Graph Neural Network-Based Semi-Supervised Open-Set Fault Diagnosis for Marine Machinery Systems

• URL: http://arxiv.org/abs/2511.01258v1
• Date: Mon, 03 Nov 2025 06:06:25 GMT
• Title: Graph Neural Network-Based Semi-Supervised Open-Set Fault Diagnosis for Marine Machinery Systems
• Authors: Chuyue Lou, M. Amine Atoui,
• Abstract summary: This paper proposes a semi-supervised open-set fault diagnosis (SOFD) framework that enhances and extends the applicability of deep learning models in open-set fault diagnosis scenarios.<n>The framework includes a reliability subset construction process, which uses a multi-layer fusion feature representation extracted by a supervised feature learning model to select an unlabeled test subset.<n>The labeled training set and pseudo-labeled test subset are then fed into a semi-supervised diagnosis model to learn discriminative features for each class, enabling accurate classification of known faults and effective detection of unknown samples.
• Score: 0.42970700836450487
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recently, fault diagnosis methods for marine machinery systems based on deep learning models have attracted considerable attention in the shipping industry. Most existing studies assume fault classes are consistent and known between the training and test datasets, and these methods perform well under controlled environment. In practice, however, previously unseen or unknown fault types (i.e., out-of-distribution or open-set observations not present during training) can occur, causing such methods to fail and posing a significant challenge to their widespread industrial deployment. To address this challenge, this paper proposes a semi-supervised open-set fault diagnosis (SOFD) framework that enhances and extends the applicability of deep learning models in open-set fault diagnosis scenarios. The framework includes a reliability subset construction process, which uses a multi-layer fusion feature representation extracted by a supervised feature learning model to select an unlabeled test subset. The labeled training set and pseudo-labeled test subset are then fed into a semi-supervised diagnosis model to learn discriminative features for each class, enabling accurate classification of known faults and effective detection of unknown samples. Experimental results on a public maritime benchmark dataset demonstrate the effectiveness and superiority of the proposed SOFD framework.

Related papers

• ProbeLLM: Automating Principled Diagnosis of LLM Failures [89.44131968886184]
  We propose ProbeLLM, a benchmark-agnostic automated probing framework that elevates weakness discovery from individual failures to structured failure modes.<n>By restricting probing to verifiable test cases and leveraging tool-augmented generation and verification, ProbeLLM grounds failure discovery in reliable evidence.
  arXiv  Detail & Related papers  (2026-02-13T14:33:13Z)
• Learning Robust Diffusion Models from Imprecise Supervision [75.53546939251146]
  DMIS is a unified framework for training robust Conditional Diffusion Models from Imprecise Supervision.<n>Our framework is derived from likelihood and decomposes the objective into generative and classification components.<n>Experiments on diverse forms of imprecise supervision, covering tasks covering image generation, weakly supervised learning, and dataset condensation demonstrate that DMIS consistently produces high-quality and class-discriminative samples.
  arXiv  Detail & Related papers  (2025-10-03T14:00:32Z)
• Towards a more realistic evaluation of machine learning models for bearing fault diagnosis [0.28873930745906956]
  This paper investigates the issue of data leakage in vibration-based bearing fault diagnosis and its impact on model evaluation.<n>We propose a leakage-free evaluation methodology centered on bearing-wise data partitioning, ensuring no overlap between the physical components used for training and testing.<n>We evaluate our methodology on three widely adopted datasets: CWRU, Paderborn University (PU), and University of Ottawa (UORED-VAF)
  arXiv  Detail & Related papers  (2025-09-26T12:35:02Z)
• CLIP Meets Diffusion: A Synergistic Approach to Anomaly Detection [54.85000884785013]
  Anomaly detection is a complex problem due to the ambiguity in defining anomalies, the diversity of anomaly types, and the scarcity of training data.<n>We propose CLIPfusion, a method that leverages both discriminative and generative foundation models.<n>We believe that our method underscores the effectiveness of multi-modal and multi-model fusion in tackling the multifaceted challenges of anomaly detection.
  arXiv  Detail & Related papers  (2025-06-13T13:30:15Z)
• Class Incremental Fault Diagnosis under Limited Fault Data via Supervised Contrastive Knowledge Distillation [9.560742599396411]
  Class-incremental fault diagnosis requires a model to adapt to new fault classes while retaining previous knowledge.<n>We introduce a Supervised Contrastive knowledge distiLlation for class Incremental Fault Diagnosis framework.
  arXiv  Detail & Related papers  (2025-01-16T13:20:29Z)
• A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection [89.92916473403108]
  This paper proposes a comprehensive visual anomaly detection benchmark, ADer, which is a modular framework for new methods.<n>The benchmark includes multiple datasets from industrial and medical domains, implementing fifteen state-of-the-art methods and nine comprehensive metrics.<n>We objectively reveal the strengths and weaknesses of different methods and provide insights into the challenges and future directions of multi-class visual anomaly detection.
  arXiv  Detail & Related papers  (2024-06-05T13:40:07Z)
• 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)
• 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)
• Generalized Out-of-distribution Fault Diagnosis (GOOFD) via Internal Contrastive Learning [8.583116999933731]
  We propose a Generalized Out-of-distribution Fault Diagnosis framework to integrate diagnosis subtasks. A unified fault diagnosis method based on internal contrastive learning and Mahalanobis distance is put forward to underpin the proposed framework. Our proposed method can be applied to multiple faults diagnosis tasks and achieve better performance than the existing single-task methods.
  arXiv  Detail & Related papers  (2023-06-27T07:50:25Z)
• 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)
• On-board Fault Diagnosis of a Laboratory Mini SR-30 Gas Turbine Engine [54.650189434544146]
  A data-driven fault diagnosis and isolation scheme is explicitly developed for failure in the fuel supply system and sensor measurements. A model is trained using machine learning classifiers to detect a given set of fault scenarios in real-time on which it is trained. Several simulation studies were carried out to demonstrate and illustrate the proposed fault diagnosis scheme's advantages, capabilities, and performance.
  arXiv  Detail & Related papers  (2021-10-17T13:42:37Z)
• Probabilistic Bearing Fault Diagnosis Using Gaussian Process with Tailored Feature Extraction [10.064000794573756]
  Rolling bearings are subject to various faults due to its long-time operation under harsh environment. Current deep learning methods perform the bearing fault diagnosis in the form of deterministic classification. We develop a probabilistic fault diagnosis framework that can account for the uncertainty effect in prediction.
  arXiv  Detail & Related papers  (2021-09-19T18:34:29Z)
• Open Set Recognition with Conditional Probabilistic Generative Models [51.40872765917125]
  We propose Conditional Probabilistic Generative Models (CPGM) for open set recognition. CPGM can detect unknown samples but also classify known classes by forcing different latent features to approximate conditional Gaussian distributions. Experiment results on multiple benchmark datasets reveal that the proposed method significantly outperforms the baselines.
  arXiv  Detail & Related papers  (2020-08-12T06:23:49Z)

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.