Sensors for Fault Detection and Condition Monitoring
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".
Deadline for manuscript submissions: closed (30 March 2024) | Viewed by 20037
Special Issue Editor
Special Issue Information
Dear Colleagues,
Industrial automation (Industry 4.0), along with recent developments in artificial intelligence, has played a significant role in the enhancement of technological advancements in modern society. Fault detection and diagnosis are essential for the elimination of production losses and safety hazards due to complex automated installations. The early detection of faults in machineries can help in reducing the wear and tear of internal components, thereby enhancing their useful lifetime and reliability. Additionally, the identification of specific fault types can assist service personnel equip for preventive maintenance. Hence, recently, fault diagnosis and detection have gained the attention of several researchers. Additionally, intelligent fault diagnosis techniques that deliver instantaneous results are the need of the hour. Fault detection and diagnostic techniques involve the consumption of high volumes of data from sensors. Several types of sensors, such as piezoelectric, temperature, pressure, force, acoustic, etc., have been adopted, along with machine learning and deep learning techniques utilized to identify the health condition of machinery. However, certain challenges, such as data augmentation (deep learning), multifault diagnosis, machine learning algorithms for small datasets, data and sensor fusion, interpretation of deep learning algorithms and feature representation, still persist.
This Special Issue aims to highlight various sensors and techniques adopted for performing intelligent fault diagnosis and detection in machineries. Furthermore, innovations in fault feature extraction, feature selection strategies and the development of novel machine learning algorithms aiming for higher accuracy and minimal computational efforts are appreciable. Contributors are welcome to submit review/original articles addressing (but not limited to) the topics in the list of keywords found below.
- sensor vibration/sound/force/AE;
- machine learning /deep learning;
- fault detection/fault diagnosis/prognosis;
- oil analysis using sensors;
- low-cost sensors for fault diagnosis;
- wavelets/fractals applied to fault diagnosis;
- FD of mechanical components, such as bearings, gears, belts, etc.;
- FD of automobile components, such as engines, brakes, clutches, etc.;
- FD of energy systems, such as wind turbine, photo voltaic module, etc.
Prof. Dr. Sugumaran Vaithiyanathan
Guest Editor
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