On Line Fault Identification of Induction Motor using Fuzzy System

Transcription

1 On Line Fault Identification of Induction Motor using Fuzzy System 1 D. K. Chaturvedi, 2 Akash Gautam, 3 Mayank Pratap Singh, 4 Md. Sharif Iqbal Dept. of Electrical Engineering, Faculty of Engineering, Dayalbagh Educational Institute, Agra , India. dkc.foe@gmail.com Abstract- It is well known that Induction motors are used worldwide as the workhorse in industrial applications. Although, these electromechanical devices are highly reliable, they are susceptible to many types of faults. Such fault can become catastrophic and cause production shutdowns, personal injuries and waste of raw material. However, induction motor faults can be detected in an initial stage in order to prevent the complete failure of an induction motor and unexpected production costs. The motive of this project is to analyse the fault in induction motor through sound and electrical signature produced during the specific fault existing in the induction motor and then to analyse it through various technique. In this paper, a method for mechanical and electrical fault diagnosis in induction motor through sound and electrical signature analysis has been proposed. The work reported in this project uses noninvasively method for sound signature for diagnosing different mechanical faults. For the electrical fault diagnosis the current signature of 3-phase induction motor has been recorded. The recorded sound signature and current signature of the faulty induction motor and a healthy induction motor during different faults have been analysed using Fourier transform. The magnitude and frequency of FFT of these signatures have been used for identification of different faults using fuzzy system. Keywords Induction motor, fuzzy logic I. INTRODUCTION INDUCTION motors are electro-mechanical devices utilized in most industrial applications for the conversion of power from electrical to mechanical form. Induction motors are used worldwide as the workhorse in industrial applications. Such motors are robust machines used not only for general purposes, but also in hazardous locations and severe environments. General purpose applications of induction motors include pumps, conveyors, machine tools, centrifugal machines, presses, elevators, and packaging equipment. On the other hand, applications in hazardous locations include petrochemical and natural gas plants, while severe environment applications for induction motors include grain elevators, shredders, and equipment for coal plants. Additionally, induction motors are highly reliable, require low maintenance, and have relatively high efficiency. Moreover, the wide range of power of induction motors, which is from hundreds of watts to megawatts, satisfies the production needs of most industrial processes. However, induction motors are susceptible to many types of fault in industrial applications. A motor failure that is not identified in an initial stage may become catastrophic and the induction motor may suffer severe damage. Thus, undetected motor faults may cascade into motor failure, which in turn may cause production shutdowns. Such shutdowns are costly in terms of lost production time, maintenance costs, and wasted raw materials. The motor faults are due to mechanical and electrical stresses. Mechanical stresses are caused by overloads and abrupt load changes, which can produce bearing faults and rotor bar breakage. On the other hand, electrical stresses are usually associated with the power supply. Induction motors can be energized from constant frequency sinusoidal power supplies or from adjustable speed ac drives. This paper proposes a mechanical fault and electrical diagnosis of induction motor system using sound signature and current signature analysis. II. THE PROPOSED SYSTEM The Proposed method is for diagnosis of mechanical and electrical faults in 3-phase induction motor through electrical and sound signal analysis. It is a complete fault detection system. Through this method the mechanical fault prevailing in the induction motor can be diagnose, and for electrical faults the electrical current waves during different faults are also recorded. The main advantage of this report is a noninvasive fault diagnosis in induction motor. An experiment on 3-phase induction motor for fault diagnosis was conducted to record sound signal of the faulty induction motor and a healthy induction motor then we analyze it through the use of software i.e. MATLAB 7.5 by using Fourier transform technique. After the analysis of the captured data it should be compared using fuzzy system and then final results will be made. III. EXPERIMENTAL SETUP The experimentation done in Electrical Engineering lab at Dayalbagh Educational Institute (Deemed University) Agra, India. The experiment consisting of two induction motor (one is healthy IM and other is faulty IM), A laptop preloaded with MATLAB software and a sound recorder. Induction motor specifications are 240V, 50 Hz, 500W. The real time data is acquired with the help of recorder from both the IM one by one and sends them to computer. MATLAB imports these signals and signal analysis using FFT is done shown in fig

2 The estimated parameters compared with the healthy IM under normal operating conditions Fig. 2: Healthy bearing IV. FLOW CHART FOR PROPOSED METHOD A flow chart for proposed method is shown below in fig.1 A. SYSTEM For the proposed method we have a system i.e. induction motor. There are two identical induction motor out of which in one induction motor there no fault exist i.e. it is a healthy induction motor and another induction motor in which fault is there. Healthy and faulty induction motor has been shown in fig 2 and fig 3. B. SIGNAL ACQUISITION Fig. 3: Faulty bearing In signal acquisition a signal is acquired from the system. Firstly the signal is acquired from the healthy induction motor by using sound recorder then similarly the signal is acquired for the faulty induction motor through sound recorder. Fig. 4a: Original healthy signal Fig.1: Flow chart 965

3 Fig. 5b: nfft of faulty induction motor Fig. 4b: Original Faulty signal C. DATA PRE-PROCESSING In data pre-processing stage the signal is prepared for further analysis process i.e. if the signal is need to be amplified or the conversion of signal to an appropriate format for further processing. D. SIGNAL ANALYSIS After the data pre processing part the signal analysis is done. In signal analysis the signal which is obtained after pre processing, it is analysed through the use of MATLAB software by taking Fourier transform. Various diagrams whose waveforms have been analysed are as follows. 1. Fast Fourier Transform. 2. Periodogram. PERIODOGRAM A plot of power versus frequency is called a periodogram. E. EXPERIMENTAL RESULTS AND COMPARISON The results obtained after signal analysis of healthy and faulty system have been compared to identify faults. It is clearly seen in FFT of the healthy induction motor there are many peaks out of which two major peaks are of approx and amplitude at approximate 555 and 832 Hz frequency respectively. Similarly in faulty induction motor there are two major peaks are of approx and 1128 amplitude at approximate 927 and 790 Hz frequency respectively. These are the characteristics of healthy and faulty induction motor signal. Similarly in the periodogram there is one major peak between 0 to 5 Hz whereas in faulty induction motor along with that sharp peak there are some distinct spikes between 5 to 10 Hz. V. EXPERIMENTAL SETUP FOR RECORDING SOUND SIGNALS DURING ELECTRICAL FAULTS The experiment consisting of 3-phase induction motor, a laptop preloaded with MATLAB software and a sound recorder. The real time data is acquired with the help of recorder from IM under different fault conditions and then they are sends to computer for further analysis. MATLAB imports these signals and signal analysis using FFT is done then the estimated parameters compared with the healthy IM under normal operating conditions. Fig.5a: afft of healthy induction motor Fig. 7: Setup in Electrical Machines Lab, DEI Dayalbagh, 966

4 A. Healthy Sound Signal in Time Domain 1. Star connected C. FFT of the signal when 1/3 Coil Is Short Circuited 1. Star Connected 2. Delta connected 1) 2. Delta Connected B. Healthy Sound Signal in Frequency Domain 1. Star connected D. FFT of the signal in single phasing fault 1. Star Connected 2. Delta connected 2. Delta Connected 967

5 VI. COMPARISON OF EXPERIMENTAL RESULTS TABLE I: Frequency and amplitude during different faults in star connection C. Rule Base: If the highest amplitude lies in low amplitude and at medium frequency range then it is healthy motor in star connection. TABLE.II: Frequency and amplitude during different faults in delta connection If highest amplitude lies in medium amplitude and at low frequency range then it is healthy motor in delta connection. If the highest amplitude lies in high amplitude and at low frequency range then it is single phasing fault. If the highest amplitude lies in medium amplitude and at medium frequency range then it is short circuit fault in star connected induction motor If highest amplitude lies in high amplitude and at medium frequency range then it is short circuit fault in delta connected induction motor. VII. FUZZY SYSTEM FOR IDENTIFYING FAULTS Incoming FFT signals are fuzzified. The sound signals were given to fuzzy system for identifying the different faults based on different amplitude and frequency in 3- phase induction motor. D. Defuzzification: Then defuzzification will be done using centre of area method. VIII. EXPERIMENTAL SETUP FOR RECORDING ELECTRICAL SIGNATURE The experimentation is done in Electrical Engineering Lab Faculty of Engg. at Dayalbagh Educational Institute (Deemed University) Agra, India. A. Fuzzification: Input to the fuzzification block is frequency and amplitude of different signals which should be recorded during electrical faults which are fuzzified. B. Membership Function: Let FREQ and AMP be linguistic variables with the label frequency and amplitude. The unit of frequency is hertz. FREQ= [Low, Medium, High] Fig.8: setup in Electrical Machines Lab, DEI, Dayalbagh A. 3-phase Induction Motor specifications: Rating- 3 HP, 50Hz 3-phase, 4 pole, 1500 R.P.M, 400/440volts. Total No. of coils or slots 36. Coils/pole-9. AMP = [Low, Medium, High] Coils/pole/phase-3 968

6 IX. STATOR WINDING It consists of conventional squirrel cage rotor and a stator with 36 slots and wound in double layer with 36 multiturn coils, all the 72 ends of which are brought out to a circular Bakelite board on the face of which is inlaid a representation of the 36 coils of the stator as shown in diagram which gives the full details of the terminal plate except that the arcs showing the coils are only 12 instead of 36 to avoid congestion. The coil pitch is 9 slots or 19 coils sides i.e. if one of a coil is in the 1 st slot then the other coil side is in 10 th slot and so on Fig.10b : wave forms of line current ir during starting of 3-phase induction motor Fig.9: stator with 3-phase winding X. EXPERIMENTAL RESULTS Fig.11a: wave forms of line current IR when 1/3 coil of r-phase is short circuited TABLE III: Effect of Faults XI. TRANSIENTS DURING ELECTRICAL FAULTS Fig.11b: wave forms of line current IR when 1/3 coil of r-phase is short circuited Fig.10a: wave forms of line current IR during starting of 3-phase induction motor Fig.12a: wave forms of line current IR when r-phase is open circuited in star connection 969

7 [7] N. Paterson, The Analysis and Detection of Faults in Three Phase Induction Machines Using Finite Element Techniques, Doctoral Thesis, Robert Gordon Univ., Wetherby British Library, Aberdeen, 1998, 180 p. [8] W. T. Thomson, R. J. Gilmore, Motor Current Signature Analysis to Detect Faults in Induction Motor Drives Fundamentals, Data Interpretation, and Industrial Case Histories, Proceedings of the 32nd Turbo machinery Symposium, Houston, TX, USA, Sept. 8-11, pp , Fig.12b: wave forms of line current IR when y-phase is open circuited in delta connection XII. CONCLUSION AND FUTURE WORK The purpose of this paper is to diagnose electrical and mechanical faults in induction motor systems. The analysis results showed that the proposed method can identify the faults. The method described provides a promising way to establish potential metrics for the description of motor health degradation. Therefore, it is desirable to develop a condition monitoring system based on the above method and realize on-line health evaluation of induction motor. With such a function, the critical failure of induction motor systems can be avoided, and the reliability and availability of motor can be guaranteed REFERENCES [1] D. K. Chaturvedi and H. Vijay, Parameters Estimation of an Electric Fan Using ANN, Journal of Intelligent Learning Systems and Applications, 2010, 2: [2] D.K. Chaturvedi, Modeling & simulation of systems using Matlab/simulink, CRC Press, 2009 [3] P. F. Albrecht, J. C. Appiarius, and D. K. Sharma, "Assessment of the reliability of Motorsutility applications- Updated," IEEE Transactions on Energy Conversion, vol.1,pp , [4] A. Siddique and G. S. Yadava, A Review of Stator Fault Monitoring Techniques of Induction Motors, IEEE Trans. Energy Convers., vol. 20, no. 1, pp , [5] P. J. Tavner and A. F. Anderson, Core Faults in Large Generators, IEEE Proceedings on Electric Power Applications, Vol. 152, Issue 6, pp , [6] J. Ramirez-Nino, A. Pascacio, Detecting Interturn Short Circuits in Rotor Windings, IEEE Comp. Appl. in Power, vol. 14, issue 4, pp , [9] S. Nandi, H. A. Toliyat, Detection of Rotor Slot and Other Eccentricity Related Harmonics in a Three Phase Induction Motor With Different Rotor Cages, Proceedings of International Conference on Power Electronic Drives and Energy Systems for Industrial Growth PEDES 98, Perth,Australia, Nov. 30-Dec. 3, Vol. 1, pp , [10] Peter Vas, Parameter estimation, condition monitoring, and diagnosis of electrical machines, Clarendon Press Oxford., [11] P. J. Tavner and J. Penman, Condition monitoring of electrical machines". Hertfordshire, England: Research Studies Press Ltd, ISBN: , 1987 [12] P. H. Mellor, D. Roberts and D. R. Turner, Lumped parameter thermal model for electrical machines of TEFC design, IEEE Proc. Electric Power Application, Vol. 138, pp , [13] O. I. Okoro, Steady and transient states thermal analysis of a 7.5-kW squirrel- cage induction machine at rated-load operation, IEEE Transactions on Energy Conversion, Vol. 20, No. 4, pp , [14] John S. Hsu, Monitoring of defects in induction motors through air-gap torque observation IEEE Transactions on Industry Applications, Vol. 31, No. 5, pp , [15] D. G. Dorrell and A. C. Smith, Calculation and measurements of unbalance magnetic pull in cage induction motors with eccentric rotors, part 2: experimental investigation, IEEE Proceedings Electric Power Applications, Vol. 143, No. 3, May, pp , [16] A. Belahcen, A. Arkkio, P. Klinge, J. Linjama, V. Voutilainen and J. Westerlund, 1999, Radial forces calculation in a synchronous generator for noise analysis, Proceeding of the Third Chinese International Conference on Electrical Machines, Xi an, China, pp , August 1999 [17] D.K. Chaturvedi, Soft computing techniques and its applications to electrical engineering, Springer Verlag, Germany,