A5034723364- Machine Fault Diagnosis Techniques
- Gear and Bearing Dynamics Analysis
- Industrial Technology and Control Systems
- Manufacturing Process and Optimization
- Advanced X-ray and CT Imaging
- Advanced machining processes and optimization
- Electric Power Systems and Control
- Fault Detection and Control Systems
- Railway Engineering and Dynamics
- Radiomics and Machine Learning in Medical Imaging
- Machine Learning and ELM
Xi'an University of Technology
2022-2025
In this article, a novel prediction index is constructed, hybrid filtering proposed, and remaining useful life (RUL) framework developed. the proposed framework, different models are built for operation states of rolling bearings. normal state, linear model built, Kalman filter (KF) implemented to determine failure start time (FST). degradation dimensionless CRRMS based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) wavelet threshold. Then, double exponential...
Classification of coronary artery stenosis is essential in assisting physicians diagnosing cardiovascular diseases. However, due to the complexity medical diagnosis and confidentiality images, it difficult obtain many image samples for scientific research general. In addition, degree, location, morphology different patients, as well noise CT angiography (CTA) make challenging extract typing features effectively. To address above problems, firstly, a joint segmentation method proposed based...
Abstract Railway point machine (RPM) condition monitoring has attracted engineers’ attention for safe train operation and accident prevention. To realize the fast accurate fault diagnosis of RPMs, this paper proposes a method based on entropy measurement broad learning system (BLS). Firstly, modified multi-scale symbolic dynamic (MMSDE) module extracts characteristics from collected acoustic signals as features. Then, fuzzy BLS takes above features input to complete model training. Fuzzy...
The fault diagnosis of railway point machines (RPMs) has attracted the attention engineers and researchers. Seldom have studies considered diverse noises along track. To fulfill this aspect, a multi-time-scale variational mode decomposition (MTSVMD) is proposed in paper to realize accurate robust RPMs under multiple noises. MTSVMD decomposes condition monitoring signals after coarse-grained processing varying degrees. In manner, information contained signal components at time scales can...