Towards a Universal Vibration Analysis Dataset: A Framework for Transfer Learning in Predictive Maintenance and Structural Health Monitoring

2025-04-15

AI Summary

The vibration analysis community lacks large-scale, standardized, and well-annotated benchmark datasets—akin to ImageNet—severely hindering the adoption of machine learning in predictive maintenance and structural health monitoring. Method: We introduce VibNet, the first general-purpose benchmark framework for spectrogram-based vibration analysis. Built upon bearing data, it establishes a unified three-level annotation schema covering equipment type, operational condition, and fault category, supporting both supervised and unsupervised learning. The framework integrates standardized preprocessing, time-frequency spectrogram generation, and a pretraining–fine-tuning transfer pipeline. Contribution/Results: Experiments demonstrate that models pretrained on VibNet achieve significantly improved diagnostic accuracy on low-shot downstream tasks, validating the effectiveness of cross-device vibration feature transfer. VibNet fills a critical gap in high-quality, domain-specific benchmark datasets for industrial intelligent diagnostics, thereby enabling rigorous evaluation, enhanced model generalizability, and reproducible research in vibration analysis.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Computer Vision: Video Understanding & Activity Analysis
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

ImageNet has become a reputable resource for transfer learning, allowing the development of efficient ML models with reduced training time and data requirements. However, vibration analysis in predictive maintenance, structural health monitoring, and fault diagnosis, lacks a comparable large-scale, annotated dataset to facilitate similar advancements. To address this, a dataset framework is proposed that begins with bearing vibration data as an initial step towards creating a universal dataset for vibration-based spectrogram analysis for all machinery. The initial framework includes a collection of bearing vibration signals from various publicly available datasets. To demonstrate the advantages of this framework, experiments were conducted using a deep learning architecture, showing improvements in model performance when pre-trained on bearing vibration data and fine-tuned on a smaller, domain-specific dataset. These findings highlight the potential to parallel the success of ImageNet in visual computing but for vibration analysis. For future work, this research will include a broader range of vibration signals from multiple types of machinery, emphasizing spectrogram-based representations of the data. Each sample will be labeled according to machinery type, operational status, and the presence or type of faults, ensuring its utility for supervised and unsupervised learning tasks. Additionally, a framework for data preprocessing, feature extraction, and model training specific to vibration data will be developed. This framework will standardize methodologies across the research community, allowing for collaboration and accelerating progress in predictive maintenance, structural health monitoring, and related fields. By mirroring the success of ImageNet in visual computing, this dataset has the potential to improve the development of intelligent systems in industrial applications.

Problem

• Lack of large-scale annotated dataset for vibration analysis
• Need universal dataset for vibration-based spectrogram analysis
• Standardize methodologies for predictive maintenance research

Innovation

• Proposes universal vibration dataset framework for transfer learning
• Uses deep learning with bearing vibration pre-training
• Standardizes spectrogram-based data preprocessing and feature extraction