Mechanical Fault Diagnosis and Signal Processing
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".
Deadline for manuscript submissions: 20 January 2025 | Viewed by 763
Special Issue Editors
Interests: condition monitoring; fault detection; artificial intelligence; deep learning; signal processing; electromechanical systems
Special Issues, Collections and Topics in MDPI journals
Interests: cyber-physical systems; fault detection and diagnosis methods; machine and deep learning; electrical machines and drives
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue aims to collect and publish the results of innovative research in the field of mechanical fault diagnosis and signal processing. Electrical and mechanical machines such as motors, pumps, gearboxes, and compressors are widely used in industrial processes; although most of these elements have been previously assessed, new challenges arise as industry applications and machinery configurations become increasingly complex. While any machine is strictly composed of mechanical elements that can fail due to extreme operating conditions, the most common faults are related to problems in shafts, bearings, couplings, gears, and rotors.
This Special Issue will share novel signal processing methods applied to the detection and identification of mechanical faults, involving applications based on classic or advanced processing techniques like Fourier Transform, Wavelet Transform, Hilbert Transform, and MUSIC.
Thus, this Special Issue is focused on, but not limited to, the following topics:
- Intelligent fault detection;
- High-resolution signal processing;
- Noise elimination and feature extraction;
- Non-linear operating conditions;
- Electromechanical systems.
Dr. Juan Jose Saucedo-Dorantes
Dr. Miguel Delgado-Prieto
Guest Editors
Manuscript Submission Information
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Keywords
- intelligent fault detection
- high-resolution signal processing
- noise elimination and feature extraction
- non-linear operating conditions
- electromechanical systems
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