Use of Statistical Quality Control for Estimating the Machine Interference

Although it is important to test quality of coal used in thermal power station, it is more important to know how to interpret the data from the results. No single test can be used independently to determine the quality of coal. Machine interference is a significant problem in many manufacturing system and testing equipments. The variation of results for testing equipments may be due to various factors which need to calculate the uncertainty of measurement to show the accuracy of the machine. In case of coal testing laboratory, the plant layout and surrounding environment affects the performance of the system. The machine interference comes under variable cause which may effect on the result. This paper, proposes a methodology for constructing system performance measures, finding out the various factors responsible for variations in result. The study is based on performance of coal used in thermal power station in India. The paper deals with estimation of machine interference existence using variable control chart approach for coal testing equipments. The paper gives the solution for the coal testing laboratory to improve their performance. The analysis of results for such machine interference will be useful and significant for system designers and practitioners. DOI: 10.4018/ijmtie.2012010102 18 International Journal of Measurement Technologies and Instrumentation Engineering, 2(1), 17-34, January-March 2012 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. quality control. The purpose of this discussion is to outline a simple statistical procedure that is widely applicable and practical in testing laboratory (Westgard, Barry, & Hunl, 1981). In case of material testing, the result of a measurement is only an approximation to the value of the measurand and is only complete when it is accompanied of the uncertainty. Computing the reported test result is straightforward, however, computing the uncertainty associated with the test result requires more consideration. The uncertainty evaluation process will encompass a number of influences quantities that affect the result obtained for the measurand. In order to quantify the uncertainty we will have to consider all the factors that could influence the results. Material testing measurement process is always doubtful about the value of quantity to be measured which provides the basis for safe working of components during their operations therefore, it is needed that measurement professionals should be need to be explained to their clients the value of their work including its limitations. A reading will give the false impression that the measurement is accurate. As there are other variables which would be accounted for, critical applications those will rely upon if measurements might have errors. Those experienced with measuring equipment are typically aware of this fact and often provide a guest mate of a reading to the best of their knowledge. But skill and experience often plays a role in how accurate those guest mates might be. However, as the benefits of measurement uncertainty (UOM) are becoming more widely known, the practice is being put to use with greater credibility in many critical fields. Because a realistic uncertainty measurement can account for actual variables that may be encountered, possible solutions can be provided and procedures can be enacted to account for those variables. A realistic uncertainty reading also lends greater credibility to the measurement information being produced. International standards and procedures have been created that outlines how to take a realistic measurement uncertainty reading utilizing a tool known as the “Uncertainty Budget” (Mandavgade, Jaju, & Lakhe, 2011b). Variations due to chance causes are inevitable in any process or product. They are difficult to trace and difficult to control even under best conditions of testing. It has been established that if the variations are due to chance factors alone, the observations will follow a ‘normal curve.’ Knowledge of behavior chance variation is the foundation on which the control chart analysis rests (Mahajan, 2001). 2. FACTORS INVENTORY The uncertainty of measurement gets affected by qualitative factors as well as quantitative factors. The effects of quantitative factors can be determined using analytic approach and data from calibration certificates, instruments manual, etc. (Mandavgade, Jaju, & Lakhe, 2011a). In case of qualitative terms, UOM is affected by various terminologies applicable to that particular case. According to the expert suggestions or opinions, questioners filled by different peoples in particular field and practical experience of the author in field, some of the qualitative factors applicable for M/s. S.K. Mitra coal testing laboratory, Nagpur (India) are as below (Figure 1): 2.1. Operator: The operator will affect the performance of test in term of following factors. 2.1.1. Field Knowledge of Operator: If the operator is not skilled enough or if he lacks in the knowledge of a particular instruments and its procedure then the testing results may not be so accurate. The effects because of the field knowledge of operator are not possible to calculate in numerical terms. 2.1.2. Experience of the Operator: Experience plays a vital role in case of testing and measuring procedure. Experience somewhat related to field of 16 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the product's webpage: www.igi-global.com/article/use-statistical-quality-controlestimating/72699?camid=4v1 This title is available in InfoSci-Journals, InfoSci-Journal Disciplines Engineering, Natural, and Physical Science. Recommend this product to your librarian: www.igi-global.com/e-resources/libraryrecommendation/?id=2