International Journal of Scientific and Research Publications, Volume 3, Issue 11, November 2013 1 ISSN 2250-3153 www.ijsrp.org Proactive condition Monitoring Systems for Power Plants Shameer V. Hameed * , Shameer K. M ** * Department of Mechanical Engineering, S.C.M.S School of Engineering and Technology, karukutty-683582 ** Research at Bharathiar University, Coimbatore Abstract- Power plant condition monitoring systems are pervasive around the industrial space of mechanical systems due to the ever-increasing demand for improved reliability and fail proof operation. Much of the downtime can be obviated with proactive maintenance by measuring vital machine parameters to discover imminent failures. The condition of the turbine and the generator are monitored using the characteristic signatures present in the Acoustic Emanations generated by the vibrating components of the reciprocating parts. The feeble noise of the malfunctioning components will not be detectable at the beginning stages as they are often buried in the noise floor and this creates a major challenge in prognostic reporting of the events that could lead to a catastrophic failure. In this paper an attempt is made to lift and isolate the fault signatures that are hidden behind the noise floor by utilizing Digital Signal Processing Source separation techniques, which is then compared with a pre-collected data base of the different fault stages of the turbine and generator that helps in prognostic report making which in turn eliminates the need of trained professionals for condition monitoring. Implementation of this prototype system will effectively reduce the downtime of power generation along with the elimination of expensive human professional. The collected acoustic data are simulated in MATLAB. Index Terms- Proactive condition monitoring, Noise floor, Classifier, Neural networks. I. INTRODUCTION ower plants productivity depends profoundly on turbines and generators. In order to maximize the power plant efficiency, the rotating parts of the turbines and generators should work with less downtime and maximum throughput. Rotating parts of the power plants passed through a series of subsequent stages before catastrophic failure occurs. The reciprocating parts of the generators produce acoustic emanations during it operations and it varies as it goes through different stages of its lifetime, which is being utilized for monitoring the moving parts of the power plant. Proactive monitoring systems continuously listen to the acoustic emanations of the interested bearings and shafts and helps in making prognostic report for taking necessary actions, which leads to fail proof operation of the plant. The proposed system acquire signals from the power generating machine trains that often buried in the noise floor which is then separated and elevated from the noise using blind source separation, a digital signal processing technique. The source separated fault signals are classified in to corresponding class and the fault stage preceding to failure are identified that gives way for a mechanical engineer to prepare timely report for proactive maintenance. Based on the report the malfunctioning components of the plant can be identified at each stage before it stops and appropriate maintenance schedule can be prepared and carried out without affecting the overall productivity of the plant. Power generation efficiency of a plant is an interesting problem and a lot of capital and human effort is spend on this for maximum fail proof operation. By the implementation of the prototype system human effort in diagnosing, the faulty stages are being reduced drastically. Automated monitoring helps in recognizing the malfunctioning parts at an earlier stage with great accuracy and to eliminate the expense in maintaining an expert engineer for the purpose. II. METHODOLOGY Acoustic emanations from the vibrating parts of the power plant machinery of monitoring interest are acquired, preprocessed and source separated from the background mixture using BSS a signal processing technique. The decomposed signal contains the independent acoustic sources from the entire vibrating components of the power plants being made utilized for the proactive monitoring. The source-separated signals, includes bearing noise, rotor, shaft, piston slap, turbine noise etc. are classified into corresponding class labels using an artificial neural network classifier with the help of pretrained database of the interested components. Classifier with prior knowledge of various fault stages of the components to be monitored categorizes each stage of a component from its normal operation to fault and the present stage of the monitoring component is indicated, which helps in preparing the prognostic report for scheduling the maintenance task that constructively brings down catastrophic failure and down times, resulting in improved performance and efficiency of the power plant. Block diagram of the prototype system is depicted in figure 1. III. SOURCE SEPARATION Sensors attached to the interested parts of the power plant to be monitored, consists of an exotic ensemble of background noises emanating from all parts, that should be source separated for further classification and fault detection. Acoustic emissions from each parts of the machinery have a unique feature set that reveals its identity among the noise mixture are extracted, after the mixture being source separated. Separation of the interested signal sources were accomplished by component analysis for estimating the individual signals. The composite signal mixture P