Yue Youjun

The design of multiobjective fuzzy control algorithm in elevator group control system

The essential problem of elevator group control is how to manage the operation of the elevators properly and then to reach the top efficiency; a high efficiency means a shorter waiting time and running time, less crowding, and less power consumption. A good algorithm should be able to select a proper control policy according to the traffic pattern. This paper proposes a new multiobjective elevator group control algorithm based on fuzzy logic, and describes its structure and realization in detail. The algorithm can change the power vector of the multiobjectives automatically. The effectiveness of the algorithm is confirmed by computation result.

Design and realization of multi-function car-carry fault diagnosis system

The present study fist proposed the structure of a multi-function car-carry fault diagnosis system, and the design of vehicle status monitoring module. Secondly, this paper analyzed the design of vehicle fault diagnosis expert knowledge base, inference machine and interpreter program. The paper used fault tree analysis (FTA) to establish fault tree of automotive engine, chassis systems, body parts and electrical equipment, then simplified the fault tree to form fault diagnosis rules, and establishes car-carry fault diagnosis expert system knowledge base. In its software implementation, SQLite database is used to save and manage diagnostic rules. Expert system inference engine uses an interactive search method based adopt reasoning mechanism. The expert system uses preset text to realize explanation. Finally, this paper discusses hardware and software design of the fault diagnosis system, and demonstrates the systems human-machine interface operating results.

Developmentof automobile fault diagnosis expert system based on fault tree — Neural network ensamble

The structure of cars is increasingly complex, the fault type, fault phenomena and fault causes of cars is more complicated, therefore ordinary users are stranded when fault happens. To solve the above questions, this paper outlines a kind of on-vehicle fault diagnosis expert system based on fault tree — neural network ensamble. The knowledge base of the expert system is divided into two parts, the fault tree analysis based knowledge base and neural network ensemble based knowledge base. For the faults easy to form production rules, the fault tree analysis is used to form expert rules. For those difficult to find specific expression between failure mode and fault reason, the neural network ensemble based method is adopt to form a diagnosis model, and it is tested by the simulation examples. Finally, the program language EVC++ under Windows CE is used to develop the fault diagnosis expert system for cars, which had better man-machine interacted interface.

The Valve Electric Actuator’s Fault Diagnosis Method Based on Principle Component Analysis and Support Vector Machines

This paper, the mechanism of valve electric actuator failures is analyzed, and the fault diagnosis method based on principle component analysis (PCA) and support vector machines (SVM) is put forward. In order to reduce the dimension of feature signals, the fault diagnosis method uses PCA to extract fault feature signals. And a multi-class Support Vector Machine classifier is constructed to develop the model of failure diagnosis. The model can identify and detect the actuator’s constant gain fault, the constant deviation fault and the dead zone fault. The simulation experiment result verified its feasibility and validity.