i-manager Publications

i-manager's Journal on Instrumentation and Control Engineering (JIC)

Current Issue Vol. 12 Issue 1

Most Cited

Diagnosis of Air-Gap Eccentricity Fault for Inverter Driven Induction Motor Drives in the Transient Condition
Modelling and Simulation Study of a Helicopter with an External Slung Load System
Comparative Study of Single Phase Power Inverters Based on Efficiency and Harmonic Analysis
LabVIEW Based Design and Analysis of Fuzzy Logic, Sliding Mode and PID Controllers for Level Control in Split Range Plant
Trichotomous Exploratory Data Analysis [Tri–EDA]: A Post Hoc Visual Statistical Cumulative Data Analysis Instrument Designed to Present the Outcomes of Trichotomous Investigative Models

Volume 6 Issue 4 August - October 2018

Research Paper

Diagnostic Analysis Of The Energy Consumption Of Aluminum Direct Extrusion Operations For Real Time Conditions

Samer As'ad* , Adnan Al-Smadi, Wael Massarweh*

Head, Department of Renewable Energy Engineering, Middle East University, Jordan.

Former Dean, Hijjawi College for Engineering Technology, Yarmouk University, Jordan.

Consultant, Al Hussein Technical University, Amman, Jordan.

As'ad, S., Al-Smadi, A., & Massarweh, W. (2018). Diagnostic Analysis Of The Energy Consumption Of Aluminum Direct Extrusion Operations For Real Time Conditions. i-manager's Journal on Instrumentation and Control Engineering, 6(4), 1-8. https://doi.org/10.26634/jic.6.4.14945

Abstract

In direct aluminum extrusion, so much work has been accumulated to control the process with respect to such principle element as billet metallurgy, die design, equipment, and process parameters. The objective of this paper is to develop an algorithm that can be used to extract some features from the energy consumption cycle in one billet. The results of the diagnostic analysis can be used to monitor energy consumption and time it takes to extrude one billet. In addition, the developed method can be automated to predict how long the production will be at its maximum and at its minimum. Hence, energy and time can be saved. The work presented here is based on real-data taken from a sensor attached to three pumps at the plant that takes 5 readings every 2 seconds for each pump. That is, a total of 15 readings every 2 seconds. The work presented is for direct-extrusion using 2420 Metric ton capacity. The algorithm detects the boundary points for the regions in the signature. It should be emphasized that the available machine is designed for direct extrusion operations only.

References

[1]. Altenpohl, D. G. (1982). Aluminium viewed from within: An Introduction into the Metallurgy of Aluminium Fabrication. Aluminium-Vrlg.

[2]. Al-Smadi, A., As' ad, S., & Massarweh, W. (2007, June). Identification and analysis of the power consumption for aluminum extrusion Process. In Control & Automation, 2007. MED'07. Mediterranean Conference on (pp. 1-6). IEEE.

[3]. Asad, S. (2005). On-line identification and analysis of the power consumption signal aluminum extrusion (Masters Thesis, Yarmouk University).

[4]. Altan, T., & Kobayashi, S. (1968). A numerical method for estimating the temperature distributions in extrusion through conical dies. Journal of Engineering for Industry, 90(1), 107-118.

[5]. Borowski, J., & Wendland, J. (2016). The phenomenon of durability variable dies for aluminum extrusion profiles. Metalurgija, 55(2), 229-232.

[6]. Castle, A. F. (1993). Temperature control in aluminum extrusion. In Proceedings of International Aluminium Extrusion Technology Seminar (Vol. 2, pp. 181-181). Aluminium Association Inc, & Aluminium Extruders Council.

[7]. Castle, A. F., & Sheppard, T. (1976). Hot-working theory applied to extrusion of some aluminium alloys. Metals Technology, 3(1), 454-464.

[8]. Chahare, A. S., & Inamdar, K. H. (2017). Optimization of Aluminium extrusion process using Taguchi method. Journal of Mechanical and Civil Engineering, 61-65.

[9]. Garold J., & Borse. (1997). Numerical Methods with MATLAB: A Resource for Scientists and Engineers. PWS Publishing Company.

[10]. Hatch, J. (1984). Aluminum: Properties and Physical Metallurgy. ASM International.

[11]. Hashimoto, N. (2017). Application of Aluminum extrusions to automotive parts. Kobelco Technology Review, 35, 69-75.

[12]. Mohapatra, C. R., & Nayak, N. C. (2015). Squared Multi-hole Extrusion Process: Experimentation & Optimization. International Refereed Journal of Engineering and Science (IRJES), 4(8), 32-37.

[13]. Karampour, P., & Jabbari, A. (2015). Die design optimization in forward extrusion of an Aluminum rod. Journal of Mechatronics, 3(1), 49-52.

[14]. Mei, R. B., Du, Y. X., Bao, L., Zhang, X. Y., Zhang, B., & Zhou, Z. J. (2015). Study on hot deformation behavior of 7085 aluminum alloy during backward extrusion process. Modelling and Simulation in Engineering, 2015.

[15]. Saha, P. K. (1977). Temperature Distribution in Extrusion (Masters Thesis, University of Calcuta).

[16]. Saha, P. K., & Ghosh, R. K. (1979). Temperature distribution during hot extrusion of Aluminum-theoretical evaluation. Indian Journal of Technology, 17(7), 264-268.

[17]. Saha P. K. (2000). Aluminum Extrusion Technology. ASM International.

[18]. Tashiro, Y., Yamasaki, H., Ohneda, N., & Nakanishi, K. (1993). Extrusion conditions and metal flow to minimize both distortion and variance of cross sectional shape. In Proceedings of International Aluminium Extrusion Technology Seminar (Vol. 2, pp. 191-191). Aluminium Association Inc, & Aluminium Extruders Council.

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Research Paper

Condition Monitoring And Fault Diagnosis Of Rolling Element Bearing

Shayela Farheen Aziz* , Arun Kumar**

*PG Scholar (Instrumentation and Control), Chhattisgarh Swami Vivekananda Technical University, Durg Chhattisgarh, India.

**Professor, Department of ETC, BIT Durg, Chhattisgarh, India.

Aziz, F. S., & Kumar, A. (2018). Condition Monitoring And Fault Diagnosis Of Rolling Element Bearing. i-manager's Journal on Instrumentation and Control Engineering, 6(4), 9-17. https://doi.org/10.26634/jic.6.4.14955

Abstract

Bearings are the most important part of any machinery used for domestic purpose or in big industries. Accurate working of these machines and devices relies on resistance free running of the bearings. In industrial applications, these bearings act as the most crucial machine component because they have to work under high load and speed; and defects in bearing, until noticed in time, can even lead to failure of the machinery. Therefore, identification of these defects is important for condition monitoring and quality inspection of bearings. Different methods are used for detection and diagnosis of bearing defects; they are classified as vibration measurements, acoustic measurements, temperature measurements, and wear debris analysis. For monitoring the condition and diagnosing the fault that may occur in REB, the vibration analysis technique is widely used because of its reliability and accuracy. In this work, the vibration signal of the REB under the normal and faulty condition from the database of Case Western Reserve University is taken for detailed analysis. Useful data from the raw signal is mined using Time Synchronous Averaging (TSA) and then TSA signal is processed using Discrete Wavelet Transform (DWT). The features of the DWT signal (D3) are extracted and further processed using Feed forward Backpropagation Artificial Neural Network. Moreover, comparison between existing work and this work has been shown.

References

[1]. Ali, J. B., Chebel-Morello, B., Saidi, L., Malinowski, S., & Fnaiech, F. (2015). Accurate bearing remaining useful life prediction based on Weibull distribution and Artificial Neural Network. Mechanical Systems and Signal Processing, 56, 150-172.

[2]. Bechhoefer, E., & Kingsley, M. (2009a). A review of time synchronous average algorithms. In Annual Conference of the Prognostics and Health Management Society (Vol. 1, pp. 1-10).

[3]. Bechhoefer, E., & Kingsley, M. (2009b). A review of time synchronous average algorithms. In Annual Conference of the Prognostics and Health Management Society (pp. 24-33).

[4]. Bravo-Imaz, I., Ardakani, H. D., Liu, Z., García-Arribas, A., Arnaiz, A., & Lee, J. (2017). Motor current signature analysis for gearbox condition monitoring under transient speeds using wavelet analysis and dual-level time synchronous averaging. Mechanical Systems and Signal Processing, 94, 73-84.

[5]. Case Western Reserve University Bearing Data Center. Retrieved from https://csegroups.case.edu/bearing datacenter/ home

[6]. Choudhury, A., & Paliwal, D. (2016). Application of frequency B-Spline wavelets for detection of defects in rolling bearings. Procedia Engineering, 144, 289-296.

[7]. Golbaghi, V. K., Shahbazian, M., Moslemi, B., & Rashed, G. (2017). Rolling element bearing condition monitoring based on vibration analysis using statistical parameters of discrete wavelet coefficients and neural networks. International Journal of Automation and Smart Technology, 7(2), 61-69.

[8]. Huo, Z., Zhang, Y., Francq, P., Shu, L., & Huang, J. (2017). Incipient fault diagnosis of roller bearing using optimized wavelet transform based multi-speed vibration signatures. IEEE Access, 5, 19442-19456.

[9]. Jayaswal, P., Wadhwani, A. K., & Mulchandani, K. B. (2008a). Machine fault signature analysis. International Journal of Rotating Machinery, 2008.

[10]. Kumar, H. S., Pai, P. S., Sriram, N. S., & Vijay, G. S. (2013). ANN based evaluation of performance of wavelet transform for condition monitoring of rolling element bearing. Procedia Engineering, 64, 805-814.

[11]. Lebold, M., McClintic, K., Campbell, R., Byington, C., & Maynard, K. (2000, May). Review of vibration analysis methods for gearbox diagnostics and prognostics. In Proceedings of the 54^thMeeting of the Society for Machinery Failure Prevention Technology (Vol. 634, p. 16).

[12]. Peyré, G. (2010). The numerical tours of signal processing. Advanced Computational Signal and Image Processing. IEEE Computing in Science and Engineering, 13(4), 94-97.

[13]. Riera-Guasp, M., Antonino-Daviu, J. A., & Capolino, G. A. (2015). Advances in electrical machine, power electronic, and drive condition monitoring and fault detection: State of the art. IEEE Trans. Industrial Electronics, 62(3), 1746-1759.

[14]. Samanta, B., & Al-Balushi, K. R. (2003). Artificial Neural Network based fault diagnostics of rolling element bearings using time-domain features. Mechanical Systems and Signal Processing, 17(2), 317-328.

[15]. Sanz, J., Perera, R., & Huerta, C. (2007). Fault diagnosis of rotating machinery based on autoassociative neural networks and wavelet transforms. Journal of Sound and Vibration, 302(4-5), 981-999.

[16]. Schröder, D., Vorländer, M., & Svensson, P. U. (2010). Open acoustic measurements for validating edge diffraction simulation methods. In Baltic-Nordic Acoustic Meeting.

[17]. Scilab. Retrieved from www.scilab.org

[18]. Sidar, R., Sen, P. K., & Sahu, G. (2015). Review of vibration based fault diagnosis in rolling element bearing and vibration analysis techniques. International Journal of Scientific Research Engineering & Technology, 4(10), 998-1003.

[19]. Tahir, M. M., Khan, A. Q., Iqbal, N., Hussain, A., & Badshah, S. (2017). Enhancing fault classification accuracy of ball bearing using central tendency based time domain features. IEEE Access, 5, 72-83.

[20]. Tandon, N., & Choudhury, A. (1999). A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribology International, 32(8), 469-480.

[21]. Tyagi, S., & Panigrahi, S. K. (2017). A DWT and SVM based method for rolling element bearing fault diagnosis and its comparison with Artificial Neural Networks. Journal of Applied and Computational Mechanics, 3(1), 80-91.

[22]. Wear Debris Analysis. (n.d.). Physics of Mechanical Engineering.

[23]. Wörner, S. (n.d.). Fast Fourier Transform, Numerical Analysis, First Edition. Swiss Federal Institute of Technology, Zurich.

[24]. Xiao, F., Shi, Y., & Ren, W. (2018). Robustness analysis of asynchronous sampled-data multiagent networks with time-varying delays. IEEE Transactions on Automatic Control, 63(7), 2145-2152.

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Research Paper

Adequacy Check-Orifice Plates – A Cost Effective Solution For Brown Field Projects

Shikha Sardana* , Aditi Bhatia**

*-**Control Systems Engineer, Fluor Daniel India Pvt. Ltd., Gurgaon, Haryana, India.

Sardana, S., & Bhatia, A. (2018). Adequacy Check-Orifice Plates A Cost Effective Solution For Brown Field Projects. i-manager's Journal on Instrumentation and Control Engineering, 6(4), 18-26. https://doi.org/10.26634/jic.6.4.15099

Abstract

In today's competitive market, every EPC company aims at building client's trust through each project it executes. One of the best ways to develop this trust is by putting yourself in client's shoe while working on revamp/ brown field projects, where the main intent of client is to safely use the existing facility, instruments, and installation as much as possible in order to reduce capital expenditure. This gives rise to the subject of adequacy check of inline/offline instrument, i.e evaluating if the existing inline/offline instrument shall work under new process conditions or not. This paper describes various steps involved for checking adequacy of existing orifice plates for Revamp / Brownfield projects. Since every project has a significant number of orifice plates, adequacy check of existing orifice is quite essential and can save a huge amount to client in terms of effort hours and installation cost, if existing plate is reused.

References

[1]. A Guide to Sizing Orifice Plate Flow Meters. (n.d.). Retrieved from http://www.instrumentationtoolbox.com/ 2013/10/a-guide-to-sizing-orifice-plate-flow.html

[2]. Basics of the Orifice Plate Flow Meter. (n.d.). Retrieved from https://www.instrumentationtoolbox.com/ 2013/03/basics-of-orifice-flow-meter.html

[3]. International Organization for Standardization. (2003). Measurement of fluid flow by means of pressure differential devices inserted in circular cross-section conduits running full -- Part 2: Orifice plates (ISO 5167- 2:2003). Retrieved from https://www.iso.org/standard/ 30190.html

[4]. Liptak, B. G. (2003). Instrument Engineers' Handbook, Volume One: Process Measurement and Analysis. CRC Press.

[5]. Miller, R. W. (1996). Flow Measurement Engineering Handbook. New York: McGraw-Hill.

[6]. Orifice Plate. (2018, October 18). Retrieved from https://en.wikipedia.org/wiki/Orifice_plate

[7]. Persamaan Bernoulli. (2018). Retrieved on June 20, 2018 from https://priyahitajuniarfan.wordpress.com/ 2011/02/06/persamaan-bernoulli/

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Research Paper

Optimal Switching Techniques For Aircraft Lateral Dynamics

Pavithra A. C.* , Yathisha L., Archana N. V.*

*Research Scholar, Department of Electronics and Communication Engineering, NIEIT, Mysore, Karnataka, India.

Associate Professor, Department of Electronics and Communication Engineering, ATME College of Engineering, Mysore, Karnataka, India.

Professor and Head, Department of Electronics and Communication Engineering, NIEIT, Mysore, Karnataka, India.

Pavithra, A. C., Yathisha, L., & Archana, N. V. (2018). Optimal Switching Techniques For Aircraft Lateral Dynamics. i-manager's Journal on Instrumentation and Control Engineering, 6(4), 27-34. https://doi.org/10.26634/jic.6.4.14950

Abstract

Recently, hybrid control systems are occupying the positions of conventional control techniques in the aircraft systems due to their several advantages. Stabilizing, Controlling, and Minimizing the overshoots of the present Aircraft system compared to earlier is quite a challenge. In order to meet the requirements of todays flight control system and also following the investigations of both control and aircraft communities, this paper concentrates on the application of optimal switching control techniques for the lateral flight dynamics of an aircraft control system. The proposed techniques are implemented using MATLAB/SIMULINK platform.

References

[1]. Akyazi, O., Usta, M. A., & Akpinar, A. S. (2012). A self-tuning fuzzy logic controller for aircraft roll control system. International Journal of Control Science and Engineering, 2(6), 181-188.

[2]. Aravena, J. L., & Devarakonda, L. (2006). Performance driven switching control. IEEE International Symposium on Industrial Electronics. DOI: 10.1109/ISIE. 2006.295564

[3]. Chrif, L., & Kadda, Z. M. (2014). Aircraft control system using LQG and LQR controller with optimal estimation- Kalman filter design. Procedia Engineering, 80, 245-257.

[4]. Gopinadh. (2005). M.Tech Dissertation, Department of Aerospace Engineering, IIT, Bombay.

[5]. Henrion, D., Daafouz, J., & Claeys, M. (2013). Optimal switching control design for polynomial systems: An LMI approach. arXiv preprint arXiv:1303.1988

[6]. Heydari, A., & Balakrishnan, S. N. (2014). Optimal switching and control of nonlinear switching systems using approximate dynamic programming. IEEE Transactions on Neural Networks and Learning Systems, 25(6), 1106- 1117.

[7]. Maddi, A., Guessoum, A., & Berkani, D. (2009). Using Linear Quadratic Gaussian Optimal Control for Lateral Motion of Aircraft. International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, World Academy of Science, Engineering and Technology, 3(1), 19-23.

[8]. Sutarto, H. Y., Budiyono, A., Joelianto, E., & Hiong, G. T. (2006, December). Switched linear control of a model helicopter. In Control, Automation, Robotics and Vision, th 2006. ICARCV'06. 9^thInternational Conference on (pp. 1- 8). IEEE.

[9]. Yathisha, L., & Kulkarni, S. P. (2013). Optimum LQR switching approach for the improvement of STATCOM performance. In Proceedings of the Third International Conference on Trends in Information, Telecommunication and Computing (pp. 259-266). Springer, New York, NY.

[10]. Yathisha, L., & Kulkarni, S. S. P. (2015, May). Application and comparison of switching control algorithms for power system stabilizer. In Industrial Instrumentation and Control (ICIC), 2015 International Conference on (pp. 1300-1305). IEEE.

[11]. Yathisha, L., Davoodi, K., & Kulkarni, S. S. P. (2017). Optimal switching control strategy for UPFC for wide range of operating conditions in power system. 3^rd Indian Control Conference, IEEE Xplore (pp. 225-232). DOI:10.1109/INDIANCC.2017.7846479.

[12]. Yathisha, L., & Patilkulkarni, S. (2018). LQR and LQG based optimal switching techniques for PSS and UPFC in power systems. Control Theory and Technology, 16(1), 25- 37.

[13]. Wang, Z. J., Guo, S. J., & Li, W. (2008). Modelling, simulation and optimal control for an aircraft of aileronless folding wing. WSEAS Transactions on Systems and Control, 3(10), 869-878.

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Research Paper

Intelligent Control System For Resistance Spot Welding System

S. Rama Subbanna* , M. Rajini **

* Associate Professor, Department of Electrical and Electronics Engineering, Sasi Institute of Technology & Engineering, Tadepalligudem, Andhra Pradesh, India.

** Lecturer, Department of Computer Science, SCIM Government Degree and P.G. College, Tanuku, Andhra Pradesh, India.

Subbanna, R. S., & Rajini, M. (2018). Intelligent Control System For Resistance Spot Welding System. i-manager's Journal on Instrumentation and Control Engineering, 6(4), 35-41. https://doi.org/10.26634/jic.6.4.15366

Abstract

This paper is an attempt to accomplish a performance analysis of the different control techniques on current spike reduction by means of magnetization level control in the primary winding on the medium frequency transformer based DC spot welding system. Current Spike reduction is an important factor to be considered while spot welding systems are concerned. The current control technique is a piecewise linear control technique that is inspired from the DC-DC converter control algorithms to register a novel current spike reduction method in the MFDC spot welding applications. The different controllers that were used for the current spike reduction by means of magnetization level control of the overall applications involve the hysteresis controller, Proportional Integral (PI) controller. Care is taken such that the current control technique would maintain a reduced current spike in the primary current of the transformer while it reduces the Total Harmonic Distortion. In this research, intelligent control system is introduced by using Adaptive Neuro Fuzzy Inference System (ANFIS). The performance of ANFIS controller is presented considering the parameters, percentage of current spike reduction, and THD. It is proved that current spikes are controlled successfully and has less THD than the above mentioned conventional controllers. Matlab/Simulink^TM based simulation is carried out for the MFDC with 220 KVA welding transformer in resistance spot welding systems.

References

[1]. Antonelli, D., & Astanin, S. (2015). Enhancing the quality of manual spot welding through augmented reality assisted guidance. Procedia CIRP, 33, 556-561.

[2]. Chae, Y. M., Gho, J. S., Mok, H. S., Choe, G. H., & Shin, W. S. (1999, August). A new instantaneous output current control method for inverter arc welding machine. In Power Electronics Specialists Conference, 1999. PESC th 99. 30 Annual IEEE (Vol. 1, pp. 521-526). IEEE.

[3]. Jeon, S. J., & Cho, G. H. (2001). A zero-voltage and zero-current switching full bridge DC-DC converter with transformer isolation. IEEE Transactions on Power Electronics, 16(5), 573-580.

[4]. Klopcic, B., Dolinar, D., & Stumberger, G. (2008). Advanced control of a resistance spot welding system. IEEE Transactions on Power Electronics, 23(1), 144-152.

[5]. Klopcic, B., Stumberger, G., & Dolinar, D. (2007, September). Iron core saturation of a welding transformer in a medium frequency resistance spot welding system caused by the asymmetric output rectifier characteristics. In Industry Applications Conference, 2007. 42^nd IAS Annual Meeting. Conference Record of the 2007 IEEE (pp. 2319-2326). IEEE.

[6]. Morimoto, K., Doi, T., Manabe, H., Ahmed, N. A., Lee, H. W., & Nakaoka, M. (2005a). Advanced high frequency transformer linked soft switching PWM DC-DC power converter with utility AC dual voltage modes for low voltage and large current applications. In 2005 European Conference on Power Electronics and Applications. IEEE.

[7]. Morimoto, K., Doi, T., Manage, H., Ahmed, N. A., & Lee, H. W. (2005b). Advanced high power DC-DC converter using novel type half-bridge soft switching PWM inverter with high frequency transformer for arc welder. In Power Electronics and Drives Systems, 2005. PEDS 2005. International Conference on (Vol. 1, pp. 113-118). IEEE.

[8]. Sabate, J. A., Vlatkovic, V., Ridley, R. B., Lee, F. C., & Cho, B. H. (1990, March). Design considerations for high-voltage high-power full-bridge zero-voltage-switched PWM converter. In Proc. IEEE APEC (Vol. 90, pp. 275-284).

[9]. Saleem, J. (2012). Power electronics for resistance spot welding equipment (Doctoral Dissertation, Mid Sweden University).

[10]. Stumberger, G., Dezelak, K., Klopcic, B., & Dolinar, D. (2012). The impact of the voltage generation method on acoustic noise emissions caused by a welding transformer. IEEE Transactions on Magnetics, 48(4), 1669- 1672.

[11]. Subbanna, S. R., & Suryakalavathi, M. (2016). Detection and control of saturation level in the magnetic core of a welding transformer by ann detector and pi controller. i-manager's Journal on Instrumentation & Control Engineering, 4(4), 37-44.

[12]. Takasaki, Y., & Sonoda, T. (2005). Current controllability of the low-voltage 10 kA inverter power source. IEEE Transactions on Magnetics, 41(10), 4054- 4056.

i-manager's Journal on Instrumentation and Control Engineering (JIC)

Current Issue Vol. 12 Issue 1

Most Read

Most Cited

Volume 6 Issue 4 August - October 2018

Diagnostic Analysis Of The Energy Consumption Of Aluminum Direct Extrusion Operations For Real Time Conditions

Samer As'ad* , Adnan Al-Smadi**, Wael Massarweh***

*Head, Department of Renewable Energy Engineering, Middle East University, Jordan. **Former Dean, Hijjawi College for Engineering Technology, Yarmouk University, Jordan. ***Consultant, Al Hussein Technical University, Amman, Jordan.

As'ad, S., Al-Smadi, A., & Massarweh, W. (2018). Diagnostic Analysis Of The Energy Consumption Of Aluminum Direct Extrusion Operations For Real Time Conditions. i-manager's Journal on Instrumentation and Control Engineering, 6(4), 1-8. https://doi.org/10.26634/jic.6.4.14945

Abstract

References

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Condition Monitoring And Fault Diagnosis Of Rolling Element Bearing

Shayela Farheen Aziz* , Arun Kumar**

*PG Scholar (Instrumentation and Control), Chhattisgarh Swami Vivekananda Technical University, Durg Chhattisgarh, India. **Professor, Department of ETC, BIT Durg, Chhattisgarh, India.

Aziz, F. S., & Kumar, A. (2018). Condition Monitoring And Fault Diagnosis Of Rolling Element Bearing. i-manager's Journal on Instrumentation and Control Engineering, 6(4), 9-17. https://doi.org/10.26634/jic.6.4.14955

Abstract

References

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Adequacy Check-Orifice Plates – A Cost Effective Solution For Brown Field Projects

Shikha Sardana* , Aditi Bhatia**

*-**Control Systems Engineer, Fluor Daniel India Pvt. Ltd., Gurgaon, Haryana, India.

Sardana, S., & Bhatia, A. (2018). Adequacy Check-Orifice Plates A Cost Effective Solution For Brown Field Projects. i-manager's Journal on Instrumentation and Control Engineering, 6(4), 18-26. https://doi.org/10.26634/jic.6.4.15099

Abstract

References

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Optimal Switching Techniques For Aircraft Lateral Dynamics

Pavithra A. C.* , Yathisha L.**, Archana N. V.***

Pavithra, A. C., Yathisha, L., & Archana, N. V. (2018). Optimal Switching Techniques For Aircraft Lateral Dynamics. i-manager's Journal on Instrumentation and Control Engineering, 6(4), 27-34. https://doi.org/10.26634/jic.6.4.14950

Abstract

References

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Intelligent Control System For Resistance Spot Welding System

S. Rama Subbanna* , M. Rajini **

* Associate Professor, Department of Electrical and Electronics Engineering, Sasi Institute of Technology & Engineering, Tadepalligudem, Andhra Pradesh, India. ** Lecturer, Department of Computer Science, SCIM Government Degree and P.G. College, Tanuku, Andhra Pradesh, India.

Subbanna, R. S., & Rajini, M. (2018). Intelligent Control System For Resistance Spot Welding System. i-manager's Journal on Instrumentation and Control Engineering, 6(4), 35-41. https://doi.org/10.26634/jic.6.4.15366

Abstract

References

Full Article (HTML)

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Samer As'ad* , Adnan Al-Smadi, Wael Massarweh*

Head, Department of Renewable Energy Engineering, Middle East University, Jordan.

Former Dean, Hijjawi College for Engineering Technology, Yarmouk University, Jordan.

Consultant, Al Hussein Technical University, Amman, Jordan.

*PG Scholar (Instrumentation and Control), Chhattisgarh Swami Vivekananda Technical University, Durg Chhattisgarh, India.

**Professor, Department of ETC, BIT Durg, Chhattisgarh, India.

Pavithra A. C.* , Yathisha L., Archana N. V.*

* Associate Professor, Department of Electrical and Electronics Engineering, Sasi Institute of Technology & Engineering, Tadepalligudem, Andhra Pradesh, India.

** Lecturer, Department of Computer Science, SCIM Government Degree and P.G. College, Tanuku, Andhra Pradesh, India.