Anomaly Detection for Equipment Degradation Identification and Remaining Useful Life Prediction

Dear Colleagues,

Electronics is pleased to announce a Special Issue dedicated to exploring the latest advancements, theories, and applications of anomaly detection. As data ecosystems grow in scale and complexity, anomaly detection serves as a critical gatekeeper across numerous domains. We invite researchers and experts from diverse disciplines to contribute their original research articles, reviews, and case studies to this Special Issue, shedding light on the paradigm shift from shallow rule-matching to deep semantic understanding.

Objective:

This special issue aims to showcase innovative research that harnesses the potential of advanced computational models to enhance the accuracy, intelligence, and robustness of anomaly detection. We invite submissions that highlight the transformative role of emerging technologies such as deep learning, generative AI, and graph neural networks in addressing the challenges posed by high-dimensional, nonlinear, and multimodal data. This special issue will serve as a platform for exploring novel feature representations, few-shot learning paradigms, and interdisciplinary approaches, fostering collaboration among researchers, practitioners, and professionals in the fields of data mining, artificial intelligence, and system security.

Topics of interest include, but are not limited to, the following:

1. Anomaly detection in graphical and image data (e.g., industrial visual inspection, medical lesion recognition, video temporal monitoring, UAV visual detection)
2. 2. Anomaly detection in complex data architectures (e.g., graph-structured data, multi-view and multimodal data, time-series and streaming data)
3. Advanced mechanisms for anomaly feature learning (e.g., self-supervised feature reconstruction, diffusion model-based anomaly feature enhancement)
4. Anomaly detection for software and system security (e.g., anomaly-behavior-aware fuzzing strategies, crash detection and classification in intelligent fuzzing, anomaly detection for API robustness validation)
5. Few-shot and zero-shot anomaly detection
6. Interpretable anomaly analysis and explainable AI for anomaly detection
7. Anomaly Detection for Equipment Degradation Identification and Remaining Useful Life Prediction
8. Innovative applications in emerging scenarios (e.g., autonomous driving safety, large language model security, IoT sensor networks); Case studies showcasing successful real-world deployments and best practices.

Dr. Mingsheng Cao
Dr. Feng Wang
Dr. Jiefan Qiu
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly 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 2400 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.