Abstract
Nowadays, there is an increasing demand for Condition Based Maintenance (CBM) activities as time-directed maintenance are observed to be inefficient in many situations. CBM is a maintenance strategy based on collecting information concerning the working condition of equipment, such as vibration intensity, temperature, pressure, etc., related to the system degradation or status in order to prevent its failure and to determine the optimal maintenance. Prognosis is an important part of CBM. Different methodologies can be used to perform prognosis and can be classified as: model-based or data-driven. Model-based methods use physical models of the process or statistical estimation methods based on state observers, to this approach belong Kalman filters, particle filters, etc. On the other hand, data-driven methods only makes use of the available monitoring data which to train a learning algorithm.
In this paper a data-driven approach is presented to detect abnormal behaviours in industrial equipment. The suggested approach combines two multivariate analysis techniques: principal component analysis (PCA) and partial least squares (PLS). With PCA the most important contributors to characterize the condition of the equipment are found. Next, PLS is used to predict the system state and detect abnormal behaviour. This behaviour can lead to perform maintenance tasks. Finally, an example of application to an asynchronous generator is presented.
