State space models for condition monitoring: a case study

Abstract A Condition Monitoring system can increase safety, quality and availability in industrial plants. Safety requirements are especially important in critical machineries, like a turbine driving a centrifugal compressor located at a petrochemical plant in the case study presented in this paper. A Condition Monitoring system is set up for vibration data coming from the turbine. Four years of monthly data observed at two different locations of the equipment are analysed. The core of the system is a model to forecast the state of the machine using data provided by the Condition Monitoring system at each moment in time. The model is based on the State Space framework whose associated recursive algorithms (Kalman Filter and Fixed Interval Smoothing) provide the basis for a number of different operations, from which the most important in the present context is the extrapolation of the distribution of forecasts on which the probability of failure is estimated. The cost model on which the decision of making a preventive replacement is taken is based on the ‘expected cost per unit time’ for a pre-determined critical value of the vibration measure. The system is thoroughly tested on the data.

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