Implementing Generative Adversarial Networks for industrial reliability

Implementing Generative Adversarial Networks for industrial reliability

We all have heard of AI, Machine learning, deep learning, data engineering, and a lot of these terms (jargons) recently.

But where does all this start from?

Well, the answer is none other than your very own “DATA”. Data is the key to the world of Artificial Intelligence. At Eugenie, for example, we offer predictive maintenance by leveraging AI and machine learning techniques. There are various algorithms and models which are applied to the collected data. This process works fine when we have the correct amount of historic data to refer to. Despite, the age of digital transformation, we do understand that machines are still perceived as a physical entity with a limited scope, and the question remains that what if we don’t have the right data OR the adequate amount of data for prediction? What happens next and has Eugenie considered that scenario?

The original generative adversarial network (GAN) model
The original generative adversarial network (GAN) model.(Ref:-https://www.mdpi.com/)

Well, the answer to that is an absolute YES. This is where our cutting-edge technology and patented algorithms come to your rescue. Our models are based on GAN technology.

GANs (Generative Adversarial Networks) is a type of Neural network used to perform unsupervised machine learning tasks. In simple language, these models can generate exactly the type of data your machine would produce after mapping various parameters, making it highly domain and machine-specific in nature.

GANs are not only used extensively in the Industrial sector but are applied in various domains like – Security, Computer Vision, Image editing, 3D Object generation, etc.

The most important asset for a manufacturing company is its machinery. Any unexpected downtime or degradation can cause a significant loss for your company. At Eugenie, we practice predicting asset failure with utmost accuracy. This is done by understanding an anomaly or its possibility within your systems. These alerts are then translated into our dashboards for you to visualize and understand in a more human-friendly way. Sounds easy, right?

Well, it’s not so simple.

Every industry has its own set of problems, and every anomaly is not a damaging one. So how do we exactly differentiate between what exactly is an anomaly and what is not?

Out of the massive amount of data, your assets generate very little help in possibly understanding the anomaly data points. Once we identify them, our GAN models continuously monitor them to help create a similar sample space for analyzing future machine behavior. After successful training, we freeze the parameters of the discriminator, while the generator keeps modifying.

GANs are one of the best neural network applications when finding the unknown from very little available data. Though very useful, there are multiple challenges involved while implementing a GAN architecture. This includes deciding the dimension of the latent space for the Generator. Since the Generative model is transitioning from a lower dimension (AKA latent space) which consists of a lot of noise to the original space (At which the machine operates). A common problem that arises during the training of these models is “Loss Saturation”. Everyone wishes for high accuracy and minimum loss. This value of the loss, though decreasing with every cycle, sits stagnant after a certain period of time.

Like various neural networks, the accuracy for GANs is also calculated using various metrics like ROC, AUC (Area under the curve) decides the ability of the classifier to distinguish between the two classes, Cross validations, and testing the model against a certain benchmark dataset.

Unlike other Machine Learning models, the major purpose of GANs is not to predict any label with respect to the data but try to learn how the process is distributed.

In conclusion, we can say that GANs (Generative Adversarial neural networks) are used in a lot of Unsupervised machine learning space, where you have a scarcity and random data points. This technology is thereby well suited for Industrial applications with either very little or voluminous amounts of data.

To know more about Eugenie’s offerings, get in touch with us

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