Guest Editors
Muhammad Irfan, Associate Professor, Najran University, Saudi Arabia
He has over 09 years of experience in industry, academia and research. Currently, he is working as an associate professor in the Electrical Engineering Department, Najran University Saudi Arabia. He has received several research awards, including the Best Researcher Award from Najran University Saudi Arabia. He has authored over 200 research articles in journals, books and conference proceedings (Google Scholar Citations 2700 , H-index 24). He has one patent application in the US Patent Office and one patent granted by the Malaysian Patent Office. He has secured several research grants. His main research areas are in Energy Efficiency, Condition Monitoring, Vibration Analysis, Artificial Intelligence, Internet of Things (IoT), Big Data Analytics, Smart Cities and Smart Healthcare.
Adam Glowacz, Associate Professor, Department of Automatic, Control and Robotics, AGH University of Science and Technology, Poland
Dr. Adam Glowacz deals with signal processing, image processing, acoustic/vibration analysis, fault diagnosis, pattern recognition, and artificial intelligence. He received his Ph.D. in Computer Science from the AGH University of Science and Technology, Cracow, Poland, in 2013. He works at the AGH University of Science and Technology as Associate Professor since 2020. Adam Glowacz is the author/co-author of 190 scientific papers (145 papers are indexed by Web of Science). He has h-index=35 and 3,492 citations from Web of Science. He has h-index=38 (4000 citations, Scopus). He is the author of 1000 reviews of scientific papers.
Summary
Unplanned interruption of industrial processes can result in serious financial losses; as such, it becomes of significant relevance to prevent unplanned shutdowns of machinery. The monitoring and diagnosis of the current health state of the machine is crucial in achieving this.
Acoustic-based machine condition monitoring is a method that utilizes sound analysis to determine the health of industrial machinery. It's an alternative to traditional vibration and performance monitoring techniques, which often require contact-type sensors like accelerometers or temperature transducers to be installed directly on the equipment. The acoustic method offers several advantages, such as the ability to implement sensors quickly and inexpensively, and without the need for physical contact with the machine. This can be particularly beneficial in situations where adding additional sensors is not feasible due to cost, space constraints, or concerns about sensor reliability.
However, there are challenges associated with acoustic-based monitoring. The acoustic signals collected can be sensitive to background noise and may change with the machine's operating conditions. Addressing these challenges is crucial for the industrial applicability of Vibration-based and acoustic-based monitoring systems.
We welcome high-quality papers discussing the current state of the art and highlighting the key challenges that need to be addressed to advance the field further.
Potential topics include but are not limited to:
Modern vibration analysis techniques
Artificial Intelligence for machine fault analysis
Acoustic Emission techniques for fault monitoring
Modern signal processing & image processing techniques for condition monitoring
Modern sensors and Internet of Things (IoT) for fault diagnosis
Keywords
Acoustic and vibration sensing
Acoustic and vibration emissions
Acoustic sensors
Machine condition monitoring
Acoustics, speech and signal processing
Acoustic evaluation of faults in electrical machines
Anomalous sound detection
Signal processing of audio and acoustics
Sound dataset
Malfunctioning industrial machine investigation and inspection
Detection and classification of acoustic scenes and events
Industrial sound analysis
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