Information Theory and Its Application in Machine Condition Monitoring

• Muut tekijät: Li, Yongbo (Toimittaja), Gu, Fengshou (Toimittaja), Liang, Xihui (Toimittaja)
• Aineistotyyppi: Elektroninen Kirjan osa
• Kieli: englanti
• Julkaistu: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
• Aiheet: Technology: general issues; History of engineering & technology; fault detection; deep learning; transfer learning; anomaly detection; bearing; wind turbines; misalignment; fault diagnosis; information fusion; improved artificial bee colony algorithm; LSSVM; D-S evidence theory; optimal bandwidth; kernel density estimation; JS divergence; domain adaptation; partial transfer; subdomain; rotating machinery; gearbox; signal interception; peak extraction; cubic spline interpolation envelope; combined fault diagnosis; empirical wavelet transform; grey wolf optimizer; low pass FIR filter; support vector machine; satellite momentum wheel; Huffman-multi-scale entropy (HMSE); support vector machine (SVM); adaptive particle swarm optimization (APSO); rail surface defect detection; machine vision; YOLOv4; MobileNetV3; multi-source heterogeneous fusion; n/a
• Linkit: DOAB: download the publication; DOAB: description of the publication

Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a...