A5078476204- Gear and Bearing Dynamics Analysis
- Machine Fault Diagnosis Techniques
- Tribology and Lubrication Engineering
- Advanced machining processes and optimization
- Mechanical Engineering and Vibrations Research
- Fault Detection and Control Systems
- Structural Health Monitoring Techniques
- Engineering Diagnostics and Reliability
- Reliability and Maintenance Optimization
- Manufacturing Process and Optimization
- Software Reliability and Analysis Research
- Risk and Safety Analysis
- Anomaly Detection Techniques and Applications
- Additive Manufacturing and 3D Printing Technologies
- Spectroscopy and Chemometric Analyses
- Probabilistic and Robust Engineering Design
- Additive Manufacturing Materials and Processes
- Fatigue and fracture mechanics
- Mechanical stress and fatigue analysis
- Analytical Chemistry and Chromatography
- Safety Systems Engineering in Autonomy
- Non-Destructive Testing Techniques
- Vehicle Dynamics and Control Systems
- Mechanical Failure Analysis and Simulation
- Iterative Learning Control Systems
University of Manitoba
2014-2025
Merck & Co., Inc., Rahway, NJ, USA (United States)
2020-2024
Institute of Semiconductors
2020-2023
Guangdong Academy of Sciences
2020-2023
Southwest Jiaotong University
2022
Henan Normal University
2021
United States Military Academy
2019
University of Alberta
2013-2018
Ocean University of China
2010
Shandong University
2009
This paper presents a novel signal processing scheme, bandwidth empirical mode decomposition, and adaptive multiscale morphological analysis (BEMD-AMMA) for early fault diagnosis of rolling bearings. In this we propose based method to select the best envelope interpolation method. First, multiple algorithms are defined separately subtracted from original data obtain preintrinsic functions (PIMFs). Second, an IMF with smallest frequency is selected be optimal (OIMF). Third, OIMF signal, then...
Time-varying mesh stiffness is a periodic function caused by the change in number of contact tooth pairs and positions gear teeth. It one main sources vibration transmission system. An efficient effective way to evaluate time-varying essential comprehensively understand dynamic properties planetary set. According literature, there are two ways stiffness, finite element method analytical method. The time-consuming because needs model every meshing pair order know range pairs. On other hand,...
This article presents a new deep transfer learning method, named structured domain adversarial neural network (SDANN), for bearing fault diagnosis with the data collected under different working conditions. The key idea of this method is integrating strong adaptability (DANN) and relatedness information among multiple failure modes to improve effect learning. First, fine-grained alignment between from conditions, loss function discriminative regularizer designed DANN by using maximum...
This article tries to answer the two questions of bearings' remaining useful life (RUL) prediction with deep transfer learning: what bearing data in source domain contribute more learning and how quantify such contribution? From perspective sample-based interpretability, this proposes a new approach RUL prediction. First, time series clustering algorithm based on multiscale degradation similarity is proposed. Comprehensively considering geometry tendency characteristics sequences, can divide...
This article proposes a novel deep transfer learning-based online remaining useful life (RUL) approach for rolling bearings under unknown working condition. solves the following concerns: drift of condition would block data accumulation and raise bias in prediction model, bearing merely has early fault when activating RUL prediction, failing to conduct learning from offline data. First, new time series recursive forecasting model is constructed generate pseudovalues via fusing prior...
Despite the big success of transfer learning techniques in anomaly detection, it is still challenging to achieve good transition detection rules merely based on preferred data with one-class classification, especially for a large distribution difference. To address this challenge, novel deep algorithm domain-adversarial training proposed paper. First, by integrating hypersphere adaptation constraint into neural network, new adversarial mechanism designed. Second, an alternative optimization...
Abstract Dielectric ceramics with both excellent energy storage and optical transmittance have attracted much attention in recent years. However, the transparent Pb‐free energy‐storage were rare reported. In this work, we prepared relaxor ferroelectric (1 − x )Bi 0.5 Na TiO 3 – NaNbO (BNT– NN) by conventional solid‐state reaction method. We find NN‐doping can enhance polarization breakdown strength of BNT suppressing grain growth restrained reduction Ti 4+ to 3+ . As a result, high...