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Detecting faulty EL photos with machine learning (ML)

Can two EL photos of two panels be identical? No. Every EL (electroluminescence) photo is different. What if the same photos come from the factory for different serial numbers?

EL photos coming out of solar panel factory lines are very similar to each other. For this reason, your chances of saying “I have seen this photo before” are very low. During our inspection of a solar panel factory, we noticed an identical EL photo by chance. Of course, we had our doubts; what if there were other photos that we missed?

The EL images above are identical. The two panels have different serial numbers inside. And the solution? The solution is in software, in machine learning.

Python and Tensorflow came to the rescue.

Using the MobileNet library, which is pre-trained to detect similar photos using convolutional neural networks (CNN) inside Tensorflow, we ran our own code on thousands of EL photos and automatically generated reports. Below is an example from our report page; the first example at the top and two other files containing the same image below.

As a result of the software’s analysis, we found that some photos were used in more than one serial number. Of course, this is not something that the factories do intentionally; some problems in the software can also cause such problems. IT intervened quickly and eliminated the problem in about a week. After this stage, this control became our standard procedure and we encountered this problem in 77% of the factories we audited.

It is really important to have production inspected in solar panel factories. There is no rule that the manufacturer will necessarily make bad production, but why shouldn’t you buy the best product coming off the lines? If you are considering panel supply, you should definitely have your production line inspected. The more a factory is open to inspection, the more confident it is.

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