According to the probability and statistics theory, the free height of the large number of bolster springs is normally distributed. In this part, a large number of original spring height records are input into the database as samples. Based on the theory of probability and statistics, the data is analyzed and processed by database technology, and the data statistics are visually given in the form of histograms. Optimize the matching rules based on the rules. According to the matching rule, the spring is matched, and the success probability of the spring matching in each spring interval is counted.
Using database programming to automatically partition data, display This article combines high-level programming language with database technology to achieve automatic partitioning of data, and store data in the database. The graphical component is used to graphically represent the partitions, which more visually shows the data relationships in the database, as shown in .
According to the rules, the spring-predicting model is established according to the rule that the free height difference of the same set of springs should not exceed 2 mm. The matching model adopts the principle of limiting interval combination, that is, each type of large spring has three kinds of spring-loading modes and small springs: Small spring matching of the left adjacent section; small spring matching with this section; (small spring matching with the right adjacent section. In order to ensure the probability of success of small probability spring matching as large as possible, to achieve small inventory The priority is set in the spring prediction model, and the priority of the small probability spring is higher than that of the large probability spring, that is, the priority is selected for the small probability spring, and the principle of the two-way matching spring is adopted.
The free height detection link of the pillow spring uses a non-contact capacitive sensor to convert the free height signal of the pillow spring into a voltage signal, which is sent to the single-chip microcomputer for processing by A/D conversion, thereby realizing the online detection of the free height of the pillow spring. The non-contact capacitive sensor is used to collect the height data, which avoids the error caused by the spring deformation, and does not need to compensate the error, which simplifies the program and shortens the development cycle.
The hardware of this part of the pillow spring is mainly composed of a wind cylinder and a solenoid valve. There are 8 sections of the spring of the pillow spring. Each section has a corresponding section size and is provided with a wind cylinder and a solenoid valve. The pillow spring selection wiring of the system can accommodate 200 bolster springs. When the number of bolster springs reaches 70% of the capacity, it is adjusted according to the actual situation on the site. The adjustment method is more retreat and less, that is, the number of occlusion springs in a certain interval is large. For the predicted amount, the system automatically retracts the spring. If it is less than the predicted amount, the field staff extracts the same period of the bolster spring from the spare spring to supplement it as closely as possible. According to the free height, type and adjustment result of the bolster, the system issues a partition command through the computer to control the action of the solenoid valve. The action of the air cylinder is controlled by a solenoid valve, and the transmitted bolster spring is distributed to the respective sections of the bolster spring by the action of the air cylinder.
Conclusion The system model introduced in this paper makes use of the spring-predictive model and type recognition for the first time. The use of this system can completely eliminate the hidden dangers caused by the repair of the occipital spring of the vehicle, greatly reduce the labor intensity and improve the work efficiency, and is of great significance for improving the automation level of railway vehicle maintenance.
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