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Corresponding Author

El-Rammal, Hassan

Subject Area

Mechanical Power Engineering

Article Type

Original Study

Abstract

At any production process, a significant part of the total cost has been expended during the maintenance of the operated equipment. To control this maintenance cost; choosing certain, clear, and effective policy is an essential for increasing industrial productivities, minimize related costs, and to ensure plant reliability and equipment availability. Predictive maintenance policy for rotating equipment had been used in the machinery fault diagnosis. This research introduces detailed elucidation for this methodology as a case study of an industrial air blower located in urea fertilizer production plant at Ain-Sokhna, Egypt, acting as an air atomization source required for the formation process of urea granules. It is considered as the main and more critical equipment in the production facilities and its breakdown leads to complete stoppage of the production process. The proposed methodology has been organized in certain phases; starting with the preparation of measuring instrumentation for data acquisition. Data gathering from various determined locations at the blower and drive electric motor bearings in different directions was performed. After that, feeding acquired data to the supporting vibration database analysis software on PC and followed it up periodically till high values were detected. Vibration data analysis performed on the basis of spectral plot, frequencies against amplitude. The spectral analysis was used to analyze blower faults and predict their causes and consequences to determine the time of machine shutdown and maintenance. Vibration overall values and spectrum compared on initial conditions, troubleshooting, and after carrying-out corrective action; also referred to vibrations severity standard according to the international standard (ISO-10816-1). From this study; effectiveness of diagnostic and prognostic methodology was verified to be applied for other rotating equipment.

Keywords

Predictive Maintenance; condition monitoring; Vibration analysis; Air Blower

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