Data Quality Assurance Techniques for a Monitoring and Diagnosis System

By researching the data quality problem in the monitoring and diagnosis system （MDS） , the method of detecting non-condition data based on the development trend of equipment condition is proposed, and three requirements of criteria for detecting non-condition data： dynamic, syntheses and simplicity are discussed. According to the general mode of data management in MDS, a data quality assurance system （DQAS） comprising data quality monitoring, data quality diagnosis, detection criteria adjusting and artificial confirmation is set up. A route inspection system called MTREE realizes the DQAS. Aiming at vibration data of route inspection, two detecting criteria are made. One is the quality monitoring parameter, which is found through combining and optimizing some fundamental parameters by genetic programming （GP）. The other is the quality diagnosis criterion, i. e. pseudo distance of Spectral-Energy-Vector （SEV） named Adjacent J-divergence, which indicates the variation degree of adjacent data＇s spectral energy distribution. Results show that DQAS, including these two criteria, is effective to improve the data quality of MDS.

A Study on a Remote Monitoring and Diagnosis System and Its Application

Remote monitoring and diagnosis （RMD） is a new kind of monitoring and diagnosis technology that combines computer science, communication technology and fault diagnosis technology. Via the Internet a remote monitoring and diagnosis system can be established. In this paper, the model of an Internet based remote monitoring and diagnosis system is presented; the function of every part of the RMD system is discussed. Then, we introduce a practical example of a remote monitoring and diagnosis system that we established in a factory; its traits and functions are described.

STUDY OF REAL-TIME EXPERT SYSTEM TOOL FOR INDUSTRIAL FAULT MONITORING AND DIAGNOSIS

From the requirements of industrial production,an integrated fault monitoring,diagnosis and repairing system is suggested in this paper. This new scheme of fault monitoring and diagnosis system is realized by a master-slave real-time expert system,and a real-time expert system tool for this system is also developed accordingly. As an example of application of this tool ,a realtime expert system for fault monitoring and diagnosis on DC mine hoist is developed. Experiments show that this tool possesses better supporting environment, strong knowledge acquisition ability, and convenience for use. The system developed by this tool not only meets the real-time requirement of DC hoist,but also can give correct diagnosis results.

Research on remote hydro-generator sets diagnosis system

A type of remote monitoring and diagnosis system is brought forward which based on Matlab Web Server.Firstly,wavelet packet decomposition is introduced to acquire energy features of which reflect hydrogenerator sets performance to be Feature Parameter.Then these Feature Parameters can be adopted as BP Neural Network input variable to realize fault diagnosis.Most of all,it is the first time to adopt Matlab Web Server to hydro-generator sets faults diagnosis field to implement distributed remote monitoring and diagnosis system.Therefore,remote diagnosis application is independent from the OS used on server side.There is no need for software maintenance by clients.And clients can finish remote diagnosis by Web Browser and without installation of Matlab-software.Client users can monitor and diagnose hydro-generator sets by Browser.Finally,further research work is pointed out such as hydro-generator sets fault modeling,accelerating BP Neural Network learning speed and convergence property,improving data transfer speed of Matlab Web Server to meet the needs of real-time diagnosis for hydropower generator sets.