A5101666118- Machine Fault Diagnosis Techniques
- Gear and Bearing Dynamics Analysis
- Fault Detection and Control Systems
- Structural Health Monitoring Techniques
- Advanced MEMS and NEMS Technologies
- Engineering Diagnostics and Reliability
- Industrial Vision Systems and Defect Detection
- stochastic dynamics and bifurcation
- Piezoelectric Actuators and Control
- Spectroscopy and Chemometric Analyses
- Advanced machining processes and optimization
- Probabilistic and Robust Engineering Design
- Advanced Sensor and Control Systems
- Advanced Sensor and Energy Harvesting Materials
- Advanced Algorithms and Applications
- Industrial Technology and Control Systems
- Advanced Measurement and Detection Methods
- Innovative Energy Harvesting Technologies
- Non-Destructive Testing Techniques
- Structural Analysis and Optimization
- Reliability and Maintenance Optimization
- Mechanical Engineering and Vibrations Research
- Tribology and Lubrication Engineering
- Lubricants and Their Additives
- Magnetic Bearings and Levitation Dynamics
Anhui University
2016-2025
Xi'an Jiaotong University
2022-2024
State Key Laboratory of Electrical Insulation and Power Equipment
2022-2024
China Tobacco
2024
Shandong University of Science and Technology
2023-2024
Collaborative Innovation Center of Chemistry for Energy Materials
2023
China Earthquake Administration
2017-2020
Henan University of Science and Technology
2016-2019
University of South China
2018
Peking University
2018
Bearing is the key part of mechanical equipment, which can support rotating machinery running. It crucial to diagnose bearing fault in time ensure equipment working well. Effective feature extraction an essential step diagnosis. However, vibration signals collected from usually contain interference, such as heavy noise. difficult extract effective due interference. To overcome this issue, a new method for rolling faults diagnosis proposed based on hierarchical improved envelope spectrum...
Time-frequency analysis can reveal an intrinsic signature for representing nonstationary signals machine health diagnosis. This paper proposes a novel time-frequency signature, called manifold (TFM), by addressing learning on generated distributions (TFDs). The TFM is produced in three steps. First, the phase space reconstruction (PSR) employed to reconstruct inherent dynamic embedded analyzed signal. Second, TFDs are calculated represent information space. Third, conducted discover...
Edge computing is an emerging paradigm where computation executed on the edge of networks rather than cloud servers, thereby reducing system response time, transmission bandwidth occupation, and storage resources cloud. In this paper, computing-based method for real-time fault diagnosis dynamic control rotating machines proposed, node (ECN) designed. A vibration signal three motor-phase current signals are acquired synchronously by ECN. Subsequently, 40 features extracted from fused into...
Convolutional neural networks (CNNs) are one of the most efficient deep learning techniques and have been widely used in motor fault diagnosis. However, them implemented desktop computers to process off-line signals. In this paper, an situ diagnosis method is proposed by implementing enhanced CNN model into a designed embedded system consisting Raspberry Pi signal acquisition processing circuit. To best our knowledge, topic has not investigated yet literature. First, hardware, algorithms,...
Effective feature extraction is crucial for accurate fault diagnosis of rolling bearings. A novel method called hierarchical dispersion entropy (HDE) based on analysis proposed in this study. The includes the following three steps: 1) bearing vibration signal decomposed into a series subband components; 2) entropies components different frequency bands are calculated as original vector; and 3) joint approximate diagonalization eigenmatrices (JADE) used to extract fusion features from...
A bearing is a key component in rotating machinery. The prompt monitoring of bearings’ condition critical for the reduction mechanical accidents. With rapid development artificial intelligence technology recent years, machine learning-based intelligent fault diagnosis (IFD) methods have achieved remarkable success field monitoring. However, most algorithms are developed based on computer platforms that focus analyzing offline, rather than real-time, signals. In this paper, an edge method...
This paper proposes a novel contactless angular resampling method for motor bearing fault diagnosis under variable speed. involves three steps: (1) the instantaneous rotating angle is measured using Kanade-Lucas-Tomasi object tracking algorithm from video that recorded by high-speed camera; (2) signal acquired microphone resampled in domain based on accumulated curve; and (3) demodulated to obtain characteristic frequency order analysis (OA) recognition. has been proven effective diagnosing...
Continuous condition monitoring and fault diagnosis of motor bearings are vital to guarantee safety operation reduce breakdown losses. With numerous Internet things (IoT) sensors being installed on motors for monitoring, data transmission storage problems have become new challenges. This study designed a signal enhancement compression (SEC) method implemented an IoT platform bearing diagnosis. First, vibration is acquired from accelerometer the motor. The demodulated using online...
Data-driven based fault diagnosis methods play an increasingly important role in rotating machinery. Among these methods, deep learning is widely concerned due to its strong nonlinear feature ability, wherein ResNet a very powerful model. However, the diagnostic performance of model depends on sufficient labeled data samples, which extremely difficult achieve under actual complex working conditions. In this paper, multidimensional normalized proposed for cross-working conditions limited...
Understanding the response of soil microbial communities to pathogenic Ralstonia solanacearum is crucial for preventing bacterial wilt outbreaks. In this study, we investigated physicochemical and community assess their impact on R.solanacearum through metagenomics. Our results revealed that certain archaeal taxa were main contributors influencing health plants. Additionally, presence pathogen showed a strong negative correlation with phosphorus levels, while was significantly correlated...