So far, most practical hydraulic system fault detection […]
So far, most practical hydraulic system fault detection methods in engineering are based on experienced technicians to judge the fault based on the system pressure, or after the system fails, the pressure and flow sensors are temporarily installed to measure the system pressure or flow to find the fault. The time is longer. Obviously, online fault diagnosis and prediction of hydraulic systems can improve the reliability and utilization of large hydraulic equipment. The focus of future research is mainly on the following three aspects:
(1) In-depth study of intelligent diagnosis methods for complex hydraulic system faults
At present, most of the papers use BP neural network or expert system to diagnose the faults of single hydraulic components such as hydraulic pumps, hydraulic motors, hydraulic cylinders or system leaks. In the future, intelligent diagnosis methods such as neural network methods and expert system diagnosis methods will be integrated to carry out fault diagnosis research on complex electro-hydraulic systems.
(2) Strengthen the research of sensors and other hardware
Both the neural network diagnosis method and the expert diagnosis method need to collect a large amount of data for analysis. Therefore, the high precision and high reliability of the sensor are the prerequisites to realize the intelligent fault diagnosis, and the research of the intelligent sensor is the technical guarantee for the intelligent fault diagnosis.
(3) Carry out research on general software for intelligent fault diagnosis of hydraulic system.
Develop sensors with high reliability and standardized information transmission, and develop general tool software for hydraulic system fault diagnosis expert systems, so that different hydraulic systems can use the same software for fault diagnosis, so as to achieve the purpose of large-scale popularization and application of software and reducing development costs.
With the improvement of the degree of automation of industrial equipment, more and more attention is paid to equipment fault diagnosis. The research of a variety of intelligent fault diagnosis methods will provide guarantee for the reliable operation of equipment.