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Intelligent Control Technology and Fault Detection Analysis of Mechanical Equipment

This special issue belongs to the section “Physical Sensors“.

Special Issue Information

Dear Colleagues,

With the rapid development of the manufacturing industry, the demand for precise mechanical equipment is increasing, but the traditional mechanical equipment control method struggles to meet the complex working environment. At present, relevant experts are gradually introducing intelligent control technology into mechanical equipment, which helps to improve the working efficiency of machinery and significantly reduce safety accidents.

In the intelligent process of mechanical equipment, the structure of the equipment presents complexity and precision, which means that the mechanical equipment must implement the condition monitoring of the equipment on the premise of safety and reliability, so as to ensure the effective implementation of intelligent equipment.

The purpose of this subject is to promote the construction of mechanical intelligence and monitoring platform, and propose a variety of novel high-quality mechanical equipment fault diagnosis and intelligent control methods. We welcome both original research articles and review articles discussing the current state of the art.

Topics include, but are not limited to:

(1) Fault detection;

(2) Online monitoring, intelligent diagnosis and prediction;

(3) Health condition evaluation;

(4) Dynamic modeling and analysis;

(5) Advanced vibration signal analysis;

(6) Sensor technology;

(7) Automatic control technology.

If you want to learn more information or need any advice, you can contact the Special Issue Editor Andrea Chen via directly.

Dr. Haiyang Pan
Prof. Dr. Haidong Shao
Prof. Dr. Jinde Zheng
Prof. Dr. Qingyun Liu
Guest Editors

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • signal processing
  • fault diagnosis
  • automatic control
  • feature extraction
  • sensor technology

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Sensors - ISSN 1424-8220