A5034074637- Machine Fault Diagnosis Techniques
- Gear and Bearing Dynamics Analysis
- Fault Detection and Control Systems
- Engineering Diagnostics and Reliability
- Structural Health Monitoring Techniques
- Advanced machining processes and optimization
- Structural Integrity and Reliability Analysis
- Ultrasonics and Acoustic Wave Propagation
- Reliability and Maintenance Optimization
- Machine Learning in Bioinformatics
- Blind Source Separation Techniques
- Image and Signal Denoising Methods
- Spectroscopy and Chemometric Analyses
- Planetary Science and Exploration
- Space Satellite Systems and Control
- Technology Assessment and Management
- Human Mobility and Location-Based Analysis
- Artificial Intelligence in Healthcare
- Anomaly Detection Techniques and Applications
- Stability and Control of Uncertain Systems
- Life Cycle Costing Analysis
- Brake Systems and Friction Analysis
- Adhesion, Friction, and Surface Interactions
- Optical Coherence Tomography Applications
- Software Reliability and Analysis Research
Beihang University
2018-2025
Ningde Normal University
2024
Shanxi Agricultural University
2024
Ningbo University of Technology
2021-2024
Shijiazhuang Tiedao University
2023-2024
Hubei University Of Economics
2023
Shanghai Shipbuilding Technology Research Institute
2023
Wuhan University
2020
Beijing Aerospace Flight Control Center
2019
University of Toronto
2019
A group of kurtosis-guided-grams, such as Kurtogram, Protrugram and SKRgram, is designed to detect the resonance band excited by faults based on sparsity index. However, a common issue associated with these methods that they tend choose frequency individual impulses rather than desired fault impulses. This may be attributed selection index, kurtosis, which vulnerable impulsive noise. In this paper, solve problem, called Gini introduced an alternative estimator for band. It has been found...
In this article, a new decomposition theory, feature mode (FMD), is tailored for the extraction of machinery fault. The proposed FMD essentially purpose decomposing different modes by designed adaptive finite-impulse response (FIR) filters. Benefitting from superiority correlated Kurtosis, takes impulsiveness and periodicity fault signal into consideration simultaneously. First, FIR filter bank Hanning window initialization used to provide direction decomposition. period estimation updating...
De-noising and enhancement of the weak fault signature from noisy signal are crucial for diagnosis, as features often very masked by background noise. Deconvolution methods have a significant advantage in counteracting influence transmission path enhancing impulses. However, performance traditional deconvolution is greatly affected some limitations, which restrict application range. Therefore, this paper proposes new method, named sparse maximum harmonics-noise-ratio (SMHD), that employs...
Bearing faults are the main contributors to failure of motors. Periodic harmonic components from motor rotating and random impulses caused by electromagnetic interference heavily trouble vibration-based resonance demodulation techniques. This paper presents a method that accurately identifies optimal frequency band even with complicated interferences industrial field. Singular value negentropy (SVN) is originally applied measure periodicity signal without prior knowledge. Based on SVN,...
Deconvolution methods have been widely used in machinery fault diagnosis. However, their application would be confined due to the heavy noise and complex interference since feature measured signal becomes rather weak. Time synchronous averaging (TSA) can enhance periodic components suppress others by comb filter function. And iteration process of deconvolution methods, filtered after each further processed using TSA, time delay with maximum Gini index value is refined as iterative period for...
Introduction Tomato leaf pests and diseases pose a significant threat to the yield quality of Q6 tomatoes, highlighting necessity for comprehensive studies on effective control methods. Methods Current measures predominantly rely experience manual observation, hindering integration multi-source data. To address this, we integrated information resources related tomato from agricultural standards documents, knowledge websites, relevant literature. Guided by domain experts, preprocessed this...