Data-driven fault diagnosis methods suffer from restrictive assumptions that hinder adaptability to varying work conditions, rendering offline modes insufficient. Addressing this challenge, a bioinspired spike neural network (bio-SNN) is proposed, featuring an innovative online learning mode. The network employs a novel spike encoding method with data compression for efficiently transforming time series data into spike sequences. This encoding method involves converting 1-D series data into a 2-D spectrogram using a filter bank, incorporating a stochastic spike rate for a more flexible representation compared to precise spike rates. The application of a biologically plausible learning rule, specifically spike timing-dependent plasticity (STDP), enhances the adaptability of the network. A horizontal inhibition and homeostasis mechanism are also introduced, facilitating effective online updating of synaptic weights. Experimental results on two well-established fault datasets showcase the advantages of the bio-SNN method over existing approaches, highlighting its potential for robust and adaptive fault diagnosis in practical scenarios.

中文翻译：

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使用仿生尖峰神经网络进行在线故障诊断


数据驱动的故障诊断方法受到限制性假设的影响，阻碍了对不同工作条件的适应性，导致离线模式不足。为了应对这一挑战，提出了一种仿生尖峰神经网络（bio-SNN），其特点是创新的在线学习模式。该网络采用了一种新颖的尖峰编码方法和数据压缩，可以有效地将时间序列数据转换为尖峰序列。这种编码方法涉及使用滤波器组将一维系列数据转换为二维频谱图，并结合随机尖峰率以实现比精确尖峰率更灵活的表示。应用生物学上合理的学习规则，特别是尖峰时间依赖性可塑性（STDP），增强了网络的适应性。还引入了水平抑制和稳态机制，促进突触权重的有效在线更新。两个成熟的故障数据集上的实验结果展示了生物 SNN 方法相对于现有方法的优势，突出了其在实际场景中稳健和自适应故障诊断的潜力。