Abstract
In this paper a forecasting system in condition monitoring is developed based on vibration signals in order to improve the diagnosis of a certain critical equipment at an industrial plant. The system is based on statistical models capable of forecasting the state of the equipment combined with a cost model consisting of defining the time of preventive replacement when the minimum of the expected cost per unit of time is reached in the future. The most relevant features of the system are that (i) it is developed for bivariate signals; (ii) the statistical models are set up in a continuous-time framework, due to the specific nature of the data; and (iii) it has been developed from scratch for a real case study and may be generalised to other pieces of equipment. The system is thoroughly tested on the equipment available, showing its correctness with the data in a statistical sense and its capability of producing sensible results for the condition monitoring programme.
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