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Five-plunger pumps are widely used in oil field to recover petroleum due to their reliability and relatively low cost. Petroleum production is, to a great extent, dependent upon the running condition of the pumps. Closely monitoring the condition of the pumps and carrying out timely system diagnosis whenever a fault symptom is detected would help to reduce the production downtime and improve overall productivity. In this paper, a rough set approach of mechanical fault diagnosis is proposed to identify the five-plunger pump faults. The details of the approach, together with the basic concepts of the rough sets theory, are presented. The rough classifier is a set of decision rules derived from lower and upper approximations of decision classes. The definitions of these approximations are based on the indiscernibility relation in the set of objects. The spectrum features of vibration signals are abstracted as the attributes of the learning samples. The minimum decision rule set is used to classify technical states of the considered object. The diagnostic investigation is done on data from a five-plunger pump in outdoor conditions on a real industrial object. Results show that the approach can effectively identify the different operating states of the pump

A Rough Set Approach of Mechanical Fault Diagnosis for Five-Plunger Pump