Analysis of Feature Extracting Ability for Cutting State Monitoring Using Deep Belief Networks
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Summary
Benefitting from the potential capability in information fusion, deep learning method would be a promising solution for more complex applications, like tool wear monitoring, machining surface prediction et al.
- Type
- article
- Published
- 2015-01-01
- Cited by
- 70
- References
- 20
- Access
- Open access
- OpenAlex
- https://openalex.org/W637415930
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:106472407
Keywords
Computer science, Artificial intelligence, Feature (linguistics), Deep belief network, SIGNAL (programming language)
References
- On Contrastive Divergence Learning
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- An approach to fault diagnosis of reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks
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- Advanced monitoring of machining operations
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- A review of machining monitoring systems based on artificial intelligence process models
- Application of soft computing techniques in machining performance prediction and optimization: a literature review
- Computer science: The learning machines
- On-line chatter detection and identification based on wavelet and support vector machine
- Automatic Feature Extraction for Classifying Audio Data
- Failure diagnosis using deep belief learning based health state classification
- Learning Deep Architectures for AI
- Recent developments in evolutionary computation for manufacturing optimization: problems, solutions, and comparisons
- Receptive fields, binocular interaction and functional architecture in the cat's visual cortex
- Supporting Online Material for Reducing the Dimensionality of Data with Neural Networks
- Top Downloads in IEEE Xplore [Reader's Choice]
- Deep Learning of Representations: Looking Forward
- Are Intelligent Manufacturing Systems Sustainable?
- Reducing the Dimensionality of Data with Neural Networks
Cited by
- Research and development of intelligent cutting database cloud platform system
- Deep Learning and Its Applications to Machine Health Monitoring: A Survey
- Research advances in fault diagnosis and prognostic based on deep learning
- Deep neural networks based order completion time prediction by using real-time job shop RFID data
- Method for Automatically Recognizing Various Operation Statuses of Legacy Machines
- Machining vibration states monitoring based on image representation using convolutional neural networks
- A novel adaptive fault detection methodology for complex system using deep belief networks and multiple models: A case study on cryogenic propellant loading system
- A new fault diagnosis method based on deep belief network and support vector machine with Teager–Kaiser energy operator for bearings
- A review on the application of deep learning in system health management
- An overview on the deep learning based prognostic
- Deep learning and its applications to machine health monitoring
- A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools
- Predicting tool wear with multi-sensor data using deep belief networks
- Online Tool Wear Classification during Dry Machining Using Real Time Cutting Force Measurements and a CNN Approach
- Honeycomb Core Milling Diagnosis using Machine Learning in the Industry 4.0 Framework
- Classification of Power-Quality Disturbances Using Deep Belief Network
- Deep Learning With Emerging New Labels for Fault Diagnosis
- Dynamic condition monitoring for 3D printers by using error fusion of multiple sparse auto-encoders
- Milling Diagnosis Using Machine Learning Techniques Toward Industry 4.0
- On-line part deformation prediction based on deep learning
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