Proceedings ArticleDOI
On The Application of Domain Adversarial Neural Network to Fault Detection and Isolation in Power Plants
Hossein Shahabadi Farahani,Alireza Fatehi,Mahdi Aliyari Shoorehdeli +2 more
- pp 1132-1138
TLDR
In this article, a new Fault Detection and Isolation (FDI) approach based on Transfer Learning (TL) was introduced for improving health monitoring systems of gas turbines under varying working conditions.Abstract:
In this paper, we introduce a new Fault Detection and Isolation (FDI) approach based on Transfer Learning (TL) for improving health monitoring systems of gas turbines under varying working conditions. Nowadays, researchers have found intelligent algorithms a reliable tool for condition monitoring of mechanical systems and processes. In this regard, modern automation systems in many industries, including power plants, are heavily utilizing machine learning algorithms. However, the performance of data-driven methods depends on the consistency of data distribution. Unfortunately, this assumption might not be satisfied with real-world problems. In this research, we contribute to finding a solution to this problem, which is a crucial barrier to many intelligent condition monitoring systems. We used domain adversarial training of neural networks to find models that can adapt to new working conditions of gas-turbines. Accordingly, a well-known gas-turbine simulator is employed to simulate the process behavior under various working conditions, and it is illustrated that even small variations in working conditions cause a dramatic decline in the performance of models. We demonstrate that the proposed TL-based FDI approach can be successfully employed to cope with the inconsistency of data distribution in process systems.read more
Citations
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Journal ArticleDOI
Domain Adversarial Neural Network Regression to design transferable soft sensor in a power plant
TL;DR: A new transfer learning (TL) based regression method, called Domain Adversarial Neural Network Regression (DANN-R), is proposed and employed for designing transferable soft sensors that can successfully adapt to new plants and new working conditions.
Journal ArticleDOI
Feature selection and feature learning in machine learning applications for gas turbines: A review
TL;DR: In this paper , a review on 46 studies that used feature selection and feature learning (FSFL) techniques for gas turbine (GT) modeling with ML is presented. And a new knowledge accumulation, extraction, and transfer concept is proposed to address GT modelling challenges.
Proceedings ArticleDOI
Unsupervised Domain Adaptation via Subspace Interpolating Deep Dictionary Learning: A Case Study in Machine Inspection
TL;DR: In this article , the authors proposed an unsupervised domain adaptation method where labeled data is available only in the source domain via subspace interpolation using deep dictionary learning, which can capture the domain shift and form a shared feature space for cross-domain analysis.
Proceedings ArticleDOI
CycleGAN Based Unsupervised Domain Adaptation for Machine Fault Diagnosis
Naibedya Pattnaik,Uday Sai Vemula,Kriti Kumar,Achanna Anil Kumar,Angshul Majumdar,M. Girish Chandra,Arpan Pal +6 more
TL;DR: In this article , a cycle-consistency loss employing 1D-CycleGAN for learning the source to target mapping for unsupervised adaptation for bearing fault diagnosis was proposed.
Journal ArticleDOI
Sensor Fault Detection and Isolation in Autonomous Nonlinear Systems Using Neural Network-Based Observers
TL;DR: In this paper , a learning-based Luenberger observer is proposed to detect and isolate faulty sensors in industrial systems, which is applicable to general autonomous nonlinear systems without making any assumptions about its triangular and/or normal form.
References
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Domain-adversarial training of neural networks
Yaroslav Ganin,Evgeniya Ustinova,Hana Ajakan,Pascal Germain,Hugo Larochelle,François Laviolette,Mario Marchand,Victor Lempitsky +7 more
TL;DR: In this article, a new representation learning approach for domain adaptation is proposed, in which data at training and test time come from similar but different distributions, and features that cannot discriminate between the training (source) and test (target) domains are used to promote the emergence of features that are discriminative for the main learning task on the source domain.
Proceedings Article
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Shai Ben-David,John Blitzer,Koby Crammer,Alex Kulesza,Fernando Pereira,Jennifer Wortman Vaughan +5 more
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