A Review on Basic Data-Driven Approaches for Industrial Process Monitoring

By Shen Yin, Steven X. Ding, Xiaochen Xie, Hao Luo. IEEE transactions on industrial electronics (1982. Print), 2014

1,539 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/5347f160db99e3c29d0854caad48fbaf8444266c

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In Control and Systems Engineering

In Mechanical Engineering

Citations in the corpus

Unknown Input Observer-Based Robust Fault Estimation for Systems Corrupted by Partially Decoupled Disturbances (2016, background): “During the last four decades, extensive results were reported on various analytical redundancy based fault diagnosis techniques [1-4] and their applications in aero systems, electro-mechanical systems, energy systems [5-8] and so forth.”
Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series (2018, background): “Due to their simplicity and ability to handle large number of process data, unsupervised learning methods have been wildly used for anomaly detection for various industrial processes [23].”
A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches (2015, methodology): “In [14]–[16], comprehensive fault diagnostic methods were reviewed respectively from the datadriven perspective.”
Intelligent Particle Filter and Its Application to Fault Detection of Nonlinear System (2015, background): “In the past decades, many fault detection methods have been proposed [35]–[37].”
Convergence of the recursive identification algorithms for multivariate pseudo‐linear regressive systems (2016, background): “dedicated to review the recent development of the data-driven approaches from the application point of view [15, 16].”
An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data (2016, methodology): “The comparisons are displayed in Table I. Du et al. [41] proposed a method based on wavelet leaders multifractal features and SVM.”
A Review of Fault Detection and Diagnosis for the Traction System in High-Speed Trains (2020, methodology): “Typically, principal component analysis (PCA), partial least squares (PLS), and canonical correlation analysis (CCA) are the fundamental methods in feature extraction [42], [51], [57],”
A transfer convolutional neural network for fault diagnosis based on ResNet-50 (2019, background): “As a typical kind of fault diagnosis, data-driven fault diagnosis can establish the fault modes using historic data without any prior explicit models or signal symptoms, which is very suitable for the complex systems [2].”