DeepFunction: Deep Metric Learning-based Imbalanced Classification for Diagnosing Threaded Pipe Connection Defects using Functional Data

• URL: http://arxiv.org/abs/2404.03329v2
• Date: Wed, 24 Apr 2024 12:39:30 GMT
• Title: DeepFunction: Deep Metric Learning-based Imbalanced Classification for Diagnosing Threaded Pipe Connection Defects using Functional Data
• Authors: Yukun Xie, Juan Du, Chen Zhang,
• Abstract summary: In modern manufacturing, most of the product lines are conforming. Few products are nonconforming but with different defect types. The identification of defect types can help further root cause diagnosis of production lines. We propose an innovative classification framework based on deep metric learning using functional data (DeepFunction)
• Score: 6.688305507010403
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In modern manufacturing, most of the product lines are conforming. Few products are nonconforming but with different defect types. The identification of defect types can help further root cause diagnosis of production lines. With the sensing development, signals of process variables can be collected in high resolution, which can be regarded as multichannel functional data. They have abundant information to characterize the process and help identify the defect types. Motivated by a real example from the pipe tightening process, we focus on defect classification where each sample is a multichannel functional data. However, the available samples for each defect type are limited and imbalanced. Moreover, the functions are incomplete since the pre-tightening process before the pipe tightening process is unobserved. To classify the defect samples based on imbalanced, multichannel, and incomplete functional data is very important but challenging. Thus, we propose an innovative classification framework based on deep metric learning using functional data (DeepFunction). The framework leverages the power of deep metric learning to train on imbalanced datasets. A neural network specially crafted for processing functional data is also proposed to handle multichannel and incomplete functional data. The results from a real-world case study demonstrate the superior accuracy of our framework when compared to existing benchmarks.

Related papers

• SINDER: Repairing the Singular Defects of DINOv2 [61.98878352956125]
  Vision Transformer models trained on large-scale datasets often exhibit artifacts in the patch token they extract. We propose a novel fine-tuning smooth regularization that rectifies structural deficiencies using only a small dataset.
  arXiv  Detail & Related papers  (2024-07-23T20:34:23Z)
• ITI-IQA: a Toolbox for Heterogeneous Univariate and Multivariate Missing Data Imputation Quality Assessment [0.0]
  ITI-IQA is a set of utilities designed to assess the reliability of various imputation methods. The toolbox also includes a suite of diagnosing methods and graphical tools to check measurements.
  arXiv  Detail & Related papers  (2024-07-16T14:26:46Z)
• Incomplete Multimodal Industrial Anomaly Detection via Cross-Modal Distillation [0.0]
  multimodal industrial anomaly detection (IAD) based on 3D point clouds and RGB images remains a work in progress. Existing quality control processes combine rapid in-line inspections, such as optical and infrared imaging with high-resolution but time-consuming near-line characterization techniques. We propose CMDIAD, a Cross-Modal Distillation framework for IAD to demonstrate the feasibility of a Multi-modal Training, Few-modal Inference pipeline.
  arXiv  Detail & Related papers  (2024-05-22T12:08:56Z)
• Continual learning for surface defect segmentation by subnetwork creation and selection [55.2480439325792]
  We introduce a new continual (or lifelong) learning algorithm that performs segmentation tasks without undergoing catastrophic forgetting. The method is applied to two different surface defect segmentation problems that are learned incrementally. Our approach shows comparable results with joint training when all the training data (all defects) are seen simultaneously.
  arXiv  Detail & Related papers  (2023-12-08T15:28:50Z)
• CINFormer: Transformer network with multi-stage CNN feature injection for surface defect segmentation [73.02218479926469]
  We propose a transformer network with multi-stage CNN feature injection for surface defect segmentation. CINFormer presents a simple yet effective feature integration mechanism that injects the multi-level CNN features of the input image into different stages of the transformer network in the encoder. In addition, CINFormer presents a Top-K self-attention module to focus on tokens with more important information about the defects.
  arXiv  Detail & Related papers  (2023-09-22T06:12:02Z)
• Interactive System-wise Anomaly Detection [66.3766756452743]
  Anomaly detection plays a fundamental role in various applications. It is challenging for existing methods to handle the scenarios where the instances are systems whose characteristics are not readily observed as data. We develop an end-to-end approach which includes an encoder-decoder module that learns system embeddings.
  arXiv  Detail & Related papers  (2023-04-21T02:20:24Z)
• An Order-Invariant and Interpretable Hierarchical Dilated Convolution Neural Network for Chemical Fault Detection and Diagnosis [7.226239130399725]
  Convolution neural network (CNN) is a popular deep learning algorithm with many successful applications in chemical fault detection and diagnosis tasks. In this paper, we propose an order-invariant and interpretable hierarchical dilated convolution neural network (HDLCNN) The proposed method provides interpretability by including the SHAP values to quantify feature contribution.
  arXiv  Detail & Related papers  (2023-02-13T10:28:41Z)
• Graph Neural Networks with Trainable Adjacency Matrices for Fault Diagnosis on Multivariate Sensor Data [69.25738064847175]
  It is necessary to consider the behavior of the signals in each sensor separately, to take into account their correlation and hidden relationships with each other. The graph nodes can be represented as data from the different sensors, and the edges can display the influence of these data on each other. It was proposed to construct a graph during the training of graph neural network. This allows to train models on data where the dependencies between the sensors are not known in advance.
  arXiv  Detail & Related papers  (2022-10-20T11:03:21Z)
• A Penalty Approach for Normalizing Feature Distributions to Build Confounder-Free Models [11.818509522227565]
  MetaData Normalization (MDN) estimates the linear relationship between the metadata and each feature based on a non-trainable closed-form solution. We extend the MDN method by applying a Penalty approach (referred to as PDMN) We show improvement in model accuracy and greater independence from confounders using PMDN over MDN in a synthetic experiment and a multi-label, multi-site dataset of magnetic resonance images (MRIs)
  arXiv  Detail & Related papers  (2022-07-11T04:02:12Z)
• TL-SDD: A Transfer Learning-Based Method for Surface Defect Detection with Few Samples [17.884998028369026]
  We propose TL-SDD: a novel Transfer Learning-based method for Surface Defect Detection. We adopt a two-phase training scheme to transfer the knowledge from common defect classes to rare defect classes. Compared to the baseline methods, the performance of our proposed method has improved by up to 11.98% for rare defect classes.
  arXiv  Detail & Related papers  (2021-08-16T07:24:00Z)
• Generative Partial Visual-Tactile Fused Object Clustering [81.17645983141773]
  We propose a Generative Partial Visual-Tactile Fused (i.e., GPVTF) framework for object clustering. A conditional cross-modal clustering generative adversarial network is then developed to synthesize one modality conditioning on the other modality. To the end, two pseudo-label based KL-divergence losses are employed to update the corresponding modality-specific encoders.
  arXiv  Detail & Related papers  (2020-12-28T02:37:03Z)

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.