The Research Of Information Equipment Health Management And Fault Prognosis Based On Cloud Model

With the rapid development of information technology and the increasingly high level of Enterprises information, Information equipment, as an important support for information technology, is very important. To implement condition-based maintenance to Information equipment, is an important means to ensure the normal operation of the enterprise information technology equipment. Health management and Fault prognosis for information equipment as an important part of condition-based maintenance, the research has important practical significance.Cloud model as an effective tool for the conversion of qualitative index and quantitative index, It simultaneously taking into account the randomness and fuzziness variables of index. It can also be used to solve the uncertainty problem in the information equipment health management and fault prediction. By analyzing existing equipment health management and fault forecasting techniques, this study found the deficiencies of evaluation of qualitative indicators, And proposed a evaluation method based on the cloud model for the health assessment of information equipment. And proposed a method of information equipment failure prognosis based on cloud model and least squares support vector machine(LS-SVM),so can achieve the condition-based maintenance for Information equipment by combining the health assessment index and the analysis of fault prognosis. Meanwhile, through the experiment,It verify the good usability of cloud model in the health management and fault prognosis for information equipment.Finally, the Information Devices Condition Based Maintenance (IDCBM) system has been designed and achieved parts of modules.The key functional modules of the system has been designed in detail, And the professional work flow chart and the data flow diagram of system has been created, And the database system has been designed in detail, finally, achieved the health management and failure prognosis of information equipment, providing decision support advice to condition-based maintenance.