Reliability verification

Reliability verification or reliability testing is a method to evaluate the reliability of the product in all environments such as expected use, transportation, or storage during the specified lifespan.[1] It is to expose the product to natural or artificial environmental conditions to undergo its action to evaluate the performance of the product under the environmental conditions of actual use, transportation, and storage, and to analyze and study the degree of influence of environmental factors and their mechanism of action.[2] Through the use of various environmental test equipment to simulate the high temperature, low temperature, and high humidity, and temperature changes in the climate environment, to accelerate the reaction of the product in the use environment, to verify whether it reaches the expected quality in R&D, design, and manufacturing.[3]

Description

Reliability is the probability of a product performing its intended function over its specified period of usage and under specified operating conditions, in a manner that meets or exceeds customer expectations.[4] Reliability verification is also called reliability testing, which refers to the use of modeling, statistics, and other methods to evaluate the reliability of the product based on the product's life span and expected performance.[5] Most product on the market requires reliability testing, such as automotive, integrated circuit, heavy machinery used to mine nature resources, Aircraft auto software.[6][7]

Reliability criteria

There are many criteria to test depends on the product or process that are testing on, and mainly, there are five components that are most common:[8][9]

  1. Product life span
  2. Intended function
  3. Operating Condition
  4. Probability of Performance
  5. User exceptions[10]

The product life span can be split into four different for analysis. Useful life is the estimated economic life of the product, which is defined as the time can be used before the cost of repair do not justify the continue use to the product. Warranty life is the product should perform the function within the specified time period. Design life is where during the design of the product, designer take into consideration on the life time of competitive product and customer desire and ensure that the product do not result in customer dissatisfaction.[11][12]

Testing method

A systematic approach to reliability testing is to, first, determine reliability goal, then do tests that are linked to performance and determine the reliability of the product.[13] A reliability verification test in modern industries should clearly determine how they relate to the product's overall reliability performance and how individual tests impact the warranty cost and customer satisfaction.[14]

See also

References
  1. Tang, Jianfeng; Chen, Jie; Zhang, Chun; Guo, Qing; Chu, Jie (2013-03-01). "Exploration on process design, optimization and reliability verification for natural gas deacidizing column applied to offshore field". Chemical Engineering Research and Design. 91 (3): 542–551. doi:10.1016/j.cherd.2012.09.018. ISSN 0263-8762.
  2. Zhang, J.; Geiger, C.; Sun, F. (January 2016). "A system approach to reliability verification test design". 2016 Annual Reliability and Maintainability Symposium (RAMS). pp. 1–6. doi:10.1109/RAMS.2016.7448014. ISBN 978-1-5090-0249-8. S2CID 24770411.
  3. Dai, Wei; Maropoulos, Paul G.; Zhao, Yu (2015-01-02). "Reliability modelling and verification of manufacturing processes based on process knowledge management". International Journal of Computer Integrated Manufacturing. 28 (1): 98–111. doi:10.1080/0951192X.2013.834462. ISSN 0951-192X. S2CID 32995968.
  4. Tang, Jianfeng; Chen, Jie; Zhang, Chun; Guo, Qing; Chu, Jie (2013-03-01). "Exploration on process design, optimization and reliability verification for natural gas deacidizing column applied to offshore field". Chemical Engineering Research and Design. 91 (3): 542–551. doi:10.1016/j.cherd.2012.09.018. ISSN 0263-8762.
  5. "Reliability Verification for AI and ML Processors - White Paper". www.allaboutcircuits.com. Retrieved 2020-12-11.
  6. Weber, Wolfgang; Tondok, Heidemarie; Bachmayer, Michael (2005-07-01). "Enhancing software safety by fault trees: experiences from an application to flight critical software". Reliability Engineering & System Safety. Safety, Reliability and Security of Industrial Computer Systems. 89 (1): 57–70. doi:10.1016/j.ress.2004.08.007. ISSN 0951-8320.
  7. Ren, Yuanqiang; Tao, Jingya; Xue, Zhaopeng (January 2020). "Design of a Large-Scale Piezoelectric Transducer Network Layer and Its Reliability Verification for Space Structures". Sensors. 20 (15): 4344. Bibcode:2020Senso..20.4344R. doi:10.3390/s20154344. PMC 7435873. PMID 32759794.
  8. Matheson, Granville J. (2019-05-24). "We need to talk about reliability: making better use of test-retest studies for study design and interpretation". PeerJ. 7: e6918. doi:10.7717/peerj.6918. ISSN 2167-8359. PMC 6536112. PMID 31179173.
  9. Pronskikh, Vitaly (2019-03-01). "Computer Modeling and Simulation: Increasing Reliability by Disentangling Verification and Validation". Minds and Machines. 29 (1): 169–186. doi:10.1007/s11023-019-09494-7. ISSN 1572-8641. OSTI 1556973. S2CID 84187280.
  10. Halamay, D. A.; Starrett, M.; Brekken, T. K. A. (2019). "Hardware Testing of Electric Hot Water Heaters Providing Energy Storage and Demand Response Through Model Predictive Control". IEEE Access. 7: 139047–139057. doi:10.1109/ACCESS.2019.2932978. ISSN 2169-3536.
  11. Chen, Jing; Wang, Yinglong; Guo, Ying; Jiang, Mingyue (2019-02-19). "A metamorphic testing approach for event sequences". PLOS ONE. 14 (2): e0212476. Bibcode:2019PLoSO..1412476C. doi:10.1371/journal.pone.0212476. ISSN 1932-6203. PMC 6380623. PMID 30779769.
  12. Bieńkowska, Agnieszka; Tworek, Katarzyna; Zabłocka-Kluczka, Anna (January 2020). "Organizational Reliability Model Verification in the Crisis Escalation Phase Caused by the COVID-19 Pandemic". Sustainability. 12 (10): 4318. doi:10.3390/su12104318.
  13. Jenihhin, M.; Lai, X.; Ghasempouri, T.; Raik, J. (October 2018). "Towards Multidimensional Verification: Where Functional Meets Non-Functional". 2018 IEEE Nordic Circuits and Systems Conference (NORCAS): NORCHIP and International Symposium of System-on-Chip (SoC). pp. 1–7. arXiv:1908.00314. doi:10.1109/NORCHIP.2018.8573495. ISBN 978-1-5386-7656-1. S2CID 56170277.
  14. Rackwitz, R. (2000-02-21). "Optimization — the basis of code-making and reliability verification". Structural Safety. 22 (1): 27–60. doi:10.1016/S0167-4730(99)00037-5. ISSN 0167-4730.
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