Deep transfer learning with limited data for machinery fault diagnosis

Stacked pruning sparse denoising autoencoder based intelligent fault diagnosis of rolling bearings

(2020) Haiping Zhu et al.   APPLIED SOFT COMPUTING

Remaining useful life prediction using multi-scale deep convolutional neural network

(2020) Han Li et al.   APPLIED SOFT COMPUTING

Research on feature selection for rotating machinery based on Supervision Kernel Entropy Component Analysis with Whale Optimization Algorithm

(2020) Lili Bai et al.   APPLIED SOFT COMPUTING

Hybrid attribute conditional adversarial denoising autoencoder for zero-shot classification of mechanical intelligent fault diagnosis

(2020) Haixin Lv et al.   APPLIED SOFT COMPUTING

Gearbox Fault Diagnosis Using a Deep Learning Model With Limited Data Sample

(2020) Syahril Ramadhan Saufi et al.   IEEE Transactions on Industrial Informatics

A novel gas turbine fault diagnosis method based on transfer learning with CNN

(2019) Shi-sheng Zhong et al.   MEASUREMENT

An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings

(2019) Bin Yang et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application

(2019) Te Han et al.   ISA TRANSACTIONS

A novel bearing intelligent fault diagnosis framework under time-varying working conditions using recurrent neural network

(2019) Zenghui An et al.   ISA TRANSACTIONS

A novel deep learning method based on attention mechanism for bearing remaining useful life prediction

(2019) Yuanhang Chen et al.   APPLIED SOFT COMPUTING

Fault diagnostics between different type of components: A transfer learning approach

(2019) Xudong Li et al.   APPLIED SOFT COMPUTING

An improved feature extraction method using texture analysis with LBP for bearing fault diagnosis

(2019) Kaplan Kaplan et al.   APPLIED SOFT COMPUTING

Intelligent Fault Identification Based on Multisource Domain Generalization Towards Actual Diagnosis Scenario

(2019) Huailiang Zheng et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Diagnosing Rotating Machines With Weakly Supervised Data Using Deep Transfer Learning

(2019) Xiang Li et al.   IEEE Transactions on Industrial Informatics

A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings

(2019) Zepeng Liu et al.   MEASUREMENT

Deep Learning-Based Machinery Fault Diagnostics With Domain Adaptation Across Sensors at Different Places

(2019) Xiang Li et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Robust Interpretable Deep Learning for Intelligent Fault Diagnosis of Induction Motors

(2019) Firas Ben Abid et al.   IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT

Entropy Based Fault Classification Using the Case Western Reserve University Data: A Benchmark Study

(2019) Yongbo Li et al.   IEEE TRANSACTIONS ON RELIABILITY

Classifier Inconsistency-Based Domain Adaptation Network for Partial Transfer Intelligent Diagnosis

(2019) Jinyang Jiao et al.   IEEE Transactions on Industrial Informatics

A review on the application of deep learning in system health management

(2018) Samir Khan et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

A review on data-driven fault severity assessment in rolling bearings

(2018) Mariela Cerrada et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

A Convolutional Neural Network Based on a Capsule Network with Strong Generalization for Bearing Fault Diagnosis

(2018) Zhiyu Zhu et al.   NEUROCOMPUTING

Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation

(2018) Xiang Li et al.   JOURNAL OF INTELLIGENT MANUFACTURING

A novel deep output kernel learning method for bearing fault structural diagnosis

(2018) Wentao Mao et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems

(2018) Te Han et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults

(2018) Te Han et al.   KNOWLEDGE-BASED SYSTEMS

Multi-Layer domain adaptation method for rolling bearing fault diagnosis

(2018) Xiang Li et al.   SIGNAL PROCESSING

Deep Model Based Domain Adaptation for Fault Diagnosis

(2017) Weining Lu et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet

(2017) Haidong Shao et al.   ISA TRANSACTIONS

Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery

(2017) Te Han et al.   TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL

A Survey on Wind Turbine Condition Monitoring and Fault Diagnosis—Part II: Signals and Signal Processing Methods

(2015) Wei Qiao et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

A Survey on Wind Turbine Condition Monitoring and Fault Diagnosis—Part I: Components and Subsystems

(2015) Wei Qiao et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study

(2015) Wade A. Smith et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Deep learning

(2015) Yann LeCun et al.   NATURE