Brain-Inspired Spike Echo State Network Dynamics for Aero-Engine Intelligent Fault Prediction
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866911925966733312 |
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| author | Liu, Mo-Ran Sun, Tao Sun, Xi-Ming |
| author_facet | Liu, Mo-Ran Sun, Tao Sun, Xi-Ming |
| contents | Aero-engine fault prediction aims to accurately predict the development trend of the future state of aero-engines, so as to diagnose faults in advance. Traditional aero-engine parameter prediction methods mainly use the nonlinear mapping relationship of time series data but generally ignore the adequate spatiotemporal features contained in aero-engine data. To this end, we propose a brain-inspired spike echo state network (Spike-ESN) model for aero-engine intelligent fault prediction, which is used to effectively capture the evolution process of aero-engine time series data in the framework of spatiotemporal dynamics. In the proposed approach, we design a spike input layer based on Poisson distribution inspired by the spike neural encoding mechanism of biological neurons, which can extract the useful temporal characteristics in aero-engine sequence data. Then, the temporal characteristics are input into a spike reservoir through the current calculation method of spike accumulation in neurons, which projects the data into a high-dimensional sparse space. In addition, we use the ridge regression method to read out the internal state of the spike reservoir. Finally, the experimental results of aero-engine states prediction demonstrate the superiority and potential of the proposed method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_12918 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Brain-Inspired Spike Echo State Network Dynamics for Aero-Engine Intelligent Fault Prediction Liu, Mo-Ran Sun, Tao Sun, Xi-Ming Machine Learning Neural and Evolutionary Computing Aero-engine fault prediction aims to accurately predict the development trend of the future state of aero-engines, so as to diagnose faults in advance. Traditional aero-engine parameter prediction methods mainly use the nonlinear mapping relationship of time series data but generally ignore the adequate spatiotemporal features contained in aero-engine data. To this end, we propose a brain-inspired spike echo state network (Spike-ESN) model for aero-engine intelligent fault prediction, which is used to effectively capture the evolution process of aero-engine time series data in the framework of spatiotemporal dynamics. In the proposed approach, we design a spike input layer based on Poisson distribution inspired by the spike neural encoding mechanism of biological neurons, which can extract the useful temporal characteristics in aero-engine sequence data. Then, the temporal characteristics are input into a spike reservoir through the current calculation method of spike accumulation in neurons, which projects the data into a high-dimensional sparse space. In addition, we use the ridge regression method to read out the internal state of the spike reservoir. Finally, the experimental results of aero-engine states prediction demonstrate the superiority and potential of the proposed method. |
| title | Brain-Inspired Spike Echo State Network Dynamics for Aero-Engine Intelligent Fault Prediction |
| topic | Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2406.12918 |