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.
Parboiled rice effluent: A wastewater niche for microalgae and cyanobacteria with growth coupled to comprehensive remediation and phosphorus biofertilization
Abstract The potential of microalgae and cyanobacteria for bioremediation of wastewater by nutrient uptake combined with simultaneous biomass production is a well recognized perception of today's world. The present study illustrates the treatment of a highly polluted wastewater generated during parboiling of paddy in rice mill industries, widely operational in developing countries where rice is the staple food crop, with the help of microalgal and cyanobacterial isolates capable of growing at high rates in parboiled rice mill effluent (RME). This endeavor leads to comprehensive bioremediation of the said effluent and subsequent use of the harvested biomass as slow release phosphorus biofertilizers and the treated effluent for crop irrigation. The RME-acclimatized algal consortium demonstrated highest growth in terms of fresh weight and greatest remediation efficiency at the end of 36 days' treatment of RME, with 93.9% phosphorus and 100% ammonia-nitrogen removal, 98.7%, 91.6% and 93.5% reduction in biological oxygen demand, chemical oxygen demand and total dissolved solid, respectively, and an increment of 186 ± 0.3 mg L − 1 dissolved oxygen, bringing down the pollutants level well below the discharge limits suggested by Central Pollution Control Board, India. Additionally, microalgae in the consortium aggregated in clumps spontaneously in presence of the filaments of Phormidium sp. facilitating easy harvest. The RME-acclimatized algal consortium demonstrated highest accumulation of polyphosphate (poly-P) (0.76 ± 0.01% of dry weight) as well as highest release of phosphorus in non-sterile soil emphasizing the essential role of soil phosphorus solubilizing organisms to leach soluble phosphorus from the insoluble poly-P present in the biomass. The rice seedlings watered with treated RME also showed improved growth effect on shoot height and leaf width. The study results establish the suitability of RME as an excellent growth media for microalgae and cyanobacteria.