Deep learning and its applications to machine health monitoring
Explore this paper's citation graph
Summary
The applications of deep learning in machine health monitoring systems are reviewed mainly from the following aspects: Auto-encoder and its variants, Restricted Boltzmann Machines, Convolutional Neural Networks, and Recurrent Neural Networks.
- Type
- article
- Published
- 2019-01-01
- Cited by
- 2,612
- References
- 110
- OpenAlex
- https://openalex.org/W2810292802
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:125608550
Keywords
Deep learning, Artificial intelligence, Boltzmann machine, Machine learning, Restricted Boltzmann machine
References
- Robust identification of pneumatic servo actuators in the real situations
- Analysis of Feature Extracting Ability for Cutting State Monitoring Using Deep Belief Networks
- Multi-layer neural network with deep belief network for gearbox fault diagnosis
- STOCHASTIC PROGNOSTICS FOR ROLLING ELEMENT BEARINGS
- Model-Based Prognosis for Hybrid Systems With Mode-Dependent Degradation Behaviors
- Learning and Extracting Finite State Automata with Second-Order Recurrent Neural Networks
- Data-Based Techniques Focused on Modern Industry: An Overview
- Acoustic Modeling Using Deep Belief Networks
- Adaptive Input Design for Identification of Output Error Model with Constrained Output
- ARTIFICIAL NEURAL NETWORK BASED FAULT DIAGNOSTICS OF ROLLING ELEMENT BEARINGS USING TIME-DOMAIN FEATURES
- A comparative study of Naïve Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis
- Physically based diagnosis and prognosis of cracked rotor shafts
- Extracting and composing robust features with denoising autoencoders
- Degradation Assessment and Fault Modes Classification Using Logistic Regression
- A review on machinery diagnostics and prognostics implementing condition-based maintenance
- Original Contribution: Approximation of dynamical systems by continuous time recurrent neural networks
- Failure diagnosis using deep belief learning based health state classification
- Long Short-Term Memory
- Deep learning in neural networks: An overview
- Support vector machine in machine condition monitoring and fault diagnosis
Cited by
- Deep BBN Learning for Health Assessment toward Decision-Making on Structures under Uncertainties
- An overview on the deep learning based prognostic
- Gear pitting fault diagnosis using disentangled features from unsupervised deep learning
- Health Monitoring for Balancing Tail Ropes of a Hoisting System Using a Convolutional Neural Network
- A novel deep output kernel learning method for bearing fault structural diagnosis
- A novel fusion diagnosis method for rotor system fault based on deep learning and multi-sourced heterogeneous monitoring data
- Differential evolution optimization for resilient stacked sparse autoencoder and its applications on bearing fault diagnosis
- Online Tool Wear Classification during Dry Machining Using Real Time Cutting Force Measurements and a CNN Approach
- An End-to-End Model Based on Improved Adaptive Deep Belief Network and Its Application to Bearing Fault Diagnosis
- A survey on Deep Learning based bearing fault diagnosis
- Rolling-Element Bearing Fault Data Automatic Clustering Based on Wavelet and Deep Neural Network
- A review of deep learning in the study of materials degradation
- Construction of a batch-normalized autoencoder network and its application in mechanical intelligent fault diagnosis
- Weak Crack Detection for Gearbox Using Sparse Denoising and Decomposition Method
- An Image Copy-Move Forgery Detection Scheme Based on A-KAZE and SURF Features
- Kernel regression residual decomposition-based synchroextracting transform to detect faults in mechanical systems.
- Two Birds with One Network: Unifying Failure Event Prediction and Time-to-failure Modeling
- Ensemble deep learning-based fault diagnosis of rotor bearing systems
- Online Recognition Method for Voltage Sags Based on a Deep Belief Network
- A novel convolutional neural network based fault recognition method via image fusion of multi-vibration-signals
Related papers
- Representational Power of Restricted Boltzmann Machines and Deep Belief Networks
- Experiment Improvement of Restricted Boltzmann Machine Methods for Image Classification
- Extensive Deep Belief Nets with Restricted Boltzmann Machine Using MapReduce Framework
- Weight Uncertainty in Boltzmann Machine
- An Overview of Restricted Boltzmann Machines
- Incremental extreme learning machine based on deep feature embedded
- Deep Belief Network for clustering and classification of a continuous data
- The Self-Organizing Restricted Boltzmann Machine for Deep Representation with the Application on Classification Problems
- Deep-FS: A feature selection algorithm for Deep Boltzmann Machines