SensorSCAN: Self-supervised learning and deep clustering for fault diagnosis in chemical processes

Long–short-term memory encoder–decoder with regularized hidden dynamics for fault detection in industrial processes

(2023) Yingxiang Liu et al.   JOURNAL OF PROCESS CONTROL

A convolutional neural network method based on Adam optimizer with power-exponential learning rate for bearing fault diagnosis

(2022) Youming Wang et al.   Journal of Vibroengineering

One step forward for smart chemical process fault detection and diagnosis

(2022) Xiaotian Bi et al.   COMPUTERS & CHEMICAL ENGINEERING

High-fidelity positive-unlabeled deep learning for semi-supervised fault detection of chemical processes

(2022) Shaodong Zheng et al.   PROCESS SAFETY AND ENVIRONMENTAL PROTECTION

An Extended Tennessee Eastman Simulation Dataset for Fault-Detection and Decision Support Systems

(2021) Christopher Reinartz et al.   COMPUTERS & CHEMICAL ENGINEERING

A Review on Data‐Driven Learning Approaches for Fault Detection and Diagnosis in Chemical Processes

(2021) Syed Ali Ammar Taqvi et al.   ChemBioEng Reviews

Deep unLSTM network: Features with memory information extracted from unlabeled data and their application on industrial unsupervised industrial fault detection

(2021) Jianbo Yu et al.   APPLIED SOFT COMPUTING

Towards robust and understandable fault detection and diagnosis using denoising sparse autoencoder and smooth integrated gradients

(2021) Peng Peng et al.   ISA TRANSACTIONS

A novel orthogonal self-attentive variational autoencoder method for interpretable chemical process fault detection and identification

(2021) Xiaotian Bi et al.   PROCESS SAFETY AND ENVIRONMENTAL PROTECTION

A new unsupervised data mining method based on the stacked autoencoder for chemical process fault diagnosis

(2020) Shaodong Zheng et al.   COMPUTERS & CHEMICAL ENGINEERING

An unsupervised fault diagnosis method for rolling bearing using STFT and generative neural networks

(2020) Hongfeng Tao et al.   JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS

Applications of machine learning to machine fault diagnosis: A review and roadmap

(2020) Yaguo Lei et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

An optimized long short-term memory network based fault diagnosis model for chemical processes

(2020) Yongming Han et al.   JOURNAL OF PROCESS CONTROL

Multiscale intelligent fault detection system based on agglomerative hierarchical clustering using stacked denoising autoencoder with temporal information

(2020) Jianbo Yu et al.   APPLIED SOFT COMPUTING

Fault diagnosis for distillation process based on CNN-DAE

(2019) Chuankun Li et al.   CHINESE JOURNAL OF CHEMICAL ENGINEERING

Using Labeled Autoencoder to Supervise Neural Network Combined with k-Nearest Neighbor for Visual Industrial Process Monitoring

(2019) Shifu Yan et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network

(2019) Yalin Wang et al.   ISA TRANSACTIONS

Design teacher and supervised dual stacked auto-encoders for quality-relevant fault detection in industrial process

(2019) Shifu Yan et al.   APPLIED SOFT COMPUTING

A novel process monitoring approach based on variational recurrent autoencoder

(2019) Feifan Cheng et al.   COMPUTERS & CHEMICAL ENGINEERING

Fault Detection and Diagnosis Using Combined Autoencoder and Long Short-Term Memory Network

(2019) Pangun Park et al.   SENSORS

Fault detection and diagnosis in process system using artificial intelligence-based cognitive technique

(2019) Rajeevan Arunthavanathan et al.   COMPUTERS & CHEMICAL ENGINEERING

Deep convolutional neural network model based chemical process fault diagnosis

(2018) Hao Wu et al.   COMPUTERS & CHEMICAL ENGINEERING

Fault Detection for Non-Gaussian Processes Using Generalized Canonical Correlation Analysis and Randomized Algorithms

(2018) Zhiwen Chen et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders

(2018) Haidong Shao et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Discriminatively boosted image clustering with fully convolutional auto-encoders

(2018) Fengfu Li et al.   PATTERN RECOGNITION

Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks

(2018) Han Liu et al.   NEUROCOMPUTING

Canonical correlation analysis-based fault detection methods with application to alumina evaporation process

(2016) Zhiwen Chen et al.   CONTROL ENGINEERING PRACTICE

An improved SVM integrated GS-PCA fault diagnosis approach of Tennessee Eastman process

(2016) Xin Gao et al.   NEUROCOMPUTING

An extensive reference dataset for fault detection and identification in batch processes

(2015) Jan Van Impe et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

The development of an industrial-scale fed-batch fermentation simulation

(2015) Stephen Goldrick et al.   JOURNAL OF BIOTECHNOLOGY

Adaptive total PLS based quality-relevant process monitoring with application to the Tennessee Eastman process

(2015) Jie Dong et al.   NEUROCOMPUTING

Fault detection in the Tennessee Eastman benchmark process using dynamic principal components analysis based on decorrelated residuals (DPCA-DR)

(2013) Tiago J. Rato et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

A support vector clustering-based probabilistic method for unsupervised fault detection and classification of complex chemical processes using unlabeled data

(2012) Jie Yu   AICHE JOURNAL

Fault diagnosis of Tennessee Eastman process with multi-scale PCA and ANFIS

(2012) C.K. Lau et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

A new integrated on-line fuzzy clustering and segmentation methodology with adaptive PCA approach for process monitoring and fault detection and diagnosis

(2012) Hesam Komari Alaei et al.   SOFT COMPUTING

Weighted and constrained possibilistic C-means clustering for online fault detection and isolation

(2010) Soheil Bahrampour et al.   APPLIED INTELLIGENCE

Locally Consistent Concept Factorization for Document Clustering

(2010) Deng Cai et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING