A Bayesian network model for fault diagnosis of a lock mechanism based on degradation data. (April 2021)

• Record Type: Journal Article
• Authors: Pang, Tianyang; Yu, Tianxiang; Song, Bifeng
• Is Part Of: Engineering failure analysis. Volume 122(2021)
• Publication Date: 2021-04
• Subjects: Lock mechanism -- Bayesian network -- Fault diagnosis -- Wear -- K2 algorithm; System failures (Engineering) -- Periodicals; Fracture mechanics -- Periodicals; Reliability (Engineering) -- Periodicals; Pannes -- Périodiques; Rupture, Mécanique de la -- Périodiques; Fiabilité -- Périodiques; Fracture mechanics; Reliability (Engineering); System failures (Engineering); Periodicals; Electronic journals; 620.112
• DOI: 10.1016/j.engfailanal.2021.105225
• Languages: English
• ISSNs: 1350-6307

Abstract

Highlights: The dependence of components is illustrated by the Bayesian network. The K2 algorithm is innovatively used to build the Bayesian network of five joints. An improved Bayesian network model is developed, considering the joints' strong dependence. The scenarios of wear data complete and missing are discussed. Abstract: This paper aims to build a diagnosis model of a lock mechanism system considering multiple joints wear. The lock mechanism is a complex mechanical system. Diagnosis is difficult because fault modes are not easy to identify. The diagnosis result is affected by the strong dependence of wear between each component. Besides, some lock mechanism's wear detection data is challenging to acquire because of fewer sensors in some particular situations. For these problems, an improved Bayesian network-based fault diagnosis methodology considering component degradation is proposed to distinguish the fault types. An experiment of congeneric lock mechanisms is conducted, and wear data of the joints is obtained. The Bayesian networks model in which the dependence of components is not considered is established based on experimental data. Because the Bayesian network structure is affected by the strong dependence between components, the K2 algorithm is used to build the Bayesian network structure based on acquired wear data to obtain causality between components. The entire fault model is built by combining two established Bayesian networks. Three fault diagnosis cases are used to validate the accuracy and efficiency of the proposed model. A comparison is made between the improved diagnosis model and the model without considering dependence. Finally, the revolute joints are ranked by the established diagnostic model so that the weakest component can be identified.