Level: Advanced
André Caron Zanezi, Six Sigma Black Belt, WEG Electric Equipment
Danilo da Silva Toniato, Quality Engineer, WEG Electric Equipment
Quality assurance and customer needs are rigorous terms that frequently refer to reliability. Improving products in terms of reliability challenges engineers in multiple ways, including understanding cause and effect relationships, and developing tests that reproduce customer conditions and properly generate reliable data without exceeding the product launch time deadline. Combining engineering expertise, historical data and lab resources, a design of experiment (DOE) was performed to quantify the product lifetime based on process, product and critical application variables. Performing several analyses using JMP tools, from the DOE platform to the Reliability and Survival modules, the team was able to describe the product lifetime as a function of its critical factors. As a result, an accelerated life test was established which is able to simulate years of product usage in just a few weeks, providing solid evidence of some specific failure modes. Standardizing its methods and procedures, the test became a crucial requirement to verify and validate new technologies implemented at WEG motors, optimizing the development process and reducing time to market.
This poster provides information about how we used JMP to analyze data and develop an accelerated life test. The project followed the step-by-step approach:
Project charter: understanding the primary and secondary objectives, the multidisciplinary team was formed to share information and knowledge about, customer historical data, lab resources, motor reliability, cause and effect relationships, environmental application conditions and reliability data analysis.
Historical data analysis: knowing and quantifying risks about analysing historical data, the team fitted some life distributions to understand Cycles to Failure (CTF) scale and shape parameters. Mainly, shape parameter refers to the failure mode to be reproduced in the lab tests, according to the Bathtub curve.
DOE planning and analysis: in order to reproduce failures, the understanding about motors reliability was endorsed by cause and effect relationships, provided by a Fault Tree Analysis (FTA). The FTA was a source of critical variables combined into a Designed Experiment (DOE) to quantify how to accelerate product cycles to failure.
Conclusions: as a result, DOE provided a surface profiler with the indication of the best condition to accelerate products life time. The accelerated test also provided a shape parameter, that when compared with historical data shows an overlap, meaning that the same historical field failures were reproduced under controlled conditions.
Implementation: with an accelerated test, development and innovative process will became faster while providing important information about product reliability.
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Speaker |
Transcript |
| André Zanezi | So, hello, everyone. My name is Andre Zanezi. And as I am a Six Sigma Black Belt at Weg. And I'm here today in Discovery Summit to talk about the development of an accelerated lifetime test to demonstrate and quantify washing machine motors reliability. |
| We know that some...every company when they are developing new technologies, new solutions, they often | |
| face some challenges when they have to improve their reliability in their product. | |
| We face the same problem, the same challenges. And the project should analyze and understand our historical reliability data to quantify our historical reliability data. | |
| And try to develop a procedure, an accelerated life test in our internal labs, labs to reproduce our failure...our viewed failure modes. Basically doing it, we could develop... | |
| develop models in the first two way. So at the first step, we get some historical data from our motors. | |
| And using reliability and survival modules in JMP, we fit some life distribution for our motors. And we know that | |
| doing in fitting some life distributions as Weibull distributions, we can understand our motors reliability, our motors lifetime. And in JMP, we also can | |
| use lifetime distributions and fit | |
| different distributions for different failure modes. And we did it for four main different failure modes. And we compared it, we analyzed it and understand or understood our ...our motors lifetime, our motors reliability. | |
| And doing it, we were capable to understand and to quantify our scale and shape parameters and it basically doing it, how much cycles was necessary to have a failure. | |
| And also, according to the ??? to know which kind of failure modes we are facing. And we have...we we did also cross check with our internal...our validation KPIs, basically | |
| plotting survival. We have survival plots and cross checking with internal KPI's to understand if the probability and the failure range was correctly with...if our data was reliable. And understanding all these failures modes, we could | |
| we could develop an internal test to accelerate on...accelerated our internal ...our internal test. And basically to do it. We should understand the physics, the environment... | |
| that environment conditions that will our motors are | |
| working in. We did it through fault tree analysis, basically deploying | |
| and understanding the the cause and effect relationships. Doing it, we could set the most critical variables in in this cause and effect relationships. And again, use JMP to do to design an experiment | |
| in order to try to quantify the effect of some variables in our response in our cycles to fail. Basically, we were trying to reproduce field failures in our labs. And we did it, we will run several | |
| tests, and as a result of our experiments, we could have...we could set and fit some using fit model...fits on models to our data and understand the relation of our | |
| environment and motors variables to cycles to failure, and understand to the survival plot, and sort through the surface plot, understand the relation of some variables with cycles to failure, | |
| and set specific point to accelerate our, our motors' lifetime. And again, running some some batch of samples, we could | |
| set and fit lifetime distributions for our internal results, for our internal tests in accelerated life test. A | |
| we were seeing some failures but at the end of this experiment, these accelerated tests, we we should ensure that we are facing and we are causing the same failures as we had in historical data. | |
| So we come back to the lifetime distributions in survival and reliability in survival module. | |
| And again, fit some Weibull distributions now for our internal...internal results for our accelerated lifetime tests. | |
| And we noted that basically they shape parameter, the parameter of that, according to the best to means the filler mode and | |
| we cross this information with our historical data and we can see that crossing both informations, we have an overlap | |
| between the the shape parameter of internal test and the shape parameter of historical data. And it means basically that we are having...we are not reproducing the same failure modes in our accelerated life test. | |
| And basically it means that now we can develop products in a faster way because every time when we have a new technology and new design, we can put it on this accelerated life test | |
| and quantify if we are improving our motors reliability. And we can make it faster than before, and develop faster...develop products in in the faster way. | |
| We also did some technical cross checks to to prove that we are facing in reproducing the same failures to to implement in this this test in into the development process so that that this was how we used JMP to provide a lot of information and put it on our internal test. It was made | |
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| really good teams. Please feel free to make some contact and send email if you have any questions. And that's the end. |