Advancing machine fault diagnosis: A detailed examination of convolutional neural networks
It addresses the problem of reliable fault diagnosis for machinery operators and engineers, but it is incremental as a review paper summarizing existing methods.
This paper reviews the application of convolutional neural networks (CNNs) for machine fault diagnosis, analyzing their effectiveness in handling various fault types and data complexities to improve operational efficiency and safety.
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, we highlight future research directions and potential challenges to further enhance the application of CNNs for reliable and proactive machine fault diagnosis.