In-process tracking and tractability for zero-defect manufacture of electrical machines

The challenge in building systems for solving some of the activities above, particularly when constrained with limited data has led to a number of strategies within machine learning.

Specific architectures have been engineered to handle ‘few-shot’ and ‘one-shot’ learning to allow for accurate training when presented with novel examples. Alternatively, approaches that focus on the data itself through traditional data augmentation or where possible the use of synthetic data generation have also taken hold as a means to improve performance. 

It is here that we will look to explore two approaches to try and boost performance on a specific task, the first through the use of a model for creating novel images and the second through a more direct method using CAD tools to auto generated synthetic data that matches our use-case.

One thing that is always interesting about the field of machine learning and AI, in particular within deep neural networks is that new methods or architectures are created which shift the needle on performance or open up new abilities that seemed once impossible. In the past starting off with classification problems we had AlexNet and subsequent residual convolution neural networks, while the recent Transformer models showcase a rapid dominance of natural language processing. 

Another class of model, ‘generative adversarial networks’ or GANs brought new ways to generate data from a specific distribution, in essence when trained on human faces, we are then able to produce novel ones of extraordinary accuracy. So, starting with this area of generative models, we can look to train a model whose task it is to create new images from a particular distribution, and if possible, under additional conditions, for example if we were to target a specific class of failure.

An example can be seen in figure 2, where we have a number of classes (pass, gap failure, crossover failure) and we wish to eventually train a classification model to correctly identify each during a manufacturing process in real life. As discussed earlier gathering this data is time consuming and often difficult, even in a toy example such as this, but we can exploit the power of GANs to take over this task and create novel images from these three example classes, even with a small dataset starting point.

In figure 3 we can see such a model being trained over time to produce the images we seek, in this case a re-purposed model originally trained to generate faces is now able to produce a variety of examples of our target set of coil failures.

The next question arises in what way does this improve the performance of the more traditional deep neural network (DNN) classification models. Well, to keep it short, when a small dataset of original images has been ‘boosted’ with additional data, in this case our GAN generated images we find an increase in accuracy from 65% to 87% over the three classes of coil winding failure. 

This is a significant improvement, and we can also look to go further in the future with models that can be semantically aware, and when fed with a specific ‘mask’ of values we can produce targeted images that contain the faults we want spatially within an image as shown in figure 4.

The first example using GANs can work, but the degree of accuracy in both texture and resolution of the generated images can sometimes mean the output is not as accurate as we would like. The image datasets used to train the model are often constructed of images that are spatially similar, in this case the coils are central with little variation beyond the original faults. 

In some problems, this uniformity will not be present and can make such models harder to train. To overcome this, we can look to produce synthetic images using computer software, for example as has recently been done within the area of autonomous driving to use driving games such as Grand Theft Auto 5. Alternatively, we can use CAD software such as Blender to render 3D scenes, and script this image generation process to greatly vary the outcome to aid in training of machine vision models.

Producing and labelling images for training of machine vision models can be a laborious task. An example of a hand labelled dataset for a simple cable assembly task can be seen in figure 5 and includes object detection bounding boxes and individual semantic masks (cable/hand). 

Automating this brings with it numerous benefits, particularly in cost and time, however we want to ensure that the outcomes match reality as best as possible when training. Adding complex lighting, textures and shadows can help, and varying the camera angle and distance can help generalise the images dataset created to what may exist in the wild. Examples of such images can be seen in figure 6.