The data fusion technology is introduced into the fault diagnosis of hydraulic system of hydraulic excavator. By testing the pressure and flow of each point, oil temperature and spool displacement of the hydraulic system, the image relationship between the data information and fault components is found out, and then the collected data information is fused, A multi-sensor data fusion fault diagnosis method based on knowledge reasoning is formed to accurately diagnose the fault components. Data preprocessing is mainly to reduce the leakage of the original data, eliminate the noise and extract the characteristic signal. Feature extraction refers to peak extraction and weighting. Based on knowledge reasoning, the fault diagnosis of hydraulic excavator multi-sensor data fusion has the following two points to explain
1. The so-called knowledge reasoning refers to the failure criteria determined according to the structural principle of hydraulic system of hydraulic excavator, as well as the diagnosis knowledge obtained through analysis and the reasoning mechanism and diagnosis model thus determined.
2. Multisensor data fusion is the process of synthesizing the information of each sensor and analyzing the fault components and causes according to the diagnosis model. It includes two levels of processing: the first level is to calibrate the data of each sensor to meet the needs of diagnosis, mainly for weighted average. Secondary processing refers to the process of fault reasoning and diagnosis.
