Automatic fault detection and diagnosis in refrigeration systems, A data-driven approach

Zahra Soltani

doi:10.54337/aau535542944

Automatic fault detection and diagnosis in refrigeration systems, A data-driven approach

Zahra Soltani

Research output: PhD thesis

122 Downloads (Pure)

Original language	English
Supervisors	Leth, John-Josef, Principal supervisor
External collaborators	BITZER Electronics A/S
Publisher	Aalborg Universitetsforlag
Electronic ISBNs	978-87-7573-742-0
DOIs	https://doi.org/10.54337/aau535542944
Publication status	Published - 2023

Bibliographical note

PhD supervisor:
Associate Professor John-Josef Leth, Aalborg University

Keywords

machine learning
fault detection
classification
refrigeration
SVM
LDA
PCA
CNN
data-driven

Access to Document

10.54337/aau535542944

PHD_ZSFinal published version, 14.8 MB

AUB Link

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2 Article in proceeding
1 Journal article

Fault detection and diagnosis in refrigeration systems using machine learning algorithms
Soltani, Z., Sørensen, K. K., Leth, J. & Bendtsen, J. D., Dec 2022, In: International Journal of Refrigeration. 144, p. 34-45 12 p.
Research output: Contribution to journal › Journal article › Research › peer-review

Open Access
File
15 Citations (Scopus)

201 Downloads (Pure)
Robustness analysis of PCA-SVM model used for fault detection in supermarket refrigeration systems
Soltani, Z., Kjaer Soerensen, K., Leth, J-J. & Bendtsen, J. D., 2021, 2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE). Kuala Lumpur, Malaysia: IEEE, 6 p.
Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

Open Access
File
2 Citations (Scopus)

77 Downloads (Pure)
Fault Detection of Supermarket Refrigeration Systems Using Convolutional Neural Network
Soltani, Z., Soerensen, K. K., Leth, J. & Bendtsen, J. D., 18 Oct 2020, IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society. IEEE Computer Society Press, p. 231-238 8 p. 9254485. (Proceedings of the Annual Conference of the IEEE Industrial Electronics Society).
Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

Open Access
File
3 Citations (Scopus)

113 Downloads (Pure)

Cite this

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title = "Automatic fault detection and diagnosis in refrigeration systems, A data-driven approach",

keywords = "machine learning, fault detection, classification, refrigeration, SVM, LDA, PCA, CNN, data-driven",

author = "Zahra Soltani",

note = "PhD supervisor: Associate Professor John-Josef Leth, Aalborg University",

year = "2023",

doi = "10.54337/aau535542944",

language = "English",

series = "Ph.d.-serien for Det Tekniske Fakultet for IT og Design, Aalborg Universitet",

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AU - Soltani, Zahra

N1 - PhD supervisor: Associate Professor John-Josef Leth, Aalborg University

PY - 2023

Y1 - 2023

KW - machine learning

KW - fault detection

KW - classification

KW - refrigeration

KW - SVM

KW - LDA

KW - PCA

KW - CNN

KW - data-driven

U2 - 10.54337/aau535542944

DO - 10.54337/aau535542944

M3 - PhD thesis

T3 - Ph.d.-serien for Det Tekniske Fakultet for IT og Design, Aalborg Universitet

PB - Aalborg Universitetsforlag

ER -

Automatic fault detection and diagnosis in refrigeration systems, A data-driven approach

Bibliographical note

Keywords

Access to Document

AUB Link

Research output

Fault detection and diagnosis in refrigeration systems using machine learning algorithms

Robustness analysis of PCA-SVM model used for fault detection in supermarket refrigeration systems

Fault Detection of Supermarket Refrigeration Systems Using Convolutional Neural Network

Cite this