gaussian distribution endurance test

Asked Feb 02 '23 at 07:20

Active Feb 09 '23 at 14:08

Viewed 23 times

I am conducting an endurance test experiment on spare parts. I put these part under stress for long hours to see when they start failing. I have a total amount of 42 spare parts.

1 part failed at run-time between 1000 and 1500 hours 4 parts failed part failed between 1500 and 2000 hours 2 parts failed between 2000 and 2500hours 35 parts finished the endurance run at 2500h without defect

Assuming failure curve is log-normal distributed is there a way to infer the possible mean and standard deviation of such a curve from the information given? The issue I have is that my dataset is imbalanced with all sample coming from the leftmost portion of the left tail.

edited Feb 09 '23 at 14:08

asked Feb 02 '23 at 07:20

Selam Getachew

Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking. – Community Feb 02 '23 at 07:28
I have rephrased the question. I hope I am clear. Not a native speaker here. – Selam Getachew Feb 02 '23 at 07:39
Why do you think the data follows the Gaussian distribution? From your description nothing seems to suggest this. – Tim Feb 02 '23 at 08:54
The part design expert think so. It may not indeed. – Selam Getachew Feb 02 '23 at 09:22
Do you have some data? Can you show us a plot? – kjetil b halvorsen Feb 02 '23 at 13:27

gaussian distribution endurance test

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