A Generic Indirect Deep Learning Approach for Multisensor Degradation Modeling

A Generic Framework for Multisensor Degradation Modeling based on Supervised Classification and Failure Surface

(2019) Changyue Song et al.   IISE Transactions

A Generic Health Index Approach for Multisensor Degradation Modeling and Sensor Selection

(2019) Minhee Kim et al.   IEEE Transactions on Automation Science and Engineering

Integration of Data-Level Fusion Model and Kernel Methods for Degradation Modeling and Prognostic Analysis

(2018) Changyue Song et al.   IEEE TRANSACTIONS ON RELIABILITY

Long short-term memory for machine remaining life prediction

(2018) Jianjing Zhang et al.   JOURNAL OF MANUFACTURING SYSTEMS

A data-level fusion approach for degradation modeling and prognostic analysis under multiple failure modes

(2018) Abdallah Chehade et al.   JOURNAL OF QUALITY TECHNOLOGY

Remaining useful life estimation of engineered systems using vanilla LSTM neural networks

(2018) Yuting Wu et al.   NEUROCOMPUTING

Remaining useful life estimation in prognostics using deep convolution neural networks

(2018) Xiang Li et al.   RELIABILITY ENGINEERING & SYSTEM SAFETY

Using Deep Learning-Based Approach to Predict Remaining Useful Life of Rotating Components

(2018) Jason Deutsch et al.   IEEE Transactions on Systems Man Cybernetics-Systems

Statistical degradation modeling and prognostics of multiple sensor signals via data fusion: A composite health index approach

(2018) Changyue Song et al.   IISE Transactions

Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation

(2018) Ansi Zhang et al.   Applied Sciences-Basel

Controlling the Residual Life Distribution of Parallel Unit Systems Through Workload Adjustment

(2017) Li Hao et al.   IEEE Transactions on Automation Science and Engineering

Optimize the Signal Quality of the Composite Health  Index via Data Fusion for Degradation Modeling  and Prognostic Analysis

(2017) Kaibo Liu et al.   IEEE Transactions on Automation Science and Engineering

Sensory-Based Failure Threshold Estimation for Remaining Useful Life Prediction

(2017) Abdallah Chehade et al.   IEEE TRANSACTIONS ON RELIABILITY

Multistream sensor fusion-based prognostics model for systems with single failure modes

(2017) Xiaolei Fang et al.   RELIABILITY ENGINEERING & SYSTEM SAFETY

Integration of Data Fusion Methodology and Degradation Modeling Process to Improve Prognostics

(2016) Kaibo Liu et al.   IEEE Transactions on Automation Science and Engineering

Multiple Sensor Data Fusion for Degradation Modeling and Prognostics Under Multiple Operational Conditions

(2016) Hao Yan et al.   IEEE TRANSACTIONS ON RELIABILITY

State-of-Health Monitoring and Prediction of Lithium-Ion Battery Using Probabilistic Indication and State-Space Model

(2015) Jianbo Yu   IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT

Deep learning

(2015) Yann LeCun et al.   NATURE

Deep learning in neural networks: An overview

(2015) Jürgen Schmidhuber   NEURAL NETWORKS

A Data-Level Fusion Model for Developing Composite Health Indices for Degradation Modeling and Prognostic Analysis

(2013) Kaibo Liu et al.   IEEE Transactions on Automation Science and Engineering

The Inverse Gaussian Process as a Degradation Model

(2013) Zhi-Sheng Ye et al.   TECHNOMETRICS

Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life

(2012) Chao Hu et al.   RELIABILITY ENGINEERING & SYSTEM SAFETY

Residual Life Predictions in the Absence of Prior Degradation Knowledge

(2009) N. Gebraeel et al.   IEEE TRANSACTIONS ON RELIABILITY

An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring

(2009) Zhigang Tian   JOURNAL OF INTELLIGENT MANUFACTURING