New fault zero sample identification of equipment based on feature attribute description

Under the background of automation upgrading, the coupling between equipments is constantly improving, and the fault manifestations are complex and diverse. Failure to deal with a single fault in a timely manner is likely to expand the scope of influence, which further escalates the accident. In order to ensure the normal operation of the equipment, new requirements are put forward for the traditional fault diagnosis methods based on case analysis, including low cost, long-term monitoring, small sample or zero sample fault identification. To meet these new requirements, this paper proposes to introduce the zero sample classification recognition idea used in the image processing field into the fault diagnosis field. By studying the characteristic parameters of the existing fault samples, the characteristics used for condition monitoring are determined through optimization. The fuzzy neural network is used to form a feature attribute descriptor, which describes the characteristics as equipment attributes. Based on the attribute description, the ART network conducts incremental learning for long-term monitoring of equipment at the same time. That is to build a monitoring and learning mechanism based on the analysis of a small number of equipment samples or similar samples, identify the original faults and learn and record new faults at the same time. In order to illustrate the feasibility and effectiveness of this method, this paper uses a small number of motor fault data sets as prior knowledge to build a system, and mixed unknown fault samples for system testing. The experimental results show that the application of zero sample classification is expected to solve the new challenges of equipment fault diagnosis under the current technical background.