Data-Driven State Prediction and Sensor Fault Diagnosis for Multi-Agent Systems with Application to a Twin Rotational Inverted Pendulum

Processes

卷:9

Jie Zhong¹  Bowen Li²  Xin Lu³  Xiaoxu Liu³ 

[1] College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua 321004, China;

[2] School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China;

[3] Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China;

关键词: data-driven;    state prediction;    fault classification;    multi-agent system;   

DOI  :  10.3390/pr9091505

来源: DOAJ

【 摘 要 】

When a multi-agent system is subjected to faults, it is necessary to detect and classify the faults in time. This paper is motivated to propose a data-driven state prediction and sensor fault classification technique. Firstly, neural network-based state prediction model is trained through historical input and output data of the system. Then, the trained model is implemented to the real-time system to predict the system state and output in absence of fault. By comparing the predicted healthy output and the measured output, which can be abnormal in case of sensor faults, a residual signal can be generated. When a sensor fault occurs, the residual signal exceeds the threshold, a fault classification technique is triggered to distinguish fault types. Finally, the designed data-driven state prediction and fault classification algorithms are verified through a twin rotational inverted pendulum system with leader-follower mechanism.

【 授权许可】

Unknown