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Risk Assessment in Conformity of Production (COP) Tests
Technical Paper
2017-26-0130
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
In the recent times, there have been number of cases of failure to pass the COP tests. When a vehicle fails a COP test, it is very embarrassing and expensive for the manufacturer as there is a loss of faith by the society and consumers. It is also painful for the certification agency as well as government. In this context, it is important to quantify and minimize the risk associated with these tests for manufacturers as well as certification agencies.
The sampling plan specified in MoRTH / CMVR / TAP-115 is designed to quickly pass vehicles which have very low emissions and quickly reject (fail) vehicles having higher emissions compared to the specified limit. These sampling plans can be classified under Probability Ratio Sequential Tests (PRST). Sampling plans have two types of risks:
- 1Probability of rejecting vehicles complying to the requirements: This is called producers’ risk or alpha risk.
- 2Probability of accepting vehicles not complying to the requirements: This is called consumers’ or beta risk
Sequential sampling plans are aimed at minimizing the sample sizes for very good or very poor quality levels.
This paper will analyze and quantify both these risks and make recommendations to the manufacturer for process targets to minimize the risk of failing in COP.
The author believes this would be interesting and important to the manufacturers as well as certification bodies.
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Citation
Urdhwareshe, H., "Risk Assessment in Conformity of Production (COP) Tests," SAE Technical Paper 2017-26-0130, 2017, https://doi.org/10.4271/2017-26-0130.Also In
References
- Type Approval Procedure document MoRTH/CMVR/TAP-115-116 4
- Principles of acceptance sampling Kaul Renu and Chowdhury S. Roy
- U.N.E.C.E. Regulation No. 83-06 Uniform Provisions Concerning the Approval of: Vehicles with Regard to the Emission of Pollutants According to Engine Fuel Requirements
- The Efficiency of Sequential Sampling Plans Based on Prior Distributions and Costs Pfanzagl J. and Schüler W.
- Introduction to Monte Carlo Simulation Raychaudhuri Samik Proceedings of the 2008 Winter Simulation Conference