A novel fusion diagnosis method for rotor system fault based on deep learning and multi-sourced heterogeneous monitoring data
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Summary
A novel multi-mode convolutional neural network is proposed to automatically learn fault-sensitive features from raw multisensory data composed of vibration signals and infrared images, and t-distributed stochastic neighbor embedding is introduced to fuse the deep features to improve the quality of the learned features.
- Type
- article
- Published
- 2018-10-04
- Cited by
- 45
- References
- 40
- Access
- Open access
- OpenAlex
- https://openalex.org/W2891841764
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:125817495
Keywords
Computer science, Fault (geology), Deep learning, Convolutional neural network, Artificial intelligence
References
- Thermal image based fault diagnosis for rotating machinery
- An approach to fault diagnosis of reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks
- Intelligent fault diagnosis of rotating machinery using infrared thermal image
- Fault detection in multi-sensor networks based on multivariate time-series models and orthogonal transformations
- Multisensor data fusion: A review of the state-of-the-art
- Infrared thermography for condition monitoring – A review
- Gearbox Fault Identification and Classification with Convolutional Neural Networks
- Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings
- Visualizing Data using t-SNE
- Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis
- Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data
- Rolling bearing fault diagnosis using an optimization deep belief network
- Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals
- A sparse auto-encoder-based deep neural network approach for induction motor faults classification
- String representations and distances in deep Convolutional Neural Networks for image classification
- Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach
- Segmented infrared image analysis for rotating machinery fault diagnosis
- NSCT-Based Infrared Image Enhancement Method for Rotating Machinery Fault Diagnosis
- Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification
- Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis
Cited by
- Sigma-z random forest, classification and confidence
- Tri-axial vibration information fusion model and its application to gear fault diagnosis in variable working conditions
- Fault Diagnosis Method Based on Principal Component Analysis and Broad Learning System
- A Wasserstein gradient-penalty generative adversarial network with deep auto-encoder for bearing intelligent fault diagnosis
- Intelligent Fault Diagnosis of 3D Printers based on Reservoir Computing
- Applications of machine learning to machine fault diagnosis: A review and roadmap
- A comprehensive review on convolutional neural network in machine fault diagnosis
- Stepwise Intelligent Diagnosis Method for Rotor System with Sliding Bearing Based on Statistical Filter and Stacked Auto-Encoder
- Pre-classified reservoir computing for the fault diagnosis of 3D printers
- Role of artificial intelligence in rotor fault diagnosis: a comprehensive review
- Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead
- A self-Adaptive CNN with PSO for bearing fault diagnosis
- Multi-sensor heterogeneous data fusion method for rotor system diagnosis based on multi-mode residual network and discriminant correlation analysis
- Research on the fault monitoring method of marine diesel engines based on the manifold learning and isolation forest
- Condition Monitoring of Drive Trains by Data Fusion of Acoustic Emission and Vibration Sensors
- Eigen-spectrograms: An interpretable feature space for bearing fault diagnosis based on artificial intelligence and image processing
- Autocorrelation energy and aquila optimizer for MED filtering of sound signal to detect bearing defect in Francis turbine
- Fault diagnosis methods based on machine learning and its applications for wind turbines: a review
- A hybrid deep-learning model for fault diagnosis of rolling bearings in strong noise environments
- Grouping sparse filtering: a novel down-sampling approach toward rotating machine intelligent diagnosis in 1D-convolutional neural networks
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