Research On Online Monitoring And Fault Intelligent Diagnosis Method For Caster Ladle Turret

The slewing bearing, as an important rotating component of the caster ladle turret,its safety and stability is essential for the normal operation of the ladle turret. In order toavoid the ladle turret operation failure, an efficient and accurate diagnosis of anomaliesoccur during the slewing bearing operation is required. Since the slewing bearinglong-term works in the low-speed and heavy-load operating states, the traditionalbearing fault diagnosis method is not applicable, therefore a reliable slewing bearingonline monitoring and fault diagnosis system needs to be developed. The main researchworks are as follows.(1)The overall structure of the ladle turret was analyzed to set the slewing bearingas the main target to monitor its operation states. Then the vibration, displacement andelectric power signals were selected as the monitor objects and diagnostic parameters byanalyzing the main damage forms of the slewing bearing.(2)The overall program of a caster ladle turret online monitoring and faultdiagnosis system was designed. Moreover, a comprehensive and reasonable solutionprogram of problems such as the monitoring physical quantities, the measuring pointsdistribution, the data collection methods, the analytical methods, the overall structureand the function modules of the software system were proposed.(3)With the test sample s extracted from the collected data, the three methods offuzzy diagnosis, neural network and fuzzy neural network were used for fault diagnosis.By analyzing and comparing the results, the superiority of fuzzy neural network, whichsignificantly improved the accuracy of slewing bearing fault diagnosis and recognition,was proved.(4)The caster ladle turret online monitoring and fault diagnosis system wasdeveloped and applied to a steel mill steel company successfully. The system was stableand the intended design goals were achieved.