Research On Derrick Damage Identification Based On Mode Parameter And Intelligent Analysis

Research on derrick damage identification is finding out the reasons that caused the system maladjusted and judging the places and components where the faults happened. The technology of fault diagnosis is a new subject, which has developed for a lot of years. It has very important practical value. In recent years, in order to deal with the more and more complicated systems, with the development of computer and artificial intelligence, the technology of fault diagnosis comes into another phase——intelligent fault diagnosis. It regards knowledge management as the kernel and combines signal processing and knowledge management.Firstly, this article expatiates the study actuality and developing current of configuration-damage-discrimination technique, introduces various theories and methods of configuration-damage-discrimination technique, and balances these methods when applied in damage discrimination. Secondly, the paper analyze the test method and mode parameter with the model experiments, and using engineering data to analyze modal derrick test, then finding the result of the derrick damage identification first. based on BP neural network that of good using, the application of BP neural network in the fault diagnosis of research on derrick damage and the design of intelligent fault diagnosis system using BP neural network are introduced in this paper. According to the obtained operational parameter from the structural derrick and the fault symptom table in knowledge base, the system searches every generating fault symptom. Based on the rule table it can find all the possible fault causes. Verifying each possible fault cause and comparing the other fault symptom in it, thereby it infers the diagnosis result.The study of this method is inosculated with engineering, research on the project closely linked with engineering practice, the techniques of vibration modal analysis and intelligence analysis combined. Signature for damage detection of the vibration mode as input characteristic parameters used for identifying neural network, in order to conduct structural health monitoring.