A Self-Learning Diagnosis Algorithm Based on Data Clustering  

作　　者：Dmitry Tretyakov Dmitry Tretyakov(“Technekon” Ltd., Moscow, Russia)

机构地区：[1]“Technekon” Ltd., Moscow, Russia

出　　处：《Intelligent Control and Automation》2016年第3期84-92,共9页智能控制与自动化（英文）

摘　　要：The article describes an approach to building a self-learning diagnostic algorithm. The self-learning algorithm creates models of the object under consideration. The models are formed periodically through a certain time period. The model includes a set of functions that can describe whole object, or a part of the object, or a specified functionality of the object. Thus, information about fault location can be obtained. During operation of the object the algorithm collects data received from sensors. Then the algorithm creates samples related to steady state operation. Clustering of those samples is used for the functions definition. Values of the functions in the centers of clusters are stored in the computer’s memory. To illustrate the considered approach, its application to the diagnosis of turbomachines is described.The article describes an approach to building a self-learning diagnostic algorithm. The self-learning algorithm creates models of the object under consideration. The models are formed periodically through a certain time period. The model includes a set of functions that can describe whole object, or a part of the object, or a specified functionality of the object. Thus, information about fault location can be obtained. During operation of the object the algorithm collects data received from sensors. Then the algorithm creates samples related to steady state operation. Clustering of those samples is used for the functions definition. Values of the functions in the centers of clusters are stored in the computer’s memory. To illustrate the considered approach, its application to the diagnosis of turbomachines is described.

关 键 词：SELF-LEARNING Diagnostics Fault Detection CLUSTERS K-MEANS Turbomachine Gas Turbine Centrifugal Supercharger Gas Compressor Unit 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]