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.
Vibration feature analysis for rolling element bearing diagnosis using Bayesian neural networks
Condition monitoring in some form, is becoming an almost routine activity in many industries and is a key technology for machinery maintenance. Of the various techniques used, vibration monitoring is arguably the most popular given its direct ability to detect and diagnose faults in machinery. The vibration monitoring procedure comprises data acquisition; signal processing; condition assessment; and decision making. Extracting effective features plays a critical role in this procedure, since these features can indicate changes in machinery condition and track fault progression. Various features in both time and frequency domains can be extracted from the basic data by applying statistical analysis and signal processing. The sensitivities of the features have however not been compared. It is now well accepted that using multiple features can enhance condition assessment. It would be best if an optimal feature combination can be selected. This research examines feature analysis issues using a SpectraQuest machinery fault simulator. Vibration signals were collected from rolling element bearings under four conditions, healthy, inner race fault, outer race fault and ball fault. Sixteen vibration features extracted from both time and frequency domains were investigated for bearing diagnosis. The individual features were examined against their separation capabilities using discrimination measures. A Bayesian Neural Network (BNN) was then used to automatically determine the relevance of each feature. This research clarifies the sensitivity of individual vibration features and provides an approach for selecting optimal feature combination for rolling element bearing fault diagnosis.
The Contemporary British History Play
Introduction Biographical History Plays Social History Plays Oppositional History Plays Marxist and Socialist History Plays Feminist History Plays Deconstructionist and Postmodern History Plays and Satire The Search for a Theatrical Form Appendix A: Performance Dates of Contemporary British History Plays Appendix B: Performance Locales of Contemporary British History Plays Bibliography Index