This paper presents a fault early warning approach of coal mills based on the Thermodynamic Law and data mining. The Thermodynamic Law is used to describe the working characteristics of coal mills and to determine the multi-parameter vector that characterize the operating state of the coal mill. Data mining technology is applied to analysis the interrelationships among elements of the multi-parameter vector. Then the abnormal boundaries of parameters are calculated based on the distribution of parameters under different working conditions according to the Pauta criterion. Finally, the fault early warning model is implemented combining the abnormal boundaries and the confidence algorithm that can detect the working status of coal mills. Two actual numerical examples are used to illustrate the proposed method is capable of estimating the abnormality of coal mills before the fault happens.
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