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]

Hardware

Hardware Reliability Verification includes temperature and humidity test, mechanical vibration test, shock test, collision test, drop test, dustproof and waterproof test, and other environmental reliability tests.[15][16]

Growth in safety-critical applications for automotive electronics significantly increases the IC design reliability challenge.[17][18]

Hardware Testing of Electric Hot Water Heaters Providing Energy Storage and Demand Response Through Model Predictive Control is from Institute of Electrical and Electronics Engineers, written by Halamay, D.A., Starrett, M and Brekken, T.K.A. The author first discusses that a classical steady state model commonly used for simulation of electric hot water heaters can be inaccurate. Then this paper presents results from hardware testing which demonstrate that systems of water heaters under Model Predictive Control can be reliably dispatched to deliver set-point levels of power to within 2% error. Then the  author presents experiment result which shows a promising pathway to control hot water heaters as energy storage systems is  capable of delivering flexible capacity and fast acting ancillary services on a firm basis.

Advanced Circuit Reliability Verification for Robust Design, a journal discuss the models used on circuit reliability verification and application of these models. It first discusses how the growth in safety-critical applications for automotive electronics significant increases the IC design reliability challenge. Then the author starts to discuss the latest Synopsys' AMS solution for robust design. This part of the article is very technical, mostly talking about how AMS can strengthen the reliability for full-chip mixed-signal verification. This article can be a useful source for investigating why it is important to focus more on reliability verification nowadays.

Software

A Pattern-Based Software Testing Framework for Exploitability Evaluation of Metadata Corruption Vulnerabilities developed by Deng Fenglei, Wang Jian, Zhang Bin, Feng Chao, Jiang Zhiyuan, Su Yunfei discuss how there is increased attention in software quality assurance and protection. However, today’s software still unfortunately fails to be protected from cyberattacks, especially in the presence of insecure organization of heap metadata. The authors aim to explore whether heap metadata could be corrupted and exploited by cyber-attackers, and they propose RELAY, a software testing framework to simulate human exploitation behavior for metadata corruption at the machine level. RELAY also makes use of the fewer resources consumed to solve a layout problem according to the exploit pattern, and generates the final exploit.

A Methodology to Define Learning Objects Granularity developed by BENITTI, Fabiane Barreto Vavassori. The authors first discuss how learning object is one of the main research topics in the e-learning community in recent years and granularity is a key factor for learning object reuse. The authors then present a methodology to define the learning objects granularity in the computing area as well as a case study in software testing. Later, the authors carry out five experiments to evaluate the learning potential from the produced learning objects, as well as to demonstrate the possibility of learning object reuse. Results from the experiment are also presented in the article, which show that learning object promotes the understanding and application of the concepts.

A recent article, Reliability Verification of Software Based on Cloud Service, have a ground breaking effect and it explores how software industry needs a way to measure reliability of each component of the software. In this article, a guarantee-verification method based on cloud service was proposed. The article first discusses how trustworthy each component's are will be defined in terms of component service guarantee-verification. Then an effective component model was defined in the article and based on the proposed model, the process of verifying a component service is illustrated in an application sample.

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): 1–6. doi:10.1109/RAMS.2016.7448014.
  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.
  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. doi:10.3390/s20154344.
  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. 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.
  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. 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): 1–7. arXiv:1908.00314. doi:10.1109/NORCHIP.2018.8573495.
  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.
  15. Weber, Wolfgang; Tondok, Heidemarie; Bachmayer, Michael (2003). Anderson, Stuart; Felici, Massimo; Littlewood, Bev (eds.). "Enhancing Software Safety by Fault Trees: Experiences from an Application to Flight Critical SW". Computer Safety, Reliability, and Security. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer: 289–302. doi:10.1007/978-3-540-39878-3_23. ISBN 978-3-540-39878-3.
  16. Jung, Byung C.; Shin, Yun-Ho; Lee, Sang Hyuk; Huh, Young Cheol; Oh, Hyunseok (January 2020). "A Response-Adaptive Method for Design of Validation Experiments in Computational Mechanics". Applied Sciences. 10 (2): 647. doi:10.3390/app10020647.
  17. Fan, A.; Wang, J.; Aptekar, V. (March 2019). "Advanced Circuit Reliability Verification for Robust Design". 2019 IEEE International Reliability Physics Symposium (IRPS): 1–8. doi:10.1109/IRPS.2019.8720531.
  18. 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.
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