  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.
