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.
Industrial Fault Diagnosis and the Evolution of Fault Recognition
In recent decades, Industrial Fault Diagnosis (IFD) has become increasingly important in ensuring the health and safety of industrial equipment. The ability to detect and gather information about the condition of equipment has become crucial in identifying potential failures and their severity. As a result, there has been a significant focus on automating equipment monitoring to minimize safety accidents and reduce the reliance on human labor.
The Role of Artificial Neural Networks in IFD
Artificial Neural Networks (ANNs) have played a significant role in enhancing intelligent IFD algorithms, especially when dealing with large amounts of data. ANNs, as simplified models of the human brain, have proven to be effective in fault recognition. However, they do have limitations, such as their reliance on resources and data, as well as their restricted cognitive capabilities.
The Promising Alternative: Spiking Neural Networks (SNNs)
To overcome the limitations of ANNs, a third-generation model called Spiking Neural Networks (SNNs) has emerged as a promising alternative. SNNs are built on principles of Brain-inspired computing and are characterized by their biological neuron dynamics and spiking information encoding. This unique structure allows SNNs to represent spatiotemporal features exceptionally well.
The Growing Momentum of SNN-based IFD Models
The development of SNN-based IFD models has gained momentum due to their demonstrated performance. The use of SNNs in fault recognition allows for more accurate and robust results. However, despite this progress, there is a lack of systematic surveys in this field to illustrate the current situation, challenges, and future directions.
The Objectives of this Paper
This paper aims to systematically review the theoretical progress of SNN-based models in IFD. It seeks to answer the fundamental question of what SNN is and provide an understanding of its theoretical foundations. Additionally, the paper reviews and analyzes existing SNN-based IFD models to explain why SNN is necessary and how it can be effectively utilized. Lastly, the paper aims to address the challenges, solutions, and opportunities of SNN in IFD.
By addressing these objectives, this systematic review paper will contribute to the overall understanding and development of SNN-based IFD models. It will highlight the multi-disciplinary nature of this field, incorporating concepts from industrial engineering, artificial intelligence, and neuroscience.