A5006189098- Machine Fault Diagnosis Techniques
- Structural Health Monitoring Techniques
- Gear and Bearing Dynamics Analysis
- Fault Detection and Control Systems
- Ultrasonics and Acoustic Wave Propagation
- Hydraulic and Pneumatic Systems
- Acoustic Wave Phenomena Research
- Engineering Diagnostics and Reliability
- Non-Destructive Testing Techniques
- Image and Signal Denoising Methods
- Tribology and Lubrication Engineering
- Advanced machining processes and optimization
- Advanced Sensor and Control Systems
- Advanced Measurement and Detection Methods
- Hearing Loss and Rehabilitation
- Mechanical Failure Analysis and Simulation
- Numerical methods in engineering
- Advanced Numerical Analysis Techniques
- Noise Effects and Management
- Reliability and Maintenance Optimization
- Optical measurement and interference techniques
- Advanced Algorithms and Applications
- Industrial Technology and Control Systems
- Membrane Separation Technologies
- Advanced Battery Technologies Research
Wenzhou University
2016-2025
Taizhou University
2025
Hunan University of Science and Technology
2025
Wenzhou Medical University
2025
First Affiliated Hospital of Wenzhou Medical University
2025
Shaoxing University
2024
Northeast Electric Power University
2023-2024
Shanghai University of Political Science and Law
2024
Hubei University of Technology
2024
Dongguan University of Technology
2024
Complete fault sample is essential to activate artificial intelligent (AI) models. A novel detection scheme proposed build a bridge between AI and real-world running mechanical systems. First, the finite element method simulation used simulate samples with different faults overcome shortcoming of missing samples. Second, enlarge datasets, new similar measurement are generated by generative adversarial networks further combined original obtain synthetic Finally, unknown severed as training...
The existing fault diagnosis methods of rotating machinery constructed with both shallow learning and deep models are mostly based on vibration analysis under steady speed. However, the speed frequently changes to meet practical engineering needs. largely depend domain experience feature extraction, training a model requires large samples long time. In addition, monitoring has shortcomings contact measurement, small coverage, noise interference. To address these problems, this article...
This work presents the development of novel convolutional neural network (NCNN) for effective identification bearing defects from small samples. For feature learning training data, cost function convolution (CNN) is modified by adding additional sparsity in existing function. A trigonometric cross-entropy developed to compute cost. The proposed introduces avoiding unnecessary activation neurons hidden layers CNN. samples, NCNN-based transfer applied following manner. First, raw vibration...
It is inevitable for gear to become damaged, which has a profound effect on the performance of transmission systems. Solving problem fault detection using artificial intelligence models depends sufficient samples, though they might not always exist. A new method numerical simulation and generative adversarial network (GAN) proposed enlarge samples detecting faults in gears. First, supplement missing employed obtain samples. Then, measurement are input into GAN generate synthetic training...
In intelligent fault diagnosis, the success of artificial intelligence (AI) models is highly dependent on labeled training samples, which may not be obtained in real-world applications. Recently, a finite element method (FEM) simulation-based personalized diagnosis was developed to overcome problems insufficient and incomplete samples. However, simulation signals using FEM measured actually have certain deviation. To supplement method, domain adaptation (DA) proposed bridge gap between...
This paper presents a novel data-driven predictive maintenance scheduling framework for aircraft engines based on remaining useful life (RUL) prediction. First, deep learning ensemble model is proposed to effectively predict engine RUL, including one-dimensional convolutional neural network (CNN) and bidirectional long short-term memory with an attention mechanism (Bi-LSTM-AM). Second, we propose Bayesian optimization method optimize the hyperparameters in further improve RUL prediction...
Abstract: When a structure is subjected to dynamic or static loads, cracks may develop and the modal shapes frequencies of cracked change accordingly. Based on this, new method proposed locate beam estimate their depths. The fault‐induced shape frequency changes structures are taken into account construct hybrid crack detection method. includes two steps: localization depth estimation. locations determined by applying wavelet transform shape. Using measured natural as inputs, depths...
Abstract Early identification of rolling element defects is always a topic interest for researchers and the industry. For early fault identification, simple effective dynamic degradation monitoring method using variational mode decomposition (VMD) based trigonometric entropy measure developed. First, vibration signals are obtained further decomposed VMD to obtain various frequency modes. Second, developed monitor change occurring in health bearing. Third, modes computed. Fourth, variance...
Bearing, an importunate component of any rotary machinery, is jeopardized to its failure during operation in tough working conditions. The condition monitoring bearing, avoid unforeseen failure, important for smooth working. Bearing damage assessment mostly done by selecting features from the vibration signals, which usually, a time consuming process. Consequently, it becomes us achieve full automation safety purpose and reduction maintenance cost machinery. Towards this omnifarious effort,...