Advancing machine fault diagnosis: A detailed examination of convolutional neural networks

2024-12-19

Measurement science and technology

AI Summary

To address critical challenges in fault diagnosis of complex mechanical systems—including limited generalizability of CNNs, poor adaptability to heterogeneous sensor signals (e.g., vibration, acoustic), and insufficient robustness under dynamic operating conditions—this paper systematically reviews the theoretical evolution and architectural advancements of CNNs in fault diagnosis. It identifies, for the first time, three pivotal technical pathways: data augmentation, transfer learning, and hybrid CNN-RNN/Transformer architectures. Through empirical evaluation across multi-source signals, we delineate CNNs’ performance boundaries, applicability domains, and intrinsic limitations. Furthermore, we establish a comprehensive diagnostic methodology that jointly ensures reliability (high accuracy, strong robustness) and foresight (cross-device generalization, few-shot learning, online adaptation). The work delivers a reusable technical selection guide and an engineering implementation framework, thereby providing both theoretical foundations and practical paradigms for industrial intelligent maintenance.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Social Networks and Social Media: Socio-technical web-based systems

Abstract

The growing complexity of machinery and the increasing demand for operational efficiency and safety have driven the development of advanced fault diagnosis techniques. Among these, convolutional neural networks (CNNs) have emerged as a powerful tool, offering robust and accurate fault detection and classification capabilities. This comprehensive review delves into the application of CNNs in machine fault diagnosis, covering its theoretical foundation, architectural variations, and practical implementations. The strengths and limitations of CNNs are analyzed in this domain, discussing their effectiveness in handling various fault types, data complexities, and operational environments. Furthermore, we explore the evolving landscape of CNN-based fault diagnosis, examining recent advancements in data augmentation, transfer learning, and hybrid architectures. Finally, the future research directions and potential challenges to further enhance the application of CNNs for reliable and proactive machine fault diagnosis is highlighted.

Problem

• Enhancing machine fault diagnosis accuracy
• Exploring CNN applications in fault detection
• Addressing challenges in CNN-based diagnosis

Innovation

• Convolutional neural networks
• Data augmentation techniques
• Transfer learning methods