GNN-ASE: Graph-Based Anomaly Detection and Severity Estimation in Three-Phase Induction Machines

• URL: http://arxiv.org/abs/2508.00879v1
• Date: Wed, 23 Jul 2025 01:50:07 GMT
• Title: GNN-ASE: Graph-Based Anomaly Detection and Severity Estimation in Three-Phase Induction Machines
• Authors: Moutaz Bellah Bentrad, Adel Ghoggal, Tahar Bahi, Abderaouf Bahi,
• Abstract summary: This paper proposes a model-free approach using Graph Neural Networks (GNNs) for fault diagnosis in induction machines.<n>The proposed GNN-ASE model automatically learns and extracts relevant features from raw inputs.<n>It is evaluated for both individual fault detection and multi-class classification of combined fault conditions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The diagnosis of induction machines has traditionally relied on model-based methods that require the development of complex dynamic models, making them difficult to implement and computationally expensive. To overcome these limitations, this paper proposes a model-free approach using Graph Neural Networks (GNNs) for fault diagnosis in induction machines. The focus is on detecting multiple fault types -- including eccentricity, bearing defects, and broken rotor bars -- under varying severity levels and load conditions. Unlike traditional approaches, raw current and vibration signals are used as direct inputs, eliminating the need for signal preprocessing or manual feature extraction. The proposed GNN-ASE model automatically learns and extracts relevant features from raw inputs, leveraging the graph structure to capture complex relationships between signal types and fault patterns. It is evaluated for both individual fault detection and multi-class classification of combined fault conditions. Experimental results demonstrate the effectiveness of the proposed model, achieving 92.5\% accuracy for eccentricity defects, 91.2\% for bearing faults, and 93.1\% for broken rotor bar detection. These findings highlight the model's robustness and generalization capability across different operational scenarios. The proposed GNN-based framework offers a lightweight yet powerful solution that simplifies implementation while maintaining high diagnostic performance. It stands as a promising alternative to conventional model-based diagnostic techniques for real-world induction machine monitoring and predictive maintenance.

Related papers

• Digital Twin-Driven Zero-Shot Fault Diagnosis of Axial Piston Pumps Using Fluid-Borne Noise Signals [4.11115562060457]
  Axial piston pumps are crucial components in fluid power systems.<n>Traditional data-driven methods require extensive labeled fault data.<n>This paper proposes a digital twin (DT)-driven zero-shot fault diagnosis framework.
  arXiv  Detail & Related papers  (2025-12-22T11:24:42Z)
• Transformer-Based Indirect Structural Health Monitoring of Rail Infrastructure with Attention-Driven Detection and Localization of Transient Defects [1.1782896991259]
  We introduce an incremental synthetic data benchmark designed to evaluate model robustness against progressively complex challenges.<n>We evaluate several established unsupervised models alongside our proposed Attention-Focused Transformer.<n>Our proposed model achieves accuracy comparable to the state-of-the-art solution while demonstrating better inference speed.
  arXiv  Detail & Related papers  (2025-10-08T23:01:53Z)
• Fault detection and diagnosis for the engine electrical system of a space launcher based on a temporal convolutional autoencoder and calibrated classifiers [0.0]
  This paper outlines a first step toward developing an onboard fault detection and diagnostic capability for the next generation of reusable space launchers.<n>Unlike existing approaches in the literature, our solution is designed to meet a broader range of key requirements.<n>The proposed solution is based on a temporal convolutional autoencoder to automatically extract low-dimensional features from raw sensor data.
  arXiv  Detail & Related papers  (2025-07-17T11:50:29Z)
• Process mining-driven modeling and simulation to enhance fault diagnosis in cyber-physical systems [5.065341495341096]
  Fault diagnosis in Cyber-Physical Systems (CPSs) is essential for ensuring system dependability and operational efficiency.<n>We present a novel unsupervised fault diagnosis methodology that integrates collective anomaly detection in time series, process mining, and simulation.<n>This enables the creation of comprehensive fault dictionaries that support predictive maintenance and the development of digital twins for industrial environments.
  arXiv  Detail & Related papers  (2025-06-26T17:29:37Z)
• Counterfactual Explanation for Auto-Encoder Based Time-Series Anomaly Detection [0.3199881502576702]
  Auto-Encoders exhibit inherent opaqueness in their decision-making processes, hindering their practical implementation at scale.<n>In this work, we employ a feature selector to select features and counterfactual explanations to give a context to the model output.<n>Our experimental findings illustrate that our proposed counterfactual approach can offer meaningful and valuable insights into the model decision-making process.
  arXiv  Detail & Related papers  (2025-01-03T19:30:11Z)
• Unsupervised Model Diagnosis [49.36194740479798]
  This paper proposes Unsupervised Model Diagnosis (UMO) to produce semantic counterfactual explanations without any user guidance. Our approach identifies and visualizes changes in semantics, and then matches these changes to attributes from wide-ranging text sources.
  arXiv  Detail & Related papers  (2024-10-08T17:59:03Z)
• Fault Detection and Monitoring using a Data-Driven Information-Based Strategy: Method, Theory, and Application [5.056456697289351]
  We propose an information-driven fault detection method based on a novel concept drift detector.<n>The method is tailored to identifying drifts in input-output relationships of additive noise models.<n>We prove several theoretical properties of the proposed MI-based fault detection scheme.
  arXiv  Detail & Related papers  (2024-05-06T17:43:39Z)
• Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection [59.34318192698142]
  We introduce a prior-less anomaly generation paradigm and develop an innovative unsupervised anomaly detection framework named GRAD. PatchDiff effectively expose various types of anomaly patterns. experiments on both MVTec AD and MVTec LOCO datasets also support the aforementioned observation.
  arXiv  Detail & Related papers  (2023-12-26T07:08:06Z)
• Causal Disentanglement Hidden Markov Model for Fault Diagnosis [55.90917958154425]
  We propose a Causal Disentanglement Hidden Markov model (CDHM) to learn the causality in the bearing fault mechanism. Specifically, we make full use of the time-series data and progressively disentangle the vibration signal into fault-relevant and fault-irrelevant factors. To expand the scope of the application, we adopt unsupervised domain adaptation to transfer the learned disentangled representations to other working environments.
  arXiv  Detail & Related papers  (2023-08-06T05:58:45Z)
• Diffusion Denoising Process for Perceptron Bias in Out-of-distribution Detection [67.49587673594276]
  We introduce a new perceptron bias assumption that suggests discriminator models are more sensitive to certain features of the input, leading to the overconfidence problem. We demonstrate that the diffusion denoising process (DDP) of DMs serves as a novel form of asymmetric, which is well-suited to enhance the input and mitigate the overconfidence problem. Our experiments on CIFAR10, CIFAR100, and ImageNet show that our method outperforms SOTA approaches.
  arXiv  Detail & Related papers  (2022-11-21T08:45:08Z)
• An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery [2.055054374525828]
  This paper proposes a new approach for fault detection and diagnosis in rotating machinery. The methodology consists of three parts: feature extraction, fault detection and fault diagnosis. The effectiveness of the proposed approach is shown on three datasets containing different mechanical faults.
  arXiv  Detail & Related papers  (2021-02-23T18:28:18Z)
• A Novel Anomaly Detection Algorithm for Hybrid Production Systems based on Deep Learning and Timed Automata [73.38551379469533]
  DAD:DeepAnomalyDetection is a new approach for automatic model learning and anomaly detection in hybrid production systems. It combines deep learning and timed automata for creating behavioral model from observations. The algorithm has been applied to few data sets including two from real systems and has shown promising results.
  arXiv  Detail & Related papers  (2020-10-29T08:27:43Z)
• Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders [51.691585766702744]
  We propose a variant of Adversarial Autoencoder which uses a mirrored Wasserstein loss in the discriminator to enforce better semantic-level reconstruction. We put forward an alternative measure of anomaly score to replace the reconstruction-based metric. Our method outperforms the current state-of-the-art methods for anomaly detection on several OOD detection benchmarks.
  arXiv  Detail & Related papers  (2020-03-24T08:26:58Z)
• SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection [63.253850875265115]
  Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples. We propose a modular acceleration system, called SUOD, to address it.
  arXiv  Detail & Related papers  (2020-03-11T00:22:50Z)

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.