Fault detection of photoelectric detection equipment based on edge computing and deep learning
Aiming at improving the fault detection effect of photoelectric detection equipment,a fault detection method of photoelectric detection equipment based on edge computing and deep learning is proposed.First,use multi-sensor to collect the working state signal of photoelectric detection equipment,extract features from the signal,and then use edge computing technology to build a photoelectric detection equipment fault detection platform,and use deep learning algorithm to model the photoelectric detection equipment fault detection samples,and build a photoelectric de-tection equipment fault detection classifier.Finally,simulation test results show that this method can detect various photoelectric detection equipment faults with high accuracy,The fault detection accuracy of photoelectric detection e-quipment is more than 95％,and the fault detection time of photoelectric detection equipment is controlled within 20 ms,so the ideal fault detection result of photoelectric detection equipment can be obtained.
edge computingdeep learningphotoelectric detection equipment failuredetection model
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