A deep learning model for process fault prognosis
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Syed Imtiaz | Rajeevan Arunthavanathan | Faisal Khan | Salim Ahmed | S. Imtiaz | Salim Ahmed | F. Khan | Rajeevan Arunthavanathan
[1] Efstratios N. Pistikopoulos,et al. A Nonlinear Support Vector Machine-Based Feature Selection Approach for Fault Detection and Diagnosis: Application to the Tennessee Eastman Process. , 2019, AIChE journal. American Institute of Chemical Engineers.
[2] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[3] Syed Imtiaz,et al. Fault detection and diagnosis in process system using artificial intelligence-based cognitive technique , 2020, Comput. Chem. Eng..
[4] Josiah C. Hoskins,et al. Artificial neural network models for knowledge representation in chemical engineering , 1990 .
[5] Steven X. Ding,et al. A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches , 2015, IEEE Transactions on Industrial Electronics.
[6] Hee-Jun Kang,et al. A survey on Deep Learning based bearing fault diagnosis , 2019, Neurocomputing.
[7] Ming Chen,et al. A data-driven model for milling tool remaining useful life prediction with convolutional and stacked LSTM network , 2020 .
[8] Lirong Cui,et al. Availability and maintenance modelling for systems subject to multiple failure modes , 2017, Comput. Ind. Eng..
[9] Gong Ping,et al. An End-to-End model based on CNN-LSTM for Industrial Fault Diagnosis and Prognosis , 2018, 2018 International Conference on Network Infrastructure and Digital Content (IC-NIDC).
[10] Zengtao Hou,et al. An Integrative Framework for Online Prognostic and Health Management Using Internet of Things and Convolutional Neural Network , 2019, Sensors.
[11] Faisal Khan,et al. An analysis of process fault diagnosis methods from safety perspectives , 2021, Comput. Chem. Eng..
[12] Wei Li,et al. Remaining Useful Life Estimation Based on a New Convolutional and Recurrent Neural Network , 2019, 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE).
[13] Ling Zheng,et al. A Fault Prediction Of Equipment Based On CNN-LSTM Network , 2019, 2019 IEEE International Conference on Energy Internet (ICEI).
[14] Hongfu Zuo,et al. Fault prediction of bearings based on LSTM and statistical process analysis , 2021, Reliab. Eng. Syst. Saf..
[15] E. F. Vogel,et al. A plant-wide industrial process control problem , 1993 .
[16] Enrico Sciubba,et al. Automatic diagnostics and prognostics of energy conversion processes via knowledge-based systems , 2004 .
[17] Yu Zheng,et al. An ensemble framework based on convolutional bi-directional LSTM with multiple time windows for remaining useful life estimation , 2020, Comput. Ind..
[18] Bernhard Schölkopf,et al. Support Vector Method for Novelty Detection , 1999, NIPS.
[19] Yongming Han,et al. An optimized long short-term memory network based fault diagnosis model for chemical processes , 2020 .
[20] Kay Chen Tan,et al. Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics , 2017, IEEE Transactions on Neural Networks and Learning Systems.
[21] Huanxin Chen,et al. Ensemble 1-D CNN diagnosis model for VRF system refrigerant charge faults under heating condition , 2020 .
[22] Bo Jin,et al. Sequential Fault Diagnosis Based on LSTM Neural Network , 2018, IEEE Access.
[23] Jianfeng Zhao,et al. Speech emotion recognition using deep 1D & 2D CNN LSTM networks , 2019, Biomed. Signal Process. Control..
[24] Sirish L. Shah,et al. Fault detection and diagnosis in process data using one-class support vector machines , 2009 .
[25] Chen Meng,et al. Data-Driven Feature Extraction for Analog Circuit Fault Diagnosis Using 1-D Convolutional Neural Network , 2020, IEEE Access.
[26] Xiang Li,et al. Remaining useful life estimation in prognostics using deep convolution neural networks , 2018, Reliab. Eng. Syst. Saf..
[27] Gerald Penn,et al. Applying Convolutional Neural Networks concepts to hybrid NN-HMM model for speech recognition , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[28] Jizhong Tao,et al. A Novel Bearing Health Indicator Construction Method Based on Ensemble Stacked Autoencoder , 2019, 2019 IEEE International Conference on Prognostics and Health Management (ICPHM).
[29] Piergiuseppe Di Marco,et al. Fault Detection and Diagnosis Using Combined Autoencoder and Long Short-Term Memory Network , 2019, Sensors.
[30] Weihua Gui,et al. A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network. , 2019, ISA transactions.
[31] Fanming Meng,et al. An Improved Bearing Fault Diagnosis Method using One-Dimensional CNN and LSTM , 2018, Strojniški vestnik - Journal of Mechanical Engineering.
[32] Jun Wu,et al. Machine Health Monitoring Using Adaptive Kernel Spectral Clustering and Deep Long Short-Term Memory Recurrent Neural Networks , 2019, IEEE Transactions on Industrial Informatics.
[33] Taoying Li,et al. A Hybrid CNN-LSTM Model for Forecasting Particulate Matter (PM2.5) , 2020, IEEE Access.
[34] Heikki N. Koivo,et al. Application of artificial neural networks in process fault diagnosis , 1991, Autom..
[35] Mohammad Modarres,et al. A Deep Adversarial Approach Based on Multi-Sensor Fusion for Semi-Supervised Remaining Useful Life Prognostics , 2019, Sensors.
[36] Faisal Khan,et al. Process Fault Prognosis Using Hidden Markov Model–Bayesian Networks Hybrid Model , 2019, Industrial & Engineering Chemistry Research.
[37] M. A. Djeziri,et al. Data-driven approach augmented in simulation for robust fault prognosis , 2019, Eng. Appl. Artif. Intell..
[38] Noureddine Zerhouni,et al. State of the art and taxonomy of prognostics approaches, trends of prognostics applications and open issues towards maturity at different technology readiness levels , 2017 .