Research On Condition Monitoring And Failure Warning System For Steam Turbine Generator Unit

As major accident of the turbine generator frequently happened in all areas of world-wide industrial, not only which caused great losses to the social and economic development, but also gave a early warning to safety in producing and using large-scale complex equipments in China. And how to ensure stability and efficient operation for large-scale complex equipments becomes a primary condition in the process of industrial intelligent and automated transformation. And while it put forward higher requirements for safety monitoring capabilities of the large turbine generator in China. At present, challenges are faced in China, for example energy efficiency, system operation efficiency and resources utilization should be enhanced urgently, and environment quality needed to be improved etc. As well, some new opportunities of industrial intelligent application came out, such as many concepts and technology and product need to be improved. In this paper, relevance among things is fully recognized, and which among turbo-generator security and stability state is studied from multi-layer. Based on the study of the classification of typical failure modes and the optimization for failure symptoms, the research of failure warming can be mainly from three sides, such as failure range, failure properties and the failure probability. In this paper, it includes some key technologies as following, such as abnormal search for signs, property identification and risk probability:(1) Study on the failure characteristics symptoms optimization method based on rough set. Based up on the statements of typical failure mode, the classifications of failure symptoms are the failure range symptom which can reflect where the failure happened, the failure property which can reflect the failure development property, and the failure intensity symptoms which can reflect how frequent the failure is. And then this paper provides the resolved approach for failure symptoms analysis. In this paper, sequence pattern is defined, and both the symptoms online and offline can be quantization and reduction. In order to avoid the complexity of characteristic symptoms, with symptoms importance index, the failure characteristics symptoms can be optimized and reduced. Finally, synthetically considering the types of failures, valuable failure symptoms set can be proposed.(2) Study on the K-distance abnormal search method for turbo-generator based on multi-characteristic symptoms model. Based on the analysis of the turbo-generator monitoring parameters, it was first proposed the analysis method for time series of failure range symptom parameter, by use of time series segmentation technology, time series management techniques and time series abnormal search technology. It takes the sub-model of time series as the search rules, and with the K-distances abnormal search method it can search the function index composed of abnormal time series. Establishing the early warning mechanism and the failure range forecast can be realized.(3) Study on the identification method based on the gray weighted-AR combination forecasting method and multi-character state method. In contrast to the typical forecasting methods, this paper proposed the combination forecasting model based upon gray-weighted-AR theory, to predict the symptoms parameters that can reflect the development of failure properties. In order to avoid prediction misjudgment with a single parameter it defines the concept of a free state space and a benchmark state space based upon the state space theory. And multi-character recognition model is established, and also the law of the state space is provided. By this method, it solves the inaccuracy for failure trend forecast during turbine generator condition monitoring process, so the failure property can be determined more accurately. All these can provide the basis guiding for turbo-generator condition monitoring.(4) Study on the failure probability calculation method based on the identification classification logic regression. Based on the analysis of the development degree recognition for typical failures, by principle of logistic regression, the corresponding historical sample, which can reflect the failure probability, is comprehensively analyzed. And then it standardized expression pattern of characteristic parameters and established the corresponding regression model. By use of maximum likelihood function method, the failure probability regression model can be calculated out, finally with the characteristic parameter values obtained by the current monitoring the failure occurrence probability may come out. In this paper, it also set up the query mechanism for failure measures.Finally, in the above theory, under the guidance of UCML technology platform, software platform of turbine generator condition monitoring and failure warning system is designed and developed.