Reliability verification and Application

YiTing Yan

The twenty first century is the century of technology. Both hardware and software, are seeing  enormous growth. We cannot live without our phones, apps, not to mention airplanes, air conditioners, and water heaters. As technology becomes essential to our daily life, we want to ensure our product to be safe to use and have good quality. All of this implies that reliability verification is becoming important for most technology industries, since it ensures that the products we buy achieves a certain standard when we purchase them. 

The goal of reliability verification is to ensure that the product developed can survive expected use circumstances. For example, phones may require testing it underwater for water resistant, or testing is in -30 degree celsius, but for airplanes, or self-driving cars, they may have much higher standards for reliability verification testing since they are directly related to the safety of users. 

There are many criteria to test depending on the product or process that are testing on, and mainly, there are five components that are most common : product life span, intended function, operating condition, Probability of performance and user exceptions. Companies follow this testing criteria to design the testing process and make sure the product performs expectedly under these circumstances. One article called “A System Approach to Reliability Verification Test Design” proposes a system approach to reliability testing. The author of this article believes that there are two major downsides of reliability verification tests in the modern automotive and other industries. Most reliability requirements tests are not clearly determined on how it relates to the product's overall reliability performance and how individual tests impact the warranty cost and the customer satisfaction. One model that the author proposed to combat this problem has three steps: product reliability goal is first determined by performance requirement, then individual tests are linked to field performance, and third, how to allocate the overall reliability goal to the reliability targets of each individual test.

For hardware testing such as hot water heaters, a paper “Hardware Testing of Electric Hot Water Heaters Providing Energy Storage and Demand Response Through Model Predictive Control” wrote by Halamay, D.A., Starrett, M and Brekken, T.K.A talk about how classical steady state model commonly used for simulation of electric hot water heaters can be inaccurate, and therefore they proposed a testing mechanism that 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. This showshow reliability verification can be very helpful in real life. 

Besides day to day product, in one article from Plos One, written by Haiyang Xu and Ping Wang called “Real-Time Reliability Verification for UAV Flight Control System Supporting Airworthiness Certification” proposed a model-based integration framework for modeling and verification that are capable to verify the real-time reliability of unmanned aerial vehicle flight control system and comply with the airworthiness certification standard. This model needs extensive testing since it will be one of the many parts that will be in control of unmanned aerial vehicles. 

References

  1. Xu, Haiyang, and Ping Wang. 2016. “Real-Time Reliability Verification for UAV Flight Control System Supporting Airworthiness Certification.” PloS One 11(12):e0167168.

  2. Weber, Wolfgang, Heidemarie Tondok, and Michael Bachmayer. 2005. “Enhancing Software Safety by Fault Trees: Experiences from an Application to Flight Critical Software.” Reliability Engineering & System Safety 89(1):57–70.

  3. Chen, Jing, Yinglong Wang, Ying Guo, and Mingyue Jiang. 2019. “A Metamorphic Testing Approach for Event Sequences.” PloS One 14(2):e0212476.

  4. Halamay, Douglas A., Mike Starrett, and Ted K. A. Brekken. 2019. “Hardware Testing of Electric Hot Water Heaters Providing Energy Storage and Demand Response through Model Predictive Control.” IEEE Access: Practical Innovations, Open Solutions 7:139037–57

  5. 14. Zhang, Jiliang, Celine Geiger, and Feng-Bin Sun. 2016. “A System Approach to Reliability Verification Test Design.” Pp. 1–6 in 2016 Annual Reliability and Maintainability Symposium (RAMS). IEEE.