A novel cross-receptive field fusion cascade network with adaptive mask update for transfer health state diagnosis of manipulators

Abstract

Manipulators, particularly planar parallel manipulators, are widely employed in high-end precision equipment to conduct precise positioning and operation tasks due to their advantages of high stiffness, high precision, and high load. Moreover, they are also frequently exposed to changeable working circumstances, which significantly cause inconsistent health state data distribution. Although transfer learning can successfully offset the above distribution discrepancies, it remains unclear how to identify and quantify the source domain knowledge’s contribution to the transfer process. To overcome these challenges, a novel transfer health state diagnosis framework, named cross-receptive field fusion cascade network with adaptive mask update (CFFCN-AMU), is developed and employed for manipulators. Specifically, a unique cross-receptive field fusion cascade module (CFFCM), in which the receptive field self-evaluator and channel attention mechanism are jointly designed, is constructed initially to achieve adaptive extraction and fusion of cascaded features. Subsequently, in the target domain fine-tuning stage, an adaptive mask update (AMU) strategy is implemented to evaluate the contribution of source domain knowledge and selectively guide the parameter updating process. Finally, some mechanistic model-driven cross-working condition transfer scenarios are investigated. Multiple sets of excellent transfer diagnosis results fully illustrate the transferability and superiority of the constructed CFFCN-AMU model.