Measurement bias or drift often occurs after long-term use of the sensor. Measuring faults will inevitably mislead the control system, making the goal of advanced control strategies impossible. To this end, this paper proposes an artificial intelligence diagnosis method based on wavelet neural network for the diagnosis of sensor faults. Wavelet analysis is used to extract the frequency domain features of the data, and then the neural network is used to diagnose the frequency domain characteristic data of the signal. On this basis, the data of a single sensor signal is used for self-diagnosis. The advantages of wavelet analysis in extracting signal characteristics and the advantages of neural network in feature learning and discrimination are comprehensively utilized. Based on the correlation of sensor signals, this paper proposes a joint information diagnosis method. To determine the correlation of sensor signals, models based on energy balance and flow-pressure balance were established. The simulation results show that the method can effectively diagnose the fault of the sensor in the multi-sensor system.

长期使用传感器后，经常会发生测量偏差或漂移。测量故障不可避免地会误导控制系统，从而无法实现高级控制策略的目标。为此，本文提出了一种基于小波神经网络的人工智能诊断方法，用于传感器故障的诊断。小波分析用于提取数据的频域特征，然后神经网络用于诊断信号的频域特征数据。在此基础上，将单个传感器信号的数据用于自诊断。小波分析在提取信号特征中的优势以及神经网络在特征学习和识别中的优势得到了综合利用。根据传感器信号的相关性，本文提出了一种联合信息诊断方法。为了确定传感器信号的相关性，建立了基于能量平衡和流量压力平衡的模型。仿真结果表明，该方法可以有效地诊断多传感器系统中的传感器故障。