In the wave of smart manufacturing and lean transformation, the "reliability" of products and systems is no longer a traditional quality control indicator, but a core competitiveness that affects customer satisfaction, brand trust and operational efficiency. From consumer electronics and automotive parts to industrial equipment and semiconductor products, reliability analysis is rapidly becoming an indispensable and critical capability for engineering teams.
Reliability refers to the ability of a product to operate without failure under specified time and usage conditions. From the perspective of data analysis, we usually use the "life distribution model" to describe the failure time distribution of products. The most common one is the Weibull distribution, which can accurately describe the failure rate and risk function at different time points.
In actual work, reliability engineers often face the following three types of data:
- Complete observation data : records the failure events of the product at a certain point in time;
- Right-censored data : The product has not yet expired at the end of the observation period.
- Accelerated life test data : simulate the product failure process through stricter conditions.
This data can not only be used to predict product life, but also support applications such as spare parts reserves and maintenance cycle planning, thereby reducing risks and improving operational efficiency.
Why do we need to do lifespan analysis? Let’s look at a case study
Taking fan products as an example, suppose we have collected life data of 70 fans, including some complete failure events and some right-censored data. We can use this data to build a life distribution model to estimate questions such as "how many fans will fail before 6000 hours?"
A common indicator in the manufacturing industry is the B10 life , which is the product life corresponding to a 10% probability of failure . If the B10 value of a product can meet or exceed customer requirements, it means that its reliability meets the standard; otherwise, further improvement of product design or process is needed.
Can’t wait for the product to “wear out”? Accelerated lifespan experiments to help
Some products may take months or even years to fail under normal use conditions, making it difficult to observe their lifespan through natural means. At this time, accelerated life testing is needed.
For example, we want to evaluate the time required for 10% of the products to fail under the normal use torque of 35 Nm . However, under actual conditions, achieving this ratio may require tens of thousands of cycles, which is very time-consuming. Therefore, we can conduct stricter tests at higher torques (such as 50 or 100 Nm ) and use the Weibull model to deduce the reliability indicators under normal conditions.
Want to learn more about how to actually implement reliability analysis in engineering projects? Welcome to JMP Taiwan ’s free live course on May 23, 2025. Professor Shi-Ming Chen, Professor of the Department of Electronic Engineering at Chang Gung University and Director of the Reliability Science and Technology Research Center, and JMP data analysis consultant will jointly present [ Understand the Practical Reliability Engineering at Once: 4 Practical Case Studies to Get You Started with Reliability Analysis ], sharing the important concepts of reliability analysis and combining 4 industrial cases to deeply analyze the practical applications from life prediction, accelerated testing to failure modes.
If you missed the live webinar, here are some of JMP ’s online resources for reliability, or get in touch with our consultants to discuss your needs:
