems, and industrial applications.
Possible research themes include:
- AI-based fault detection and diagnosis methods using vibration signals, electric signals, acoustic signals, thermal images, etc.
- AI-based fault detection and diagnosis methods based on improved data using spectral analysis, wavelet transform (WT), empirical mode decomposition (EMD), variational mode decomposition (VMD), maximum correlated kurtosis deconvolution (MCKD), fast kurtogram (FSK), e
- AI-based fault detection and diagnosis methods using various feature analysis techniques, including feature scaling, feature normalization, feature selection, feature extraction, feature construction, and feature learning.
- Machine learning for fault detection and diagnosis, such as support vector machines (SVMs), k-nearest neighbor (KNN), Bayesian classifier, ensemble methods, etc.
- Evolutionary computation (EC) techniques for fault detection and diagnosis, such as genetic programming (GP), particle swarm optimization (PSO), differential evolution (DE), genetic algorithms (GAs), etc.
- Deep learning for fault detection and diagnosis, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), belief networks, reinforcement learning, etc.
- Fault detection and diagnosis of various machines including rotating machines, electricity-driven machines, and different types of engines.
Dr. Ying Bi
Prof. Dr. Mengjie Zhang
Prof. Dr. Bing Xue
Dr. Bo Peng
Guest Editors
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