Generalizing Fault Detection Against Domain Shifts Using Stratification-Aware Cross-Validation

• URL: http://arxiv.org/abs/2008.08713v1
• Date: Thu, 20 Aug 2020 00:03:09 GMT
• Title: Generalizing Fault Detection Against Domain Shifts Using Stratification-Aware Cross-Validation
• Authors: Yingshui Tan, Baihong Jin, Qiushi Cui, Xiangyu Yue, Alberto Sangiovanni Vincentelli
• Abstract summary: Incipient anomalies present milder symptoms compared to severe ones. These anomalies can be easily mistaken as normal operating conditions. We show that ensemble learning methods can give improved performance on incipient anomalies.
• Score: 4.731408120697983
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Incipient anomalies present milder symptoms compared to severe ones, and are more difficult to detect and diagnose due to their close resemblance to normal operating conditions. The lack of incipient anomaly examples in the training data can pose severe risks to anomaly detection methods that are built upon Machine Learning (ML) techniques, because these anomalies can be easily mistaken as normal operating conditions. To address this challenge, we propose to utilize the uncertainty information available from ensemble learning to identify potential misclassified incipient anomalies. We show in this paper that ensemble learning methods can give improved performance on incipient anomalies and identify common pitfalls in these models through extensive experiments on two real-world datasets. Then, we discuss how to design more effective ensemble models for detecting incipient anomalies.

Related papers

• Anomaly Detection by Context Contrasting [57.695202846009714]
  Anomaly Detection focuses on identifying samples that deviate from the norm. Recent advances in self-supervised learning have shown great promise in this regard. We propose Con2, which addresses this problem by setting normal training data into distinct contexts. Our approach achieves state-of-the-art performance on various benchmarks while exhibiting superior performance in a more realistic healthcare setting.
  arXiv  Detail & Related papers  (2024-05-29T07:59:06Z)
• AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model [59.08735812631131]
  Anomaly inspection plays an important role in industrial manufacture. Existing anomaly inspection methods are limited in their performance due to insufficient anomaly data. We propose AnomalyDiffusion, a novel diffusion-based few-shot anomaly generation model.
  arXiv  Detail & Related papers  (2023-12-10T05:13:40Z)
• SaliencyCut: Augmenting Plausible Anomalies for Anomaly Detection [24.43321988051129]
  We propose a novel saliency-guided data augmentation method, SaliencyCut, to produce pseudo but more common anomalies. We then design a novel patch-wise residual module in the anomaly learning head to extract and assess the fine-grained anomaly features from each sample.
  arXiv  Detail & Related papers  (2023-06-14T08:55:36Z)
• AGAD: Adversarial Generative Anomaly Detection [12.68966318231776]
  Anomaly detection suffered from the lack of anomalies due to the diversity of abnormalities and the difficulties of obtaining large-scale anomaly data. We propose Adversarial Generative Anomaly Detection (AGAD), a self-contrast-based anomaly detection paradigm. Our method generates pseudo-anomaly data for both supervised and semi-supervised anomaly detection scenarios.
  arXiv  Detail & Related papers  (2023-04-09T10:40:02Z)
• Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection [90.32910087103744]
  A few labeled anomaly examples are often available in many real-world applications. These anomaly examples provide valuable knowledge about the application-specific abnormality. Those anomalies seen during training often do not illustrate every possible class of anomaly. This paper tackles open-set supervised anomaly detection.
  arXiv  Detail & Related papers  (2022-03-28T05:21:37Z)
• Explainable Deep Few-shot Anomaly Detection with Deviation Networks [123.46611927225963]
  We introduce a novel weakly-supervised anomaly detection framework to train detection models. The proposed approach learns discriminative normality by leveraging the labeled anomalies and a prior probability. Our model is substantially more sample-efficient and robust, and performs significantly better than state-of-the-art competing methods in both closed-set and open-set settings.
  arXiv  Detail & Related papers  (2021-08-01T14:33:17Z)
• Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data [150.9270911031327]
  We consider the problem of anomaly detection with a small set of partially labeled anomaly examples and a large-scale unlabeled dataset. Existing related methods either exclusively fit the limited anomaly examples that typically do not span the entire set of anomalies, or proceed with unsupervised learning from the unlabeled data. We propose here instead a deep reinforcement learning-based approach that enables an end-to-end optimization of the detection of both labeled and unlabeled anomalies.
  arXiv  Detail & Related papers  (2020-09-15T03:05:39Z)
• Using Ensemble Classifiers to Detect Incipient Anomalies [12.947364178385637]
  Incipient anomalies present milder symptoms compared to severe ones. These anomalies can be easily mistaken as normal operating conditions. We show that ensemble learning methods can give improved performance on incipient anomalies.
  arXiv  Detail & Related papers  (2020-08-20T00:00:39Z)
• Are Ensemble Classifiers Powerful Enough for the Detection and Diagnosis of Intermediate-Severity Faults? [9.1591191545173]
  Intermediate-Severity (IS) faults present milder symptoms compared to severe faults. The lack of IS fault examples in the training data can pose severe risks to Fault Detection and Diagnosis (FDD) methods. We discuss how to design more effective ensemble models for detecting and diagnosing IS faults.
  arXiv  Detail & Related papers  (2020-07-07T02:05:04Z)
• Deep Weakly-supervised Anomaly Detection [118.55172352231381]
  Pairwise Relation prediction Network (PReNet) learns pairwise relation features and anomaly scores. PReNet can detect any seen/unseen abnormalities that fit the learned pairwise abnormal patterns. Empirical results on 12 real-world datasets show that PReNet significantly outperforms nine competing methods in detecting seen and unseen anomalies.
  arXiv  Detail & Related papers  (2019-10-30T00:40:25Z)

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.