A Novel Semi-Supervised Data-Driven Method for Chiller Fault Diagnosis with Unlabeled Data

• URL: http://arxiv.org/abs/2011.00187v1
• Date: Sat, 31 Oct 2020 04:57:38 GMT
• Title: A Novel Semi-Supervised Data-Driven Method for Chiller Fault Diagnosis with Unlabeled Data
• Authors: Bingxu Li, Fanyong Cheng, Xin Zhang, Can Cui, Wenjian Cai
• Abstract summary: We propose a novel semi-supervised data-driven fault diagnosis method for chiller systems based on the semi-generative adversarial network. The proposed method can improve the diagnostic accuracy to 84%, while the supervised baseline methods only reach the accuracy of 65% at most.
• Score: 9.357969752339727
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In practical chiller systems, applying efficient fault diagnosis techniques can significantly reduce energy consumption and improve energy efficiency of buildings. The success of the existing methods for fault diagnosis of chillers relies on the condition that sufficient labeled data are available for training. However, label acquisition is laborious and costly in practice. Usually, the number of labeled data is limited and most data available are unlabeled. The existing methods cannot exploit the information contained in unlabeled data, which significantly limits the improvement of fault diagnosis performance in chiller systems. To make effective use of unlabeled data to further improve fault diagnosis performance and reduce the dependency on labeled data, we proposed a novel semi-supervised data-driven fault diagnosis method for chiller systems based on the semi-generative adversarial network, which incorporates both unlabeled and labeled data into learning process. The semi-generative adversarial network can learn the information of data distribution from unlabeled data and this information can help to significantly improve the diagnostic performance. Experimental results demonstrate the effectiveness of the proposed method. Under the scenario that there are only 80 labeled samples and 16000 unlabeled samples, the proposed method can improve the diagnostic accuracy to 84%, while the supervised baseline methods only reach the accuracy of 65% at most. Besides, the minimal required number of labeled samples can be reduced by about 60% with the proposed method when there are enough unlabeled samples.

Related papers

• Efficient Data-Sketches and Fine-Tuning for Early Detection of Distributional Drift in Medical Imaging [5.1358645354733765]
  This paper presents an accurate and sensitive approach to detect distributional drift in CT-scan medical images. We developed a robust library model for real-time anomaly detection, allowing for efficient comparison of incoming images. We fine-tuned a vision transformer pre-trained model to extract relevant features using breast cancer images.
  arXiv  Detail & Related papers  (2024-08-15T23:46:37Z)
• Conditional Semi-Supervised Data Augmentation for Spam Message Detection with Low Resource Data [0.0]
  We propose a conditional semi-supervised data augmentation for a spam detection model lacking the availability of data. We exploit unlabeled data for data augmentation to extend training data. Latent variables can come from labeled and unlabeled data as the input for the final classifier.
  arXiv  Detail & Related papers  (2024-07-06T07:51:24Z)
• Active Foundational Models for Fault Diagnosis of Electrical Motors [0.5999777817331317]
  Fault detection and diagnosis of electrical motors is of utmost importance in ensuring the safe and reliable operation of industrial systems. The existing data-driven deep learning approaches for machine fault diagnosis rely extensively on huge amounts of labeled samples. We propose a foundational model-based Active Learning framework that utilizes less amount of labeled samples.
  arXiv  Detail & Related papers  (2023-11-27T03:25:12Z)
• FlatMatch: Bridging Labeled Data and Unlabeled Data with Cross-Sharpness for Semi-Supervised Learning [73.13448439554497]
  Semi-Supervised Learning (SSL) has been an effective way to leverage abundant unlabeled data with extremely scarce labeled data. Most SSL methods are commonly based on instance-wise consistency between different data transformations. We propose FlatMatch which minimizes a cross-sharpness measure to ensure consistent learning performance between the two datasets.
  arXiv  Detail & Related papers  (2023-10-25T06:57:59Z)
• Exploiting Mixed Unlabeled Data for Detecting Samples of Seen and Unseen Out-of-Distribution Classes [5.623232537411766]
  Out-of-Distribution (OOD) detection is essential in real-world applications, which has attracted increasing attention in recent years. Most existing OOD detection methods require many labeled In-Distribution (ID) data, causing a heavy labeling cost. In this paper, we focus on the more realistic scenario, where limited labeled data and abundant unlabeled data are available. We propose the Adaptive In-Out-aware Learning (AIOL) method, in which we adaptively select potential ID and OOD samples from the mixed unlabeled data.
  arXiv  Detail & Related papers  (2022-10-13T08:34:25Z)
• Boosting Facial Expression Recognition by A Semi-Supervised Progressive Teacher [54.50747989860957]
  We propose a semi-supervised learning algorithm named Progressive Teacher (PT) to utilize reliable FER datasets as well as large-scale unlabeled expression images for effective training. Experiments on widely-used databases RAF-DB and FERPlus validate the effectiveness of our method, which achieves state-of-the-art performance with accuracy of 89.57% on RAF-DB.
  arXiv  Detail & Related papers  (2022-05-28T07:47:53Z)
• Label-Assemble: Leveraging Multiple Datasets with Partial Labels [68.46767639240564]
  "Label-Assemble" aims to unleash the full potential of partial labels from an assembly of public datasets. We discovered that learning from negative examples facilitates both computer-aided disease diagnosis and detection.
  arXiv  Detail & Related papers  (2021-09-25T02:48:17Z)
• Towards Reducing Labeling Cost in Deep Object Detection [61.010693873330446]
  We propose a unified framework for active learning, that considers both the uncertainty and the robustness of the detector. Our method is able to pseudo-label the very confident predictions, suppressing a potential distribution drift.
  arXiv  Detail & Related papers  (2021-06-22T16:53:09Z)
• Boosting Semi-Supervised Face Recognition with Noise Robustness [54.342992887966616]
  This paper presents an effective solution to semi-supervised face recognition that is robust to the label noise aroused by the auto-labelling. We develop a semi-supervised face recognition solution, named Noise Robust Learning-Labelling (NRoLL), which is based on the robust training ability empowered by GN.
  arXiv  Detail & Related papers  (2021-05-10T14:43:11Z)
• A Novel Perspective for Positive-Unlabeled Learning via Noisy Labels [49.990938653249415]
  This research presents a methodology that assigns initial pseudo-labels to unlabeled data which is used as noisy-labeled data, and trains a deep neural network using the noisy-labeled data. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art methods on several benchmark datasets.
  arXiv  Detail & Related papers  (2021-03-08T11:46:02Z)
• Granular conditional entropy-based attribute reduction for partially labeled data with proxy labels [12.755874217721054]
  We propose a rough sets-based semi-supervised attribute reduction method for partially labeled data. A novel conditional entropy measure is proposed, and its monotonicity is proved in theory. Experiments conducted on UCI data sets demonstrate that the proposed semi-supervised attribute reduction method is promising.
  arXiv  Detail & Related papers  (2021-01-23T12:50:09Z)

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.