LabVIEW BASED CONDITION MONITORING OF INDUCTION MACHINE USING INTERNET PROTOCOL

Abstract

In this work a remote condition monitoring system using LabVIEW is developed and also study of stator current and vibration signal analysis has been done to detect both outer and inner race faults in roller bearings of induction motor. The investigation has been carried out on a laboratory machine at different load conditions. A software utility, actually a set of graphically driven procedures that run under the LabVIEW environment, was developed to reinforce and illustrate the signal processing techniques used in bearing diagnostics. The remote condition monitoring systems enables to monitor the health of the remotely placed Induction motor. A GUI based VI is developed for the data acquisition at the server side. Along with the data acquisition data transmission to the client has also been done using Transfer Control/Internet Protocols in the LabVIEW environment. Defective rolling element bearings generate eccentricity in the air-gap with mechanical vibrations. The air-gap eccentricities cause variations in the stator air-gap flux density that produces visible changes in the stator current spectrum. This is why we use Motor Current Signature Analysis to detect bearing faults along with vibration spectrum; in addition to this Motor Current Signature Analysis is a noninvasive method. In many industrial applications it becomes difficult to access vibration signal and since Motor Current Signature Analysis uses the induction motor as an efficient transducer it greatly contributes for fault detection. In this work wavelet packet decomposition technique is used for the current and vibration spectrums to get fine resolution; along with this auto-correlation is applied for both the signals to eliminate the randomly varying high frequency coefficients from the noise signals. Motor current signature analysis provides a nonintrusive way to assess the health of a machine. The steady-state current of an induction motor is analyzed via the discrete wavelet packet transform to detect faulty bearings in this study. III This chapter also deals with the results obtained for the laboratory set up. The results comprises of stator current spectrum and vibration spectrum through which health of the machine can be diagnosed remotely. CHAPTER 5 CONCLUSION This chapter gives the conclusion of the work done.