To address the issues such as wide coverage and limited power, a real-time network status collection and fault diagnosis system is required for the electrical wireless private network. In this paper, the fixed communication terminals are utilized as the network status acquisition devices to monitor the key performance indicators of the network in a real-time manner, and a statistical analysis framework for network fault is proposed based on the data gathered by the minimizing driving test. And then, a fault intelligent diagnosis model is established based on Softmax neural network. The proposed statistical analysis framework of network fault can effectively identify the deterioration of network quality, and the fault identification accuracy is over 89%. The proposed scheme can effectively improve the real-time performance of fault diagnosis of electrical wireless private network, and as a result, reduce the cost of network operation and maintenance, which makes a practical guiding sense.
