JIANGNan,FENGLi,LINZi-yin,et al.Precise Detection System for Sewage Leakage in Pneumatic Flap Doors Based on Dynamic Mean Perceptron Neural Network[J].China Water & Wastewater,2024,40(17):118-122.
基于动态均值感知器的拍门污水泄漏精准检测系统
- Title:
- Precise Detection System for Sewage Leakage in Pneumatic Flap Doors Based on Dynamic Mean Perceptron Neural Network
- Keywords:
- drainage network; flap door; sewage leakage; random error; mean detection; neural network; perceptron
- 摘要:
- 采用均值检测原理与感知器神经网络分类原理，基于动态均值感知器神经网络分类模型，研发了排水管网拍门污水泄漏精准检测系统。首先，以固有拍门临界工作气压为中心逐次对称增压/减压20 kPa，并连续9次检测拍门位置状态，组成9维动态特征向量；然后与9维均值权重向量加权求和，提取动态均值特征量；最后，通过阶跃激活函数，采用输出阈值分类检测拍门污水泄漏状态，并训练输出阈值为3.6。在广州市猎德涌进行了现场测试，拍门污水泄漏检测精度为96%。该方法有效解决了误检和漏检问题，从而减少了排水管网的拍门污水泄漏。
- Abstract:
- Based on the principles of mean detection and perceptron neural network classification, a dynamic mean perceptron neural network classification model was used to develop a precise detection system for sewage leakage in pneumatic flap doors of drainage network. Firstly，pressure was symmetrically increased/decreased by 20 kPa with the inherent critical working pressure P as the center, and the position and state of the flap door 9 times was continuously detected to form a 9-dimensional dynamic feature vector. Then, by weighting and summing with the 9-dimensional mean weight vector, the dynamic mean feature quantity was extracted. Finally, by using a step activation function, the output threshold classification was used to detect the leakage status of the sewage in the flap door, and the output threshold was trained to be 3.6. The on-site application system test of Liede Chong in Guangzhou City showed that the accuracy of the sewage leakage detection of the flap door was 96%. This method effectively solves the problems of the random and systematic errors of detection results, thereby reducing the leakage of sewage in the drainage network.
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