发布时间：2024-05-31

文章标题：专栏征稿：Digital Twins For Fault Diagnosis, Fault Tolerant Control and Failure Prognosis

内容：

Sensors | Special Issue : Digital Twins For Fault Diagnosis, Fault Tolerant Control and Failure Prognosis (mdpi.com)

Dr. Jinxin Liu

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Guest Editor

School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China
Interests: modeling and system identificaiton; active noise and vibration control; adaptive filtering and control; fault-tolerant control; fault detection and diagnosis of control system; vibration monitoring and signal processing

 

Special Issue Information

Dear Colleagues,

In the field of major equipment, such as aerospace, mechanical manufacturing, shipbuilding, and aero engines, machines operate within a wide range of conditions and state changes. Their safe and efficient operation relies on accurate sensor information. However, since machines work under harsh conditions such as high temperature, high pressure, heavy loads, and strong vibrations, sensors are frequent points of failure. Due to limitations in installation location and weight, it is not feasible to increase hardware redundancy to improve fault tolerance. If the major equipment fails, it will cause huge losses to the national economy and enterprise production, which is a major challenge for system designers to face.

In recent years, the continuous development of digital twin and simulation technology has brought new opportunities to the manufacturing industry. In order to increase the service life and reliability of the equipment, we can consider the use of digital twin technology to achieve fault tolerance control, fault diagnosis and fault prediction of the machine. The following techniques may be required:

1. through physical mechanism, experimental data, neural network and other methods to establish a high-precision digital twin model of the machine;
2. Reliable fault-tolerant control scheme. For example, the digital twin model data is fused with the real operation data of the equipment to improve the redundancy, and the fault tolerance control algorithm is used to enhance the reliability of the system;
3. Advanced fault diagnosis algorithm;
4. Accurate fault prediction method.

This Special Issue invites researchers to contribute original research, case studies, and reviews that address topics related to digital twins for fault tolerant control, fault diagnosis, and failure prognosis.

Dr. Jinxin Liu
Guest Editor