@Research Paper
<#LINE#>Coordination of Overcurrent Relay for Radial and Parallel feeder Networks<#LINE#>Acharya @Sandesh,Shrestha @Ravi, Nishchal@Tamang,Jha @Shailendra Kumar <#LINE#>1-8<#LINE#>1.ISCA-RJEngS-2016-104.pdf<#LINE#>Department of Electrical and Electronics Engineering, Kathmandu University, Dhulikhel, Nepal@Department of Electrical and Electronics Engineering, Kathmandu University, Dhulikhel, Nepal@Department of Electrical and Electronics Engineering, Kathmandu University, Dhulikhel, Nepal@Department of Electrical and Electronics Engineering, Kathmandu University, Dhulikhel, Nepal<#LINE#>24/11/2015<#LINE#>6/4/2016<#LINE#>Location of the fault is very important in power systems and its clearance should be carried out quickly to ensure continuous power supply to the load. Protection of the devices and their coordination is a crucial part for minimizing the fault. The research is focused on the simulations and analysis of over current relays’ coordination for radial and parallel feeder networks. This research work provides coordination of overcurrent relays for the radial and parallel feeder networks analyzing the time current characteristics. Observations and setup for radial and parallel feeder network is experimented in the electrical lab. Hardware implementation and study of time of operation of overcurrent relays is compared with the simulation result and findings from the ETAP software. For effective coordination of the relays, coordination among the relays and the respective protective devices are focused. Backup protection for the primary relay is achieved through the effective coordination time interval between the relays. Proper relay coordination effectively clears out the fault thus making the protection more reliable. The minimization of fault durations and backup protection to the primary protection is major concern of this research.<#LINE#>Yin L. G., Agileswari K.R., Farrukh H.N. and Aidil A.Z.A. (2014).@Numerical Relay for Overcurrent Protection using TMS320F2812.@Recent Researches in Circuits, Systems, Electronics, Control & Signal Processing, ISBN: 978-960-474-262-2.@Yes$Gurevich V. (2007).@Simple Very High-Speed Overcurrent Protection Relay.@Electrical engineering and Electromechanics.@Yes$Sunil S.R. (1975).@Switchgear and Protection.@Khanna Publishers.@Yes$M Pφller, B Maier and A Dierks (2015).@Simulating the Steady State and Transient Response of Protective Relays.@Digsilent Gmbh, Germany.@Yes$Y.G. Paithankar and S.R. Bhide. (2008).@Fundamentals of power system protection.@Asoke K. Ghosh publishers, Delhi, India.@Yes$Electrical4u. (2015).@Protection of Lines or Feeder.@http://www.electrical4u.com/protection-of-lines-or-feeder@No
@Research Article
<#LINE#>A Genetic Algorithm optimized PI Controller for Vector Controlled Drive<#LINE#> Kumar@ Alok <#LINE#>9-15<#LINE#>2.ISCA-RJEngS-2016-042.pdf<#LINE#>Department of Electrical Engineering, Bhilai Institute of Technology Durg, C.G., India<#LINE#>24/2/2016<#LINE#>26/4/2016<#LINE#>In the present scenario, for high performance industrial, DC machines are used. But with the help of Power Electronics, the same dynamic performance can be obtained for induction motor using vector controlled techniques. An advanced control strategy in the field of adjustable speed drives is the Indirect vector control of an Induction motor. The Indirect vector control strategy is simple with one PI controller. The tuned parameter values obtained by several methods may not perform satisfactorily for variable drive operating condition. In this paper, the Genetic Algorithm is used to optimize the gains of PI controller to enhance the performance of Induction motor drive.<#LINE#>Leonhard W. (1988).@Adjustable –Speed AC Drives.@proc. IEEE, 76(4), 455-471.@Yes$Finch J. W., Atkinson D. J. and Acarnley P. P. (1994).@Scalar to vector: General Principles of Modern Induction Motor Control.@IEEE Trans. Ind. Appl., 41(2), 201-207.@Yes$Yen Shin Lai (2003).@Machine Modelling and Universal Controller for Vector Controlled Induction Motor Drives.@IEEE Trans. On Energy Conservation, 18(1), 23-32.@Yes$Krishnan R. and Bharadwaj A.S. (1991).@A review of parameter sensitivity and adaptation in indirect vector controlled induction motor drive systems.@IEEE Trans. on Power Elec., 6(4), 695-701.@Yes$Ashok Kusagur, Kodad S.F. and Shankar Ram B.V. (2009).@Modelling of Induction Motor and Control of Speed Using Hybrid Controller Technology.@Journal of Theoretical and Applied Information Technology (JATIT), 10(2), 117-126.@Yes$Adel Aktaibi, Daw Ghanim and Rahman M.A. (2016).@Dynamic Simulation of a Three-Phase Induction Motor using MATLAB SIMULINK.@Currents.@Yes$Shi K.L., Chan T.F. and Wong Y.K. (1997).@Modelling of a Three-Phase Induction Motor Using SIMULINK.@0-7803-3946-0/97, IEEE.  WB3-6.l-6.3.@Yes$Habibur Rehman (2012).@Detuning Minimization for Alternative Energy Vehicular Drive System.@IEEE Trans. on Vehicular Power and Propulsion Conference (VPPC), 42-47.@Yes$Timothy M. Rowan, Russel J. Kerkman and David Leggate (1991).@A Simple On-Line Adaption for Indirect Field Orientation of an Induction Machine@, IEEE Trans. On Inds. App. 27(4), 720-727.@Yes$Tista Banerjee, Sumana Chowdhuri, Gautam Sarkar and Jitendranath Bera (2012).@Performance Comparison between GA and PSO for Optimization of PI and PID controller of Direct FOC Induction Motor Drive.@International Journal of Scientific and Research Publications, 2(7), 01-08.@Yes$Ranjith Kumar K., Sakthibala D. and Palaniswami S. (2010).@Efficiency Optimization of Induction Motor Drive using Soft Computing Techniques.@International Journal of Computer Applications (IJCA), 3(1), 06-12.@Yes$Laatra Yousfi, Amel Bouchemha, Mohcene Bechouat and Abdelhani Boukrouche (2014).@Vector control of induction machine using PI controller optimized by genetic algorithms.@16th International Power Electronics and Motion Control Conference and Exposition, 1493-1498, Antalya, Turkey, 21-24.@Yes$Bose B.K. (2005).@Modern Power electronics and AC drives.@Pearson Education, Singapore.@No
<#LINE#>Fault Detection System in Transmission Line Network<#LINE#>Gupta @Ankita,Potdar @R. M. <#LINE#>16-19<#LINE#>3.ISCA-RJEngS-2016-092.pdf<#LINE#>ET and T Department, BIT, Durg, India@ET and T Department, BIT, Durg, India<#LINE#>24/2/2016<#LINE#>20/4/2016<#LINE#>Now a days, technology has advanced and  its incorporation is playing such a significant role in human life that the order of the electricity power for the household, commercial and industrial loads is getting enhanced .Also,  managing the electricity power distribution system is getting more complex. Thus, examining such kind of faults is an imperative and complicated charge in power system. For   accurate   detection and study of these faults, there is a requirement of   model that detects this faults and its distance in transmission line. The statistical model precisely detains the performance of such unusual faults and position in suitable mode, and prevents   power system from faulty energy. The faults which occur in transmission lines can cause interruptions of power supplied. We can develop an   outline in this concern to   have an in built intelligence to sense the incidence   of error in the transmission line. So, to make sure a secured operation of distribution and   decrease the losses occurred by accidents, a far-off monitoring and controlling system is developed. This paper proposes Fault Detection, Classification and auto retrieve from the fault in Power Transmission Lines based on MATLAB with Arduino 328 hardware which helps in greatly reducing the human effort, minimizes times and works safely and proficiently without the interference of human being.<#LINE#>Senthil Kumar P. and R. Gowrishankar (2013).@Transmission Line Maintenance Using Sensory Data Collection through Rendezvous Nodes.@International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2(4).@No$Kamel T.S. and Moustafa Hassan M.A. (2004).@Transmission lines fault detection, classification and location using an intelligent Power System Stabiliser.@IEEE International Conference on Electric Utility Deregulation, Restructuring and Power Technologies (DRPT2004), April Hong Kong.@Yes$Banerjee M.D. and Kulkarni M.N.R. (2013).@Three phase Parameter data logging and fault detection using GSM Technology.@International Journal of Scientific and Research Publications, 3(2).@Yes$Ghosh A.K. and Lubkeman D.L. (1995).@The Classification of Power System Disturbance Waveforms Using a Neural Network Approach,@IEEE Transactions on Power Delivery, 10(1).@Yes$Jamil M. Thomas, Moinuddin M.S. and Kumar P. (2013).@Fuzzy approach to fault classification for transmission line protection, Proc.@IEEE Tencon 99 Conf., 2, 1046–1050.@Yes$Sumit Shelly vadhera (2011).@Iterative and Non-Iterative Methods for Transmission Line Fault-Location Without using Line Parameters.@International Journal of Engineering and Innovative Technology (IJEIT), 3(1).@No$Jingjing Cheng, Jing Jin, Li Kong, Huazhong (2005).@Wireless Distributed Monitoring and Centralized Controlling System for Prefabricated Substations in China.@University of Science and Technology, Hubei, China, IEEE Journal.@Yes$Bramha S.M. (2005).@Fault location scheme for a multi-terminal transmission line using synchronized voltage measurements.@IEEE Trans. Power Del., 20(2), 1325–1331.@Yes
<#LINE#>Experimental research on Aerodynamic Performance of an USPLSC airfoil <#LINE#>Yadav @Khagendra Kumar <#LINE#>20-30<#LINE#>4.ISCA-RJEngS-2016-093.pdf<#LINE#>School of Aeronautics, Northwestern Polytechnical University, Xi`an, China<#LINE#>3/3/2016<#LINE#>24/4/2016<#LINE#>Fre-and-aft symmetrical upper surface protrude lower surface concave (USPLSC) airfoils have been developed for the coaxial rotating wing of an UAV. In order to understand the aerodynamical performance of the airfoil, low speed wind tunnel experiment is carried out to measure lift coefficient, drag coefficient and pressure distribution under wind speed 20~40 m/s. Results are compared with CFD prediction using Fluent software. A satisfied agreement has been achieved and error analysis is provided. The purpose of this study is to conduct a parametric investigation on the performance of USPLSC airfoils for coaxial rotating wing UAV. The main objective of this study is to test USPLSC in wind tunnel and compare the experiment result with CFD simulation result.<#LINE#>Gordon Leishman J. (2000).@Principles of Helicopter Aerodynamics with CD Extra.@Cambridge University Press, New York, 1-4. ISBN: 987-0-521-66060-0.@Yes$Colin P. Coleman. (1997).@A survey of theoretical and experimental coaxial rotoraero dynamic research.@Ames Research Center, Moffett Field, California. 0704-0188@Yes$John D. Anderson Jr. (2005).@Fundamentals of aerodynamics.@McGraw-Hill Education, ISBN-13: 978-0073398105@Yes$Emami M.R. (2007).@Aerodynamic forces on an airfoil.@AER 303F, Aerospace Laboratory I. University of Toronto, Toronto.@Yes$Chow C.Y. (1979).@An introduction to computational fluid dynamics.@John Wiley and Sons, Inc.,New York, NY@Yes$FLUENT 6.3. (2006).@User Guide,@FLUENT Inc. of American.@No$GAMBIT 6.2. (2006).@User Guide,@FLUENT Inc. of American.@No
@Review Paper
<#LINE#>Monitoring and Control of Machining Process: A Review<#LINE#> Rai@Anjul,Ganguly@ S.K. <#LINE#>31-36<#LINE#>5.ISCA-RJEngS-2016-007.pdf<#LINE#>Mechanical Engineering Department, B.I.T. Durg, CG, India@Mechanical Engineering Department, B.I.T. Durg, CG, India<#LINE#>24/2/2016<#LINE#>2/5/2016<#LINE#>Detection and control of machining parameters such as cutting force, torque, vibration, tool condition, and surface finish is essential for faultless machining in manufacturing systems. This study presents a review on regular and enhanced methods used for monitoring and control of metal cutting processes. The difference between the various available methods, structures and the corresponding equipments are identified and evaluated.<#LINE#>Zorev N.N. (1966).@Metal Cutting Mechanics.@Pergamon Press, Oxford, England.@Yes$Yu G. (2002).@Tool Wear Monitoring in Turning Operations using Ultrasonic Wave and Artifical Neural Network.@Milwaukee, The University of Wisconsin.@No$Abuthakeet S.S., P.V. Mohanram and G. Mohan Kumar. (2011).@Prediction and Control of Cutting Tool Vibration in CNC Lathe With ANOVA and ANN.@International Journal of Lean Thinking, 2(1).@Yes$Stavropoulos P. et.al. (2015).@Tool wear predictability estimation in milling based on multi-sensorial data.@Int J Adv Manuf Technol. 82(1), 509-521.@Yes$Stavropoulos P. et.al. (2013).@Momitoring and Control of Manufacturing Processes: A Rewiew.@14th CIRP Conference on Modeling of Machining Operations (CIRP CMMO).@Yes$Taguchi. G. et.al. (1989).@Quality Engineering in Production Systems.@McGraw-Hill, NY.@Yes$Shewhart W.A. (1986).@Statistical Method from the viewpoint of Quality Control.@Dover, New York.@Yes$Ulsoy Galip A. (2006).@Monitoring and Control of Machining.@Condition monitoring and control for intelligent manufacturing, Springer.@Yes$Goodwin G.C. and Sin K. (1984).@Adaptive Filtering, Prediction, and Control.@Dover Publication, Newyork.@Yes$Stute G. (1980).@Adaptive Control.@Proceedings of the Machine Tool Task Force Conference, 4, Sect. 7.14.@Yes$Ulsoy A.G. and Koren Y. (1989).@Applications of Adaptive Control to Machine Tool Process Control.@IEEE Control Systems Magazine, 9(4), 33-37.@Yes$Galip Ulsoy A. and Koren Y. (1993).@Control of Machining Process.@Transaction of the ASME, 115.@Yes$Colwell L.V., Mazur J.C and De Vries W.R. (1978).@Analytical Strategies for Automatic Tracking of Tool Wear.@Proceedings of the 6th North American Manufacturing Research Conference, 274-282.@Yes$Yen D.W. and Wright P.K. (1983).@Adaptive Control in Machining-A New Approach Based on the Physical Constraints of Tool Wear Mechanisms@ASME Journal of Engineering for Industry, 105, 31-38.@Yes$Ulsoy A.G., Koren Y. and Rasmussen F. (1983).@Principal Developments in the Adaptive Control of Machine Tools.@ASME Journal of Dynamic Systems, Measurement, and Control, 105, 107-112.@Yes$Koren Y., Ko T.R., Ulsoy A.G. and Danai K. (1991).@Flank Wear Estimation Under Varying Cutting Conditions.@ASME Journal Of Dynamic Systems, Measurement, And Control, 113, 300-307.@Yes$A. Galip Ulsoy and Yoram Koren (1989).@Applications of Adaptive Control to Machine Tool Process Control.@IEEE.@Yes$Danai K. and Ulsoy A.G. (1987).@A dynamic state model for on-line tool wear estimation in turning.@ASME Journal of Engineering for Industry, 109, 4, 396–399.@Yes$Du R., Elbestawi M.A. and Wu S.M. (1995)@Automated monitoring of manufacturing processes, Part 1: Monitoring methods, and Part 2: Applications.@ASME Journal of Engineering for Industry, 117, 121–132@Yes$Ryabov O., Mori K. and N. Kasashima (1996).@An In-Process Direct Monitoring Method for Milling Tool Failures Using a Laser Sensor.@AIST, MITI.@Yes$Kourosh Danai and A. Galip Ulsoy (1987).@An Adaptive Observer For On-Line Tool Wear Estimation In Turning, Part I: Theory.@Mechanical systems and signal processing, l(2), 211-225.@Yes$Whitehouse D.J. (1978).@Surfaces: A Link between Manufacture and Function.@Proceedings of the Institution of Mechanical Engineers, 179–188.@Yes$Tonder K. (1987).@Effects of Skew Unidirectional Striated Roughness on Hydrodynamic Lubrication.@Wear, 115, 19.@Yes$Wilson W.R.D. and Sheu S. (1988).@Influence of Surface Topography on Viscoplastic Asperity Lubrication.@Wear, 124, 311.@Yes$Cook N.H. (1980).@Tool wear sensors.@Wear, 62, 49–57.@Yes$Cook N.H. and Subramanian K. (1978).@Micro-isotope tool wear sensor.@CIRP Annals, 73–78.@Yes$Park J. J. and Ulsoy A.G. (1993).@On-line flank wear estimation using an adaptive observer and computer vision, Part 1: Theory, Part 2: Experiment.@ASME Journal of Engineering for Industry, 115, 30–43.@Yes$El Gomayel J.I. and Bregger K.D. (1986).@On-line tool wear sensing for turning operations.@ASME Journal of Engineering for Industry, 108, 44–47.@Yes$Nair R., Danai K. and Malkin S. (1992).@Turning process identification through force transients.@ASME Journal of Engineering for Industry, 114, 1, 1–7.@Yes$Groover M.P., Karpovich R.J. and Levy E.K. (1977).@A Study of the Relationship Between Remote Thermocouple Temperature and Tool Wear In Machining.@International Journal of Product Research. 25, 2, 129–141.@Yes$Martin P., Mutels B. and Draiper J.P. (1975).@Influence of Lathe Tool Wear on the Vibrations Sustained in Cutting.@16th International Machine Tool Design and Research Conference.@Yes$Kannatey Asibu Jr. E. and Dornfeld D.A. (1982).@A Study of Tool Wear in Metal Cutting Using Statistical Analysis of Acoustic Emission@Wear, 76, 2, 247–261.@Yes$Coker S.A., Oh S.J. and Shin Y.C. (1998).@In-Process Monitoring of Surface Roughness Utilizing Ultrasound.@ASME Journal for Manufacturing Scientists and Engineers, 120, 197–200.@Yes$Bradley C., Bohlmann J. and Kurada S. (1998).@A Fiber Optic Sensor for Surface Roughness Measurement.@ASME Journal for Manufacturing Scientists and Engineers, 120, 359–367.@Yes$Kourosh D. (2002).@Machine Tool Monitoring and Control.@CRC Press LLC.@No$Kannatey Asibu E. and Emel E. (1987).@Linear Discriminant Function Analysis of Acoustic Emission Signals for Cutting Tool Monitoring.@Mechanical Systems and Signal Processing, 4, 333–347.@Yes$Houshmand A.A. and Kannatey Asibu E. (1989).@Statistical process control of acoustic emission for cutting tool monitoring.@Mechanical Systems and Signal Processing, 3, 4, 405–424.@Yes$Tlusty J. and Andrews G.C. (1983).@A Critical Review of Sensors for Unmanned Machining.@Annals of the CIRP, 32, 2, 563–572.@Yes$Altintas Y. and Yellowley I. (1987).@In-Process Detection Of Tool Failure In Milling Using Cutting Force Models.@In Sensors for Manufacturing, ASME, 1–16.@Yes$Moriwaki T. (1980).@Detection for Tool Fracture by Acoustic Emission Measurement.@Annals of the CIRP, 29, 1, 35–40.@Yes$Lan M.S. and Dornfeld D.A. (1984).@In-Process Tool Fracture Detection.@ASME Journal of Engineering Materials and Technology, 106, 111–118.@No$Matsushima K., Bertok P. and Sata T. (1982).@In-Process Detection of Tool Breakage by Monitoring the Spindle Motor Current of A Machine Tool.@In Measurement And Control for Batch Manufacturing, ASME, 145–154.@Yes$Altintas Y. (1997).@Prediction of cutting forces and tool breakage in milling from feed drive current measurements.@ASME Journal for Manufacturing Scientists and Engineers, 119, 386–392.@Yes$Hayashi S.R., Thomas C.E. and Wildes D.G. (1988).@Tool Break Detection by Monitoring Ultrasonic Vibrations.@Annals of the CIRP, 37, 1, 61–64.@Yes$Lan M. and Naerheim Y. (1986).@In-Process Detection of Tool Breakage in Milling.@ASME Journal of Engineering for Industry, 108, 191–197.@Yes$Altintas Y., Yellowley I. and Tlusty J. (1988).@The Detection of Tool Breakage in Milling Operations.@ASME Journal of Engineering for Industry, 110, 3, 271–277.@Yes$Sata T., Matsushima K., Nagakura T. and Kono E. (1973).@Learning and Recognition of the Cutting States by the Spectrum Analysis.@Annals of the CIRP, 22, 41–42.@Yes$Rangwala S. and Dornfeld D. (1990).@Sensor Integration Using Neural Networks for Intelligent Tool Condition Monitoring.@ASME Journal of Engineering for Industry, 112, 219–228.@Yes$Danai K. and Chin H. (1991).@Fault Diagnosis with Process Uncertainty.@ASME Journal of Dynamic Systems, Measurement and Control, 113, 3, 339–343.@Yes$Colgan J., Chin H., Danai K. and Hayashi S. (1994).@Tool breakage detection in turning: a multisensory method.@ASME Journal of Engineering for Industry, 116, 1, 117–123.@Yes$Jammu V.B., Danai K. and Malkin S. (1993).@Unsupervised Neural Network for Tool Breakage Detection in Turning.@CIRP Annals - Manufacturing Technology, 42, 67-70.@Yes$Delio T., Tlusty J. and Smith S. (1992).@Use of Audio Signals for Chatter Detection and Control.@ASME Journal for Manufacturing Scientists and Engineers, 119, 146–157.@Yes$Matsubara A. (2002).@Current Status and Trends of Monitoring and Control Technology in Machining Process.@J. of the Society of Instrument and Control Engineers, 41-1, 781-786.@Yes$Furutani K. (2006).@Piezoelectric sensors.@Journal of the Society of Instrument and Control Engineers, 45-4, 296-30.@No$Kistler (2015).@Kistler – Measuring Systems and Sensors.@http://www.kistler.com@Yes$Kono D., Matsubara A., Yamaji I. and Fujita T. (2007).@High-Precision Machining by Measurement and Compensation of Motion Error.@International Conference on Leading Edge Manufacturing in 21st Century, 809-812.@Yes$Jemielniak K. (1999).@Commercial Tool Condition Monitoring Systems.@International Journal. of Advanced Manufacturing Technology, 15, 711-721.@Yes$Tlusty J. and Andrews G.C. (1983).@A Critical Review of Sensor for Unmanned Machining.@Annals of the CIRP, 32-2, 563-572.@Yes$Ohzeki H., Mashine A., Aoyama H., and Inasaki I. (1999).@Development of a Magnetostrictive Torque Sensor for Milling Process Monitoring.@Journal of Manufacturing Science and Engineering, 121, 615-622.@Yes$Matsubara A. and Ibaraki S. (2009).@Monitoring and Control of Cutting Forces in Machining Processes: A Review.@International Journal of Automation Technology.@Yes$Y. Altintas (1992).@Prediction of Cutting Forces and Tool Breakage in Milling from Feed Drive Current Measurement.@Journal of Engineering for Industry, 114, 386-392.@Yes$Lee J.M., Choi D.K., Kim J. and Chu C.N. (1995).@Real-Time Tool Breakage Monitoring for NC Milling Process.@CIRP Annals – Manufacturing Technology, 44-1, 59-62.@Yes$Matsushima K., Bertok P. and Sara T. (1982).@In-Process Detection of Tool Breakage by Monitoring the Spindle Motor Current of Machine Tool. Measurement and Control for Batch Manufacturing,@The Winter Annual Meeting of ASME, 121-134.@Yes$Kim T.Y., Woo J., Shin D., and Kim J. (1999).@Indirect Cutting Force Measurement in Multi-Axis Simultaneous NC Milling Processes.@International Journal of Machine Tools and Manufacture, 39-11, 1717-1731.@Yes$Shinno H., Hashizume H. and Yoshloka H. (2003).@Sensor-Less Monitoring of Cutting Force During Ultraprecision Machining.@CIRP Annals Manufacturing Technology, 52-1, 303-306.@Yes$Kurihara D., Kakinuma Y. and Katsura S. (2009).@Sensor-less Cutting Force Monitoring using Parallel Disturbance Observer.@International Journal of Automation Technology, 3-4.@Yes$Konrad H., Isermann R. and Oette H.U. (1994).@Supervision of Tool Wear and Surface Quality during End Milling Operations. IFAC Workshop Intelligent Manufacturing Systems, 507-513.@undefined@Yes$Huang P., Chen J.C. and Chou C. (1999).@A Statistical Approach in Detecting Tool Breakage in End Milling Operations.@Journal of Industrial Technology, 15-3, 2-7.@Yes$Choi Y. J., Park M.S. and Chu C.N. (2008).@Prediction of Drill Failure Using Features Extraction in Time and Frequency Domains of Feed Motor Current.@International Journal of Machine Tools and Manufacture, 48, pp. 29-29.@Yes$Li X. (1999).@On-Line Detection of the Breakage of Small Diameter Drills Using Current Signature Wavelet Transform.@International Journal of Machine Tools and Manufacture, 39-1, 157-164.@Yes$Li X., Ouyang G. and Liang Z. (2008).@Complexity Measure of Motor Current Signals for Tool Flute Breakage Detection In End Milling.@International Journal of Machine Tools and Manufacture, 48, 371-379.@Yes$Schofield S. and Wright P. (1998).@Open Architecture Controllers for Machine Tools, Part 1: Design Principles.@ASME Journal for Manufacturing Scientists and Engineers, 120, 417–424.@Yes$Koren Y. (1997).@Control of Machine Tools.@ASME Journal for Manufacturing Scientists and Engineers, 119, 749–755.@Yes