Toward End-to-End Bearing Fault Diagnosis for Industrial Scenarios with Spiking Neural Networks

• URL: http://arxiv.org/abs/2408.11067v1
• Date: Sat, 17 Aug 2024 06:41:58 GMT
• Title: Toward End-to-End Bearing Fault Diagnosis for Industrial Scenarios with Spiking Neural Networks
• Authors: Yongqi Ding, Lin Zuo, Mengmeng Jing, Kunshan Yang, Biao Chen, Yunqian Yu,
• Abstract summary: Spiking neural networks (SNNs) transmit information via low-power binary spikes. We propose a Multi-scale Residual Attention SNN (MRA-SNN) to improve efficiency, performance, and robustness of SNN methods. MRA-SNN significantly outperforms existing methods in terms of accuracy, energy consumption noise robustness, and is more feasible for deployment in real-world industrial scenarios.
• Score: 6.686258023516048
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Spiking neural networks (SNNs) transmit information via low-power binary spikes and have received widespread attention in areas such as computer vision and reinforcement learning. However, there have been very few explorations of SNNs in more practical industrial scenarios. In this paper, we focus on the application of SNNs in bearing fault diagnosis to facilitate the integration of high-performance AI algorithms and real-world industries. In particular, we identify two key limitations of existing SNN fault diagnosis methods: inadequate encoding capacity that necessitates cumbersome data preprocessing, and non-spike-oriented architectures that constrain the performance of SNNs. To alleviate these problems, we propose a Multi-scale Residual Attention SNN (MRA-SNN) to simultaneously improve the efficiency, performance, and robustness of SNN methods. By incorporating a lightweight attention mechanism, we have designed a multi-scale attention encoding module to extract multiscale fault features from vibration signals and encode them as spatio-temporal spikes, eliminating the need for complicated preprocessing. Then, the spike residual attention block extracts high-dimensional fault features and enhances the expressiveness of sparse spikes with the attention mechanism for end-to-end diagnosis. In addition, the performance and robustness of MRA-SNN is further enhanced by introducing the lightweight attention mechanism within the spiking neurons to simulate the biological dendritic filtering effect. Extensive experiments on MFPT and JNU benchmark datasets demonstrate that MRA-SNN significantly outperforms existing methods in terms of accuracy, energy consumption and noise robustness, and is more feasible for deployment in real-world industrial scenarios.

Related papers

• Scalable Mechanistic Neural Networks [52.28945097811129]
  We propose an enhanced neural network framework designed for scientific machine learning applications involving long temporal sequences. By reformulating the original Mechanistic Neural Network (MNN) we reduce the computational time and space complexities from cubic and quadratic with respect to the sequence length, respectively, to linear. Extensive experiments demonstrate that S-MNN matches the original MNN in precision while substantially reducing computational resources.
  arXiv  Detail & Related papers  (2024-10-08T14:27:28Z)
• Towards Low-latency Event-based Visual Recognition with Hybrid Step-wise Distillation Spiking Neural Networks [50.32980443749865]
  Spiking neural networks (SNNs) have garnered significant attention for their low power consumption and high biologicalability. Current SNNs struggle to balance accuracy and latency in neuromorphic datasets. We propose Step-wise Distillation (HSD) method, tailored for neuromorphic datasets.
  arXiv  Detail & Related papers  (2024-09-19T06:52:34Z)
• Q-SNNs: Quantized Spiking Neural Networks [12.719590949933105]
  Spiking Neural Networks (SNNs) leverage sparse spikes to represent information and process them in an event-driven manner. We introduce a lightweight and hardware-friendly Quantized SNN that applies quantization to both synaptic weights and membrane potentials. We present a new Weight-Spike Dual Regulation (WS-DR) method inspired by information entropy theory.
  arXiv  Detail & Related papers  (2024-06-19T16:23:26Z)
• SpikingJET: Enhancing Fault Injection for Fully and Convolutional Spiking Neural Networks [37.89720165358964]
  SpikingJET is a novel fault injector designed specifically for fully connected and convolutional Spiking Neural Networks (SNNs) Our work underscores the critical need to evaluate the resilience of SNNs to hardware faults, considering their growing prominence in real-world applications.
  arXiv  Detail & Related papers  (2024-03-30T14:51:01Z)
• Low Latency of object detection for spikng neural network [3.404826786562694]
  Spiking Neural Networks are well-suited for edge AI applications due to their binary spike nature. In this paper, we focus on generating highly accurate and low-latency SNNs specifically for object detection.
  arXiv  Detail & Related papers  (2023-09-27T10:26:19Z)
• Enabling energy-Efficient object detection with surrogate gradient descent in spiking neural networks [0.40054215937601956]
  Spiking Neural Networks (SNNs) are a biologically plausible neural network model with significant advantages in both event-driven processing and processing-temporal information. In this study, we introduce the Current Mean Decoding (CMD) method, which solves the regression problem to facilitate the training of deep SNNs for object detection tasks. Based on the gradient surrogate and CMD, we propose the SNN-YOLOv3 model for object detection.
  arXiv  Detail & Related papers  (2023-09-07T15:48:00Z)
• Inherent Redundancy in Spiking Neural Networks [24.114844269113746]
  Spiking Networks (SNNs) are a promising energy-efficient alternative to conventional artificial neural networks. In this work, we focus on three key questions regarding inherent redundancy in SNNs. We propose an Advance Attention (ASA) module to harness SNNs' redundancy.
  arXiv  Detail & Related papers  (2023-08-16T08:58:25Z)
• A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks [53.31941519245432]
  Brain-inspired spiking neural networks (SNNs) have demonstrated promising capabilities in solving pattern recognition tasks. These SNNs are grounded on homogeneous neurons that utilize a uniform neural coding for information representation. In this study, we argue that SNN architectures should be holistically designed to incorporate heterogeneous coding schemes.
  arXiv  Detail & Related papers  (2023-05-26T02:52:12Z)
• On the Intrinsic Structures of Spiking Neural Networks [66.57589494713515]
  Recent years have emerged a surge of interest in SNNs owing to their remarkable potential to handle time-dependent and event-driven data. There has been a dearth of comprehensive studies examining the impact of intrinsic structures within spiking computations. This work delves deep into the intrinsic structures of SNNs, by elucidating their influence on the expressivity of SNNs.
  arXiv  Detail & Related papers  (2022-06-21T09:42:30Z)
• Comparative Analysis of Interval Reachability for Robust Implicit and Feedforward Neural Networks [64.23331120621118]
  We use interval reachability analysis to obtain robustness guarantees for implicit neural networks (INNs) INNs are a class of implicit learning models that use implicit equations as layers. We show that our approach performs at least as well as, and generally better than, applying state-of-the-art interval bound propagation methods to INNs.
  arXiv  Detail & Related papers  (2022-04-01T03:31:27Z)
• Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks [55.0627904986664]
  Spiking Neural Networks (SNNs) usetemporal spike patterns to represent and transmit information, which is not only biologically realistic but also suitable for ultra-low-power event-driven neuromorphic implementation. This paper investigates the contribution of spike timing dynamics to information encoding, synaptic plasticity and decision making, providing a new perspective to design of future DeepSNNs and neuromorphic hardware systems.
  arXiv  Detail & Related papers  (2020-03-26T11:13:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.