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