Deep learning technique for process fault detection and diagnosis in the presence of incomplete data

Guo, Changjie; Hu, Wenkai; Yang, Fan; Huang, Dexian

doi:10.1016/j.cjche.2020.06.015

Cited by 18 publications

(1 citation statement)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the system fault diagnosis and performance analysis, a fault tree analysis method is proposed by SLi et al Eliminate uncertainty using expert opinion, combining field data based on Dempster-Shafer theory and rough set theory [10]. Guo C et al proposed a new FDD method for process fault identification based on incomplete data interpolation techniques, with an improved stacked autoencoder in the incomplete data processing stage [11]. Jiang W et al, carried out a research on CNC machine tool health monitoring system based on artificial intelligence [12].…”

Section: Introductionmentioning

confidence: 99%

Research and application of digital technology of film coating equipment

Liu¹,

Yang²,

Zhu³

2023

FCIS

View full text Add to dashboard Cite

At present, most of the coating production still adopts relatively backward traditional methods, there are its equipment status, operation, production, repair and maintenance information of digital technology application less problems. In view of this problem, a data acquisition module and data processing system are developed to realize the data acquisition and real-time monitoring of the equipment temperature, vacuum degree, rotation speed, target material, film thickness and surface condition. The data acquisition module consists of a self-organized network system formed by the sensor nodes deployed in the detection area and installed on the device, combined with / wireless communication. Through the development of the system to achieve the digital and intelligent application of coating equipment.

show abstract

Section: Introductionmentioning

confidence: 99%

Research and application of digital technology of film coating equipment

Liu¹,

Yang²,

Zhu³

2023

FCIS

View full text Add to dashboard Cite

show abstract

Generalized transformer in fault diagnosis of Tennessee Eastman process

Zhang

Song

Zhang

et al. 2022

Neural Comput & Applic

View full text Add to dashboard Cite

Small data challenges for intelligent prognostics and health management: a review

Li,

Feng

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

Prognostics and health management (PHM) is critical for enhancing equipment reliability and reducing maintenance costs, and research on intelligent PHM has made significant progress driven by big data and deep learning techniques in recent years. However, complex working conditions and high-cost data collection inherent in real-world scenarios pose small-data challenges for the application of these methods. Given the urgent need for data-efficient PHM techniques in academia and industry, this paper aims to explore the fundamental concepts, ongoing research, and future trajectories of small data challenges in the PHM domain. This survey first elucidates the definition, causes, and impacts of small data on PHM tasks, and then analyzes the current mainstream approaches to solving small data problems, including data augmentation, transfer learning, and few-shot learning techniques, each of which has its advantages and disadvantages. In addition, this survey summarizes benchmark datasets and experimental paradigms to facilitate fair evaluations of diverse methodologies under small data conditions. Finally, some promising directions are pointed out to inspire future research.

show abstract

Deep learning technique for process fault detection and diagnosis in the presence of incomplete data

Cited by 18 publications

References 32 publications

Research and application of digital technology of film coating equipment

Research and application of digital technology of film coating equipment

Generalized transformer in fault diagnosis of Tennessee Eastman process

Small data challenges for intelligent prognostics and health management: a review

Contact Info

Product

Resources

About