@inproceedings{scholars10140,
            note = {cited By 4; Conference of 2018 IEEE Conference on Systems, Process and Control, ICSPC 2018 ; Conference Date: 14 December 2018 Through 15 December 2018; Conference Code:147803},
            year = {2018},
             doi = {10.1109/SPC.2018.8704134},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
         journal = {Proceedings - 2018 IEEE Conference on Systems, Process and Control, ICSPC 2018},
           title = {Visualizing and Predicting Reliability of Control Valves based on Simulation},
           pages = {54--59},
          author = {Mathur, N. and Asirvadam, V. S. and Abd Aziz, A. and Ibrahim, R.},
            isbn = {9781538663271},
        keywords = {Curve fitting; Flow visualization; Forecasting; Industrial plants; Maintenance; Redundancy; Reliability; Safety valves; Weibull distribution, Bathtub curve; Control instruments; Control valves; High reliability; Prediction accuracy; Quantitative method; Shape and size; Stand-by systems, Process control},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065987065&doi=10.1109\%2fSPC.2018.8704134&partnerID=40&md5=14f1640c80e2cabe0cf922417e176430},
        abstract = {An industrial plant assembled with many components, standby systems, sensors, controlling components to make smooth operation of plants. Losses produced by unplanned downtime may leads to major problems. Although, control valves which are used to control the rate of flow for pressure or liquid is an essential part of the plants operation. Since to reduce the losses or downtime damage, high reliability and standby availability is required to monitor. Hence, it requires some prediction which can express the likelihood of the events in the terms of quantitative methods. This research performs the prediction for reliability on the control instruments and its availability that is visualized for an quantitative prospective. By using Weibull distribution and calculating pdf and cdf for shape and size parameters for prediction and estimation of the consequences of these failure depends on the control valve assembled in process plant. Validating with estimation maximization(EM) technique and fitting it in curve to achieve 95 prediction accuracy. This research will give practical assessments of reliability of control valve for better actions and higher reliable redundancy. {\^A}{\copyright} 2018 IEEE.}
}