Exploring self-organization and self-adaption for smart manufacturing complex networks

Trends toward the globalization of the manufacturing industry and the increasing demands for small-batch,short-cycle,and highly customized products result in complexities and fluctuations in both external and internal manufacturing environments,which poses great challenges to manufacturing enterprises.Fortunately,recent advances in the Industrial Internet of Things(IIoT)and the widespread use of embedded processors and sensors in factories enable collecting real-time manufacturing status data and building cyber–physical systems for smart,flexible,and resilient manufacturing systems.In this context,this paper investigates the mechanisms and methodology of self-organization and self-adaption to tackle exceptions and disturbances in discrete manufacturing processes.Specifically,a general model of smart manufacturing complex networks is constructed using scale-free networks to interconnect heterogeneous manufacturing resources represented by network vertices at multiple levels.Moreover,the capabilities of physical manufacturing resources are encapsulated into virtual manufacturing services using cloud technology,which can be added to or removed from the networks in a plug-and-play manner.Materials,information,and financial assets are passed through interactive links across the networks.Subsequently,analytical target cascading is used to formulate the processes of self-organizing optimal configuration and self-adaptive collaborative control for multilevel key manufacturing resources while particle swarm optimization is used to solve local problems on network vertices.Consequently,an industrial case based on a Chinese engine factory demonstrates the feasibility and efficiency of the proposed model and method in handling typical exceptions.The simulation results show that the proposed mechanism and method outperform the event-triggered rescheduling method,reducing manufacturing cost,manufacturing time,waiting time,and energy consumption,with reasonable computational time.This work potentially enables managers and practitioners to implement active perception,active response,self-organization,and self-adaption solutions in discrete manufacturing enterprises.

A novel minority sample fault diagnosis method based on multisource data enhancement

Effective fault diagnosis has a crucial impact on the safety and cost of complex manufacturing systems.However,the complex structure of the collected multisource data and scarcity of fault samples make it difficult to accurately identify multiple fault conditions.To address this challenge,this paper proposes a novel deep-learning model for multisource data augmentation and small sample fault diagnosis.The raw multisource data are first converted into two-dimensional images using the Gramian Angular Field,and a generator is built to transform random noise into images through transposed convolution operations.Then,two discriminators are constructed to evaluate the authenticity of input images and the fault diagnosis ability.The Vision Transformer network is built to diagnose faults and obtain the classification error for the discriminator.Furthermore,a global optimization strategy is designed to upgrade parameters in the model.The discriminators and generator compete with each other until Nash equilibrium is achieved.A real-world multistep forging machine is adopted to compare and validate the performance of different methods.The experimental results indicate that the proposed method has multisource data augmentation and minority sample fault diagnosis capabilities.Compared with other state-of-the-art models,the proposed approach has better fault diagnosis accuracy in various scenarios.