In recent decades, Industrial Fault Diagnosis (IFD) has emerged as a crucial discipline concerned with detecting and gathering vital information about industrial equipment's health condition, thereby facilitating the identification of failure types and severities. The pursuit of precise and effective fault recognition has garnered substantial attention, culminating in a focus on automating equipment monitoring to preclude safety accidents and reduce reliance on human labor. The advent of artificial neural networks (ANNs) has been instrumental in augmenting intelligent IFD algorithms, particularly in the context of big data. Despite these advancements, ANNs, being a simplified biomimetic neural network model, exhibit inherent limitations such as resource and data dependencies and restricted cognitive capabilities. To address these limitations, the third-generation Spiking Neural Network (SNN), founded on principles of Brain-inspired computing, has surfaced as a promising alternative. The SNN, characterized by its biological neuron dynamics and spiking information encoding, demonstrates exceptional potential in representing spatiotemporal features. Consequently, developing SNN-based IFD models has gained momentum, displaying encouraging performance. Nevertheless, this field lacks systematic surveys to illustrate the current situation, challenges, and future directions. Therefore, this paper systematically reviews the theoretical progress of SNN-based models to answer the question of what SNN is. Subsequently, it reviews and analyzes existing SNN-based IFD models to explain why SNN needs to be used and how to use it. More importantly, this paper systematically answers the challenges, solutions, and opportunities of SNN in IFD.

翻译：近几十年来，工业故障诊断已发展成为一个关键学科，其关注于检测和收集工业设备健康状况的关键信息，从而促进故障类型和严重程度的识别。对精确有效故障识别的追求已引起广泛关注，最终聚焦于设备监测的自动化，以预防安全事故并减少对人力的依赖。人工神经网络的出现极大地推动了智能工业故障诊断算法的发展，尤其在大数据背景下。尽管取得了这些进展，但作为简化的仿生神经网络模型，人工神经网络仍存在固有的局限性，如资源和数据依赖性以及认知能力受限。为应对这些局限，基于脑启发计算原理的第三代脉冲神经网络已成为一种有前景的替代方案。脉冲神经网络以其生物神经元动力学和脉冲信息编码为特征，在表示时空特征方面展现出卓越潜力。因此，开发基于脉冲神经网络的工业故障诊断模型已获得发展动力，并显示出令人鼓舞的性能。然而，该领域尚缺乏系统性的综述来阐明现状、挑战和未来方向。为此，本文系统回顾了基于脉冲神经网络模型的理论进展，以回答“什么是脉冲神经网络”这一问题。随后，本文回顾并分析了现有的基于脉冲神经网络的工业故障诊断模型，以解释“为何需要使用脉冲神经网络”以及“如何使用它”。更重要的是，本文系统阐述了脉冲神经网络在工业故障诊断中面临的挑战、解决方案与发展机遇。