Multi-Source Domain Adaptation for Cross-Domain Fault Diagnosis of
Chemical Processes
- URL: http://arxiv.org/abs/2308.11247v1
- Date: Tue, 22 Aug 2023 07:43:59 GMT
- Title: Multi-Source Domain Adaptation for Cross-Domain Fault Diagnosis of
Chemical Processes
- Authors: Eduardo Fernandes Montesuma, Michela Mulas, Fred Ngol\`e Mboula,
Francesco Corona, Antoine Souloumiac
- Abstract summary: We provide an extensive comparison of single and multi-source unsupervised domain adaptation algorithms for Cross-Domain Fault Diagnosis (CDFD)
We show that using multiple domains during training has a positive effect, even when no adaptation is employed.
In addition, under the multiple-sources scenario, we improve classification accuracy of the no adaptation setting by 8.4% on average.
- Score: 5.119371135458389
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Fault diagnosis is an essential component in process supervision. Indeed, it
determines which kind of fault has occurred, given that it has been previously
detected, allowing for appropriate intervention. Automatic fault diagnosis
systems use machine learning for predicting the fault type from sensor
readings. Nonetheless, these models are sensible to changes in the data
distributions, which may be caused by changes in the monitored process, such as
changes in the mode of operation. This scenario is known as Cross-Domain Fault
Diagnosis (CDFD). We provide an extensive comparison of single and multi-source
unsupervised domain adaptation (SSDA and MSDA respectively) algorithms for
CDFD. We study these methods in the context of the Tennessee-Eastmann Process,
a widely used benchmark in the chemical industry. We show that using multiple
domains during training has a positive effect, even when no adaptation is
employed. As such, the MSDA baseline improves over the SSDA baseline
classification accuracy by 23% on average. In addition, under the
multiple-sources scenario, we improve classification accuracy of the no
adaptation setting by 8.4% on average.
Related papers
- Multi-Source and Test-Time Domain Adaptation on Multivariate Signals using Spatio-Temporal Monge Alignment [59.75420353684495]
Machine learning applications on signals such as computer vision or biomedical data often face challenges due to the variability that exists across hardware devices or session recordings.
In this work, we propose Spatio-Temporal Monge Alignment (STMA) to mitigate these variabilities.
We show that STMA leads to significant and consistent performance gains between datasets acquired with very different settings.
arXiv Detail & Related papers (2024-07-19T13:33:38Z) - Multi-source-free Domain Adaptation via Uncertainty-aware Adaptive
Distillation [8.791916654073088]
Source-free domain adaptation (SFDA) alleviates the domain discrepancy among data obtained from domains without accessing the data for the awareness of data privacy.
We propose Uncertainty-aware Adaptive Distillation (UAD) for the multi-source-free unsupervised domain adaptation setting.
arXiv Detail & Related papers (2024-02-09T06:48:04Z) - Calibrated Adaptive Teacher for Domain Adaptive Intelligent Fault
Diagnosis [7.88657961743755]
Unsupervised domain adaptation (UDA) deals with the scenario where labeled data are available in a source domain, and only unlabeled data are available in a target domain.
We propose a novel UDA method called Calibrated Adaptive Teacher (CAT), where we propose to calibrate the predictions of the teacher network throughout the self-training process.
arXiv Detail & Related papers (2023-12-05T15:19:29Z) - A Sparse Bayesian Learning for Diagnosis of Nonstationary and Spatially
Correlated Faults with Application to Multistation Assembly Systems [3.4991031406102238]
This article proposes a novel fault diagnosis method: clustering spatially correlated sparse Bayesian learning (CSSBL)
The proposed method's efficacy is provided through numerical and real-world case studies utilizing an actual autobody assembly system.
The generalizability of the proposed method allows the technique to be applied in fault diagnosis in other domains, including communication and healthcare systems.
arXiv Detail & Related papers (2023-10-20T23:56:53Z) - Better Practices for Domain Adaptation [62.70267990659201]
Domain adaptation (DA) aims to provide frameworks for adapting models to deployment data without using labels.
Unclear validation protocol for DA has led to bad practices in the literature.
We show challenges across all three branches of domain adaptation methodology.
arXiv Detail & Related papers (2023-09-07T17:44:18Z) - An Evidential Real-Time Multi-Mode Fault Diagnosis Approach Based on
Broad Learning System [26.733033919978364]
We propose a novel approach to achieve real-time multi-mode fault diagnosis in industrial systems.
Our approach uses an extended evidence reasoning (ER) algorithm to fuse information and merge outputs from different base classifiers.
The effectiveness of the proposed approach is demonstrated on the multi-mode Tennessee Eastman process dataset.
arXiv Detail & Related papers (2023-04-29T04:42:44Z) - Back to the Source: Diffusion-Driven Test-Time Adaptation [77.4229736436935]
Test-time adaptation harnesses test inputs to improve accuracy of a model trained on source data when tested on shifted target data.
We instead update the target data, by projecting all test inputs toward the source domain with a generative diffusion model.
arXiv Detail & Related papers (2022-07-07T17:14:10Z) - Robust Calibration with Multi-domain Temperature Scaling [86.07299013396059]
We develop a systematic calibration model to handle distribution shifts by leveraging data from multiple domains.
Our proposed method -- multi-domain temperature scaling -- uses the robustness in the domains to improve calibration under distribution shift.
arXiv Detail & Related papers (2022-06-06T17:32:12Z) - Instance Level Affinity-Based Transfer for Unsupervised Domain
Adaptation [74.71931918541748]
We propose an instance affinity based criterion for source to target transfer during adaptation, called ILA-DA.
We first propose a reliable and efficient method to extract similar and dissimilar samples across source and target, and utilize a multi-sample contrastive loss to drive the domain alignment process.
We verify the effectiveness of ILA-DA by observing consistent improvements in accuracy over popular domain adaptation approaches on a variety of benchmark datasets.
arXiv Detail & Related papers (2021-04-03T01:33:14Z) - Adaptive Risk Minimization: Learning to Adapt to Domain Shift [109.87561509436016]
A fundamental assumption of most machine learning algorithms is that the training and test data are drawn from the same underlying distribution.
In this work, we consider the problem setting of domain generalization, where the training data are structured into domains and there may be multiple test time shifts.
We introduce the framework of adaptive risk minimization (ARM), in which models are directly optimized for effective adaptation to shift by learning to adapt on the training domains.
arXiv Detail & Related papers (2020-07-06T17:59:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.