Sensor fault diagnosis based on on-line random forests

Li, Yang; Zhang, Jia; Deng, Fang; Chen, Jie

doi:10.1109/chicc.2016.7553992

Cited by 5 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method is applied to motor faults diagnosis and it gives a good accuracy of faults classification. In [31], they propose to use the on-line random forests (ORF) algorithm to identify sensor fault. The sample set is derived from Tennessee Eastman process.…”

Section: Decision Treesmentioning

confidence: 99%

Review of Machine Learning Approaches In Fault Diagnosis applied to IoT Systems

Flaus

Adrot

2019

2019 International Conference on Control, Automation and Diagnosis (ICCAD)

View full text Add to dashboard Cite

With increasing complex systems, low production costs, and changing technologies, for this reason, the automatic fault diagnosis using artificial intelligence (AI) techniques is more in more applied. In addition, with the emergence of the use of reconfigurable systems, AI can assist in self-maintenance of complex systems. The purpose of this article is to summarize the diagnosis research of systems using AI approaches and examine their application particularly in the field of diagnosis of complex systems. It covers articles published from 2002 to 2018 using Machine Learning tools for fault diagnosis in industrial systems.

show abstract

Section: Decision Treesmentioning

confidence: 99%

Review of Machine Learning Approaches In Fault Diagnosis applied to IoT Systems

Flaus

Adrot

2019

2019 International Conference on Control, Automation and Diagnosis (ICCAD)

View full text Add to dashboard Cite

show abstract

“…in heavy industry for analysis of large amounts of production data. The work [22] is devoted to the use of the Random Forest algorithm in sensor failures diagnostics. The algorithm is used to reduce memory requirements in the process of assessing the state of the equipment in real time.…”

Section: Introductionmentioning

confidence: 99%

Development of Smart Technology for Complex Objects Prediction and Control on the Basis of a Distributed Control System and an Artificial Immune Systems Approach

Pschegusova¹,

Samigulina²

2019

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

This paper is an extension of work originally presented in 2018 Global Smart Industry Conference (GloSIC). Researches are devoted to the development of Smart technology for complex objects control and prediction on the basis of a distributed Honeywell DCS control system of the TengizChevroil enterprise using the example of a technological process of medium pressure gas cleaning. The article describes how on the basis of the multialgorithm approach there was developed a modified algorithm based on modern artificial intelligence methods in order to select informative features (principal component method, Random Forest algorithm, particle swarm algorithm) and artificial immune systems (clonal selection) solving the image recognition problem and predicting the state of a complex control object. There was conducted a comparative analysis of the simulation results using the example of real production data (daily data of sensors from an average pressure absorber).

show abstract

A Method for Predicting the State of Sensors and Meters Based on Improved Generative Adversarial Networks

Wang

2022

2022 3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE)

View full text Add to dashboard Cite

Sensor fault diagnosis based on on-line random forests

Cited by 5 publications

References 9 publications

Review of Machine Learning Approaches In Fault Diagnosis applied to IoT Systems

Review of Machine Learning Approaches In Fault Diagnosis applied to IoT Systems

Development of Smart Technology for Complex Objects Prediction and Control on the Basis of a Distributed Control System and an Artificial Immune Systems Approach

A Method for Predicting the State of Sensors and Meters Based on Improved Generative Adversarial Networks

Contact Info

Product

Resources

About