Intelligent Sensors and Signal Processing in Industry
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Industrial Sensors".
Deadline for manuscript submissions: 31 December 2025 | Viewed by 1483
Special Issue Editors
Interests: magnetic flux leakage testing; electromagnetic ultrasonic guided wave testing; defect inversion imaging; signal processing; intelligent sensors
Special Issues, Collections and Topics in MDPI journals
Interests: non-destructive evaluation; ultrasonics; structural health monitoring; guided waves; measurements and instrumentation; FE modeling; microcontrollers; composite structures
Special Issues, Collections and Topics in MDPI journals
Interests: ultrasonic non-destructive testing; rail transit; intelligent sensing
Special Issue Information
Dear Colleagues,
The integration of intelligent sensors and advanced signal processing techniques in industry is revolutionizing traditional manufacturing and operational processes, ushering in a new era of efficiency, precision, and automation. Intelligent sensors, equipped with capabilities such as self-diagnostics, data processing, and communication, are pivotal in transforming raw data into actionable insights. These sensors are employed across a wide range of industrial applications, from monitoring machinery health and predicting failures to optimizing energy consumption and ensuring product quality. Complementing intelligent sensors is advanced signal processing technology. Through real-time data analysis, noise reduction, pattern recognition, and modern artificial intelligence techniques, these technologies further enhance the capabilities of intelligent sensors, enabling more accurate and reliable decision making. It is evident that intelligent sensors and signal processing technology hold significant importance in industries. Their integration brings about more efficient, precise, and automated production methods, driving industrial development and progress. By delving deeper into the exploration and application of intelligent sensors and signal processing technology, we can further enhance industrial competitiveness and achieve sustainable development.
This Special Issue aims to explore the cutting-edge advancements and applications of intelligent sensors and signal processing in various industrial contexts.
In this Special Issue, we look forward to receiving papers on a wide range of research topics, including the following:
- New materials, technologies, and designs for intelligent sensors.
- Application of intelligent sensors in NDT, SHM, and fault warning.
- Various NDT technologies in electric energy, petroleum, transportation, construction, chemical industry, and special equipment.
- Development and deployment of intelligent sensors in industrial environments.
- Signal processing technologies in industrial automation and intelligent manufacturing.
- Machine learning and AI techniques for predictive maintenance and anomaly detection.
- Sensor fusion and integration in industrial IoT (IIoT) systems.
- Case studies demonstrating the impact of intelligent sensors on operational efficiency and safety.
- Emerging trends and future directions in industrial sensor technology.
Dr. Lisha Peng
Dr. Oleksii Karpenko
Dr. Hongyu Sun
Dr. Zhichao Cai
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.
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Keywords
- non-destructive testing
- structural health monitoring
- intelligent sensors
- signal processing
- industrial applications
- predictive maintenance
- machine learning
- real-time monitoring
- Industrial IoT (IIoT)
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