Fault Diagnosis and Detection Based on Deep Learning
A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).
Deadline for manuscript submissions: 31 March 2025 | Viewed by 45
Special Issue Editors
Interests: data mining; machine learning; industrial intelligence; big data
Interests: data mining; machine learning; IoT; cloud–edge computing
2. Institute of High Performance Computing and Networking, Italian National Research Council, Via P. Bucci, 7/11C, 87036 Rende, Italy
Interests: database; data mining; data warehousing; distributed computing; artificial intelligence
Special Issue Information
Dear Colleagues,
Safety and reliability have always been an important issue for modern sophisticated systems and technologies. Therefore, fault detection and diagnosis approaches are developed for ensuring quick and efficient awareness and improving the treatment of malfunctions within equipment or systems. Methods based on deep learning are playing an important role in the powerful representation ability, with the fast increase in the volume and dimension of big data and the development of cognitive computation. They serve as a great assistant for the rare domain experts and enhance more ordinary employees with the ability to find and diagnose faults and anomalies, reducing maintenance costs.
This Special Issue invites academics, professionals, and experts to exchange cutting-edge knowledge in the rapidly growing field. It comprehensively covers the most recent developments in the closely linked topics of fault diagnosis and detection based on deep learning.
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:
- Intelligent fault detection;
- Intelligent fault diagnosis;
- Anomaly detection (tabular, time series, images, etc.);
- Anomaly classification (tabular, time series, images, etc.);
- Intelligent maintenance;
- Predictive maintenance;
- Fault diagnosis based on multi-task learning;
- Fault knowledge graph;
- Knowledge and data-driven anomaly detection;
- Expert systems of anomaly detection based on deep learning;
- Federated learning for failure detection;
- Target recognition.
We look forward to receiving your contributions.
Dr. Pin Liu
Dr. Jianyong Zhu
Prof. Dr. Alfredo Cuzzocrea
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- machine learning
- deep learning
- fault detection
- anomaly detection
- anomaly diagnosis
- fault diagnosis
- intelligent maintenance
- anomaly classification
