Method for Fault Feature Selection for a Baler Gearbox Based on an Improved Adaptive Genetic Algorithm

Abstract

The performance and efficiency of a baler deteriorate as a result of gearbox failure. One way to overcome this challenge is to select appropriate fault feature parameters for fault diagnosis and monitoring gearboxes. This paper proposes a fault feature selection method using an improved adaptive genetic algorithm for a baler gearbox. This method directly obtains the minimum fault feature parameter set that is most sensitive to fault features through attribute reduction. The main benefit of the improved adaptive genetic algorithm is its excellent performance in terms of the efficiency of attribute reduction without requiring prior information. Therefore, this method should be capable of timely diagnosis and monitoring. Experimental validation was performed and promising findings highlighting the relationship between diagnosis results and faults were obtained. The results indicate that when using the improved genetic algorithm to reduce 12 fault characteristic parameters to three without a priori information, 100% fault diagnosis accuracy can be achieved based on these fault characteristics and the time required for fault feature parameter selection using the improved genetic algorithm is reduced by half compared to traditional methods. The proposed method provides important insights into the instant fault diagnosis and fault monitoring of mechanical devices.

Meta-action unit fault identification based on multisource information fusion and D-S evidence theory

“…Common identification methods include genetic algorithms [2] , expert systems [3] , etc. For example, Bin Ren [4] used an improved adaptive genetic algorithm to select the fault features of the baler gearbox, which reduced the time required for fault feature parameters by half; Fan Liping [5] and others established an intelligent fault identification expert system for a multi-parameter monitor based on fault tree, which greatly improved the accuracy of fault identification. The above technologies are fault identification technologies based on a single piece of information.…”