Data-Based Techniques Focused on Modern Industry: An Overview

By Shen Yin, Xianwei Li, Huijun Gao, Okyay Kaynak. IEEE transactions on industrial electronics (1982. Print), 2015

954 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/8a75592eb6b1af99dc69dda4d608ad17ea47fcf4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Peng Shi
2. Xiaosong Hu
3. Lixian Zhang
4. Xuehai Wang
5. Ming Liu
6. Jinjiang Wang
7. Kezhi Mao
8. Feng Ding

Citations in the corpus

• Deep Model Based Domain Adaptation for Fault Diagnosis (2017, methodology): “pattern information and data-driven methods to detect faults [11]–[14].”
• Motor Bearing Fault Detection Using Spectral Kurtosis-Based Feature Extraction Coupled With K-Nearest Neighbor Distance Analysis (2016, methodology): “An overview of PCA is described in [41].”
• Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming (2019, background): “Torrents of data are routinely collected and archived in process industries, and these data are becoming an increasingly important asset in process control, operations and design [12-14].”
• 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].”
• Battery Health Prognosis for Electric Vehicles Using Sample Entropy and Sparse Bayesian Predictive Modeling (2016, background): “Meanwhile, these challenges constitute a major incentive to take advantage of advanced diagnosis and prognosis methodologies [15]–[17].”
• Quo vadis artificial intelligence? (2022, background): “Third, the rise of AI is associated with a significant increase in computing power [42, 43].”
• A Review of Architectures and Concepts for Intelligence in Future Electric Energy Systems (2015, background): “fault-tolerant system approaches are reviewed in recent papers [84], [85].”
• Big Data for Modern Industry: Challenges and Trends [Point of View] (2015, neutral): “[17] S.”
• Fault-Tolerant Sliding-Mode-Observer Synthesis of Markovian Jump Systems Using Quantized Measurements (2015, methodology): “Since external disturbances and unknown faults inevitably cause performance degradation for observer synthesis in a variety of industrial processes, disturbances and/or fault-tolerant observer design have thus received a great amount of attention, and a number of design techniques have been reported”
• Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks (2017, background): “During recent years, machine monitoring systems, including diagnosis and prognosis approaches, have been actively researched [1–4].”
• Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks (2018, methodology): “In KNN, we search the best neighbor from [3, 5, 7, 9].”