ICSV 14 Cairns • Australia 9-12 July , 2007 THE INVESTIGATION OF SUBMERGED VERTICAL PUMPS IN A POWER PLANT USING VIBRATION BASED CONDITION MONITORING TECHNIQUE
Condition monitoring has received considerable and wide attentions through most of the industries employing rotating machinery. It is increasingly becoming vital in industry because of the needs to prolong the machine life, increase reliability and decrease possible loss of production due to faulty machines. Vibration monitoring is the most popular technique, given its direct ability to detect and diagnose faults in machinery, which it is typically found in large industries such as oil and gas, petrochemical and power generation plant. This paper presents the field investigations of submerged vertical pumps with suspected cavitation in a power plant. Frequent impeller failures of submerged vertical pumps would potentially result in the total plant shutdown without warning. Vibration analysis was undertaken on the pump of concern in assist plant personnel in making decision on its operations. The predictive condition monitoring for the pump in the investigation include the vibration monitoring and a simple statistical analysis based on monthly overall vibration level, which is useful for reasonably accurate diagnosis in actual commercial pump in power plant. High frequency vibration was shown to be a good indicator of cavitation. It was shown that the investigations implied a wrong setting of the adjusting screw caused the large gap between the impeller and the casing and therefore enabling the seawater to slip back to the pit and hit the impeller and also the pump operating conditions that had resulted to the impeller failures.
Development of a predictive maintenance system for a centrifugal pump
An approach is presented for the development of a predictive maintenance system for rotor‐dynamic pumps, which focuses on the diagnosis of abnormal events related to fluid‐dynamic operating conditions. This methodology is based on an experimental characterization of the dynamic response of the pump under different loads and operation anomalies. The procedure has been put into practice on a medium‐sized centrifugal pump. The results obtained show that a simple spectral analysis of the pressure signals captured at either the inlet or the outlet of the pump can provide sufficient decision criteria to constitute the basis for a diagnostic system. This was not true however when analyzing signals of acceleration at the pump casing.
Fault features of large rotating machinery and diagnosis using sensor fusion
Abstract Large rotating machinery such as turbines and compressors are the key equipment in oil refineries, power plants, and chemical engineering plants. To minimize the economic loss incurred because of the defects of malfunctions of these machines, diagnosis is very important. Currently, diagnosis is carried out mainly using spectral analysis. In spite of being effective in detecting the faults (monitoring), spectral analysis is often ineffective in pin-pointing what the fault is (diagnosis). This is due to the fact that it cannot clarify the spatial and temporal features in the sensor signals that are correlated to different types of faults. In this paper, phase spectra, holospectra, purified orbit diagrams, and filtered orbit diagrams are used in searching for fault features. From the data obtained from more than 50 practical machines, distinct fault features and diagnostic induces are found for 11 different types of faults including unbalance, cracks, misalignment, rub, loose bearing caps, oil whirl, surge, fluid excitation, rotating stall, electric power supply fluctuation, and pipe excitation. Accordingly, a diagnostic procedure is proposed.