Progressive Knowledge-Guided Large Language Model Framework for Bearing Fault Diagnosis

• URL: http://arxiv.org/abs/2606.16684v1
• Date: Mon, 15 Jun 2026 13:18:48 GMT
• Title: Progressive Knowledge-Guided Large Language Model Framework for Bearing Fault Diagnosis
• Abstract summary: Vibration-based bearing fault diagnosis requires resolving three interrelated measurement challenges.<n>This paper presents a physics-guided multi-scale vibration signal processing framework.<n>The framework achieves 98.49% diagnostic accuracy with a 12.6-fold reduction in computational cost relative to signal-level baselines.
• Score: 7.28808693425028
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vibration-based bearing fault diagnosis requires resolving three interrelated measurement challenges, including the trade-off between global statistical feature efficiency and local transient signal fidelity, insufficient traceability of measurement features to underlying fault physics, and ineffective multi-source measurement information fusion across diagnostic scales. This paper presents a progressive physics-guided multi-scale vibration signal processing framework that addresses all three challenges within a unified diagnostic pipeline. An 81-dimensional measurement descriptor, derived from bearing kinematic theory and characteristic defect frequencies, establishes a physically traceable feature space enabling real-time fault screening at approximately 20 ms per sample. A fault-adaptive signal segmentation mechanism then directs analytical attention toward fault-relevant waveform regions guided by physics-based priors, without manual feature engineering. Structured fault mechanism knowledge is further encoded implicitly in model parameters during training, enabling autonomous multi-scale measurement fusion without external knowledge dependencies at inference. Validated on four public benchmark datasets under diverse operating conditions, the framework achieves 98.49% diagnostic accuracy with a 12.6-fold reduction in computational cost relative to signal-level baselines. Interpretability analysis confirms that diagnostic feature activations align with established bearing fault mechanics, supporting measurement traceability in safety-critical industrial systems.

Related papers

• Can Large Language Models Generate Observability-Aware Code? [51.30941152293301]
  We present a systematic study of observability in agent-generated systems.<n>Our results reveal a consistent gap between diagnostic semantics at the source level and fault signals at runtime.<n>We introduce an observability-oriented skill, which can serve as a guidance to improve both diagnostic semantics and fault-signal exposure.
  arXiv  Detail & Related papers  (2026-07-07T03:20:52Z)
• Parametric Generalized Adaptive Moment Features (PG-AMF) for Bearing Fault Diagnosis and Machine Health Monitoring [3.1854529627213277]
  In this work, a parametric adaptive feature extraction framework is proposed, in which feature characteristics are learned directly from data.<n>The learned representations effectively capture a wide range of signal characteristics, supporting both improved diagnostic performance and practical applicability in industrial monitoring systems.
  arXiv  Detail & Related papers  (2026-06-24T19:02:23Z)
• Flexible Gravitational-Wave Parameter Estimation with Transformers [73.44614054040267]
  We introduce a flexible transformer-based architecture paired with a training strategy that enables adaptation to diverse analysis settings at inference time.<n>We demonstrate that a single flexible model -- called Dingo-T1 -- can analyze 48 gravitational-wave events from the third LIGO-Virgo-KAGRA Observing Run.
  arXiv  Detail & Related papers  (2025-12-02T17:49:08Z)
• Physics-Guided Dual Implicit Neural Representations for Source Separation [70.38762322922211]
  We develop a self-supervised machine-learning approach for source separation using a dual implicit neural representation framework.<n>Our method learns directly from the raw data by minimizing a reconstruction-based loss function.<n>Our method offers a versatile framework for addressing source separation problems across diverse domains.
  arXiv  Detail & Related papers  (2025-07-07T17:56:31Z)
• A Multimodal Lightweight Approach to Fault Diagnosis of Induction Motors in High-Dimensional Dataset [1.768285973994044]
  An accurate AI-based diagnostic system for induction motors (IMs) holds the potential to enhance proactive maintenance, mitigating unplanned downtime and curbing overall maintenance costs within an industrial environment.<n>Researchers have proposed various fault diagnosis approaches using signal processing (SP), machine learning (ML), deep learning (DL) and hybrid architectures for BRB faults.<n>This paper implements large-scale data of BRB faults by using a transfer-learning-based lightweight DL model named ShuffleNetV2 for diagnosing one, two, three, and four BRB faults using current and vibration signal data.
  arXiv  Detail & Related papers  (2025-01-07T12:40:11Z)
• 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)
• Non-destructive Fault Diagnosis of Electronic Interconnects by Learning Signal Patterns of Reflection Coefficient in the Frequency Domain [1.8843687952462742]
  We propose a novel, non-destructive approach for early fault detection and accurate diagnosis of interconnect defects. Our approach utilizes the signal patterns of the coefficient reflection across a range of frequencies, enabling both root cause identification and severity assessment. Experimental results demonstrate that the proposed method is effective for fault detection and diagnosis and has the potential to extend to real-world industrial applications.
  arXiv  Detail & Related papers  (2023-04-20T10:51:21Z)
• Data-driven design of fault diagnosis for three-phase PWM rectifier using random forests technique with transient synthetic features [2.382536770336505]
  A three-phase pulse-width modulation (PWM) can usually maintain operation when opencircuit faults occur in insulated-gate bipolar transistors (IGBTs) Data-driven online fault diagnosis method is proposed to locate the open-circuit faults of IGBTs timely effectively.
  arXiv  Detail & Related papers  (2022-11-02T05:48:30Z)
• System Resilience through Health Monitoring and Reconfiguration [56.448036299746285]
  We demonstrate an end-to-end framework to improve the resilience of man-made systems to unforeseen events. The framework is based on a physics-based digital twin model and three modules tasked with real-time fault diagnosis, prognostics and reconfiguration.
  arXiv  Detail & Related papers  (2022-08-30T20:16:17Z)
• A Multi-size Kernel based Adaptive Convolutional Neural Network for Bearing Fault Diagnosis [5.811146610419976]
  We propose a data-driven diagnostic algorithm based on the characteristics of bearing vibrations called multi-size kernel based adaptive convolutional neural network (MSKACNN) MSKACNN provides vibration feature learning and signal classification capabilities to identify and analyze bearing faults.
  arXiv  Detail & Related papers  (2022-03-29T06:43:30Z)
• 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)

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.