The term Deep Learning becomes very familiar to Machine Learning enthusiasts. Many companies have gained a great reputation in the field of science thanks to the successful application of Deep Learning. So, how does it apply to production? Let's explore with New Ocean!
We often come across novel terms such as Computer Vision, Machine Learning, Deep Learning, in which Deep Learning is a deep learning neural network, a subset of Machine Learning that has networks capable of learning unsupervised from unstructured or unbroken data.
Digital and the application of techniques have been applied in the manufacturing field for a long time. Information storage and measurement became digitized, and computers joined in the process. The explosion of data makes traditional models inadequate and this is where Deep Learning comes into play.
In a large assembly line, Deep Learning may be involved in quality control tasks. According to Forbes, it has been proven that workflows, analytics based on Deep Learning predict a 35% increase in quality optimization and automation is expected to increase 34%.
With machines operating in the traditional way, they are only capable of detecting quality problems against the length or weight data of the product. In contrast to computer vision systems, it is possible to detect quality problems and then provide appropriate solutions to overcome them.
Deep Learning allows for more extensibility than legacy solutions and trained and reused when the manufacturing plant needs them. To fully apply deep learning, businesses need to invest in system training with relevant data images.
Enterprises need to monitor the process closely and detect abnormalities in that process to improve product quality and production productivity.
Traditional methods such as statistical process control, however, with such old methods cannot guarantee high accuracy as the number of interacting variables increases and changes in real time.
For this task, Deep Learning will detect anomalies that dimensionality reduction techniques such as PCA (Principal Component Analysis) are used to process the media statistics signal. In addition, to support process monitoring and error detection, a static or diverse Autoencoder can be used. In short, the deep learning automation controller will solve the problem of enterprise process monitoring.
Optimizing input resources in the production process is considered an important factor to increase profits for businesses. Two factors that businesses need to pay attention to to optimize are electricity and water. For large-scale factories where simple algorithms can hardly manage the flow of resources, this is the time to apply Deep Learning to processes like central neural networks.
Deep learning systems that monitor power usage patterns as a function of hundreds of plant process parameters automatically suggest best practices and solutions for optimal use of input materials. This. If the plant turns to renewable energy, the deep learning algorithm will help map out the optimal transition trajectories to a sustainable energy source. Deep Learning plays this transformation well, and classical predictive analytics is difficult to do well.
Deep Learning applied to production has become an important prerequisite for the success of Industry 4.0. Smart manufacturing with great prospects will meet increased productivity, improve product quality and meet company profits. The transformation of production model thanks to the successful application of deep learning will ensure to provide consumers with affordable products with the best quality.
Predictive Maintenance in Deep Learning
Deep Learning model has proven to be highly effective in the field of economics and finance. In predictive maintenance, data is collected in real time, so Deep learning greatly aids in predictive maintenance of complex machines and connected systems.
A particularly difficult task for businesses is determining when to carry out equipment maintenance. Every time the machine is brought in for maintenance, it will result in reduced output or even machine downtime. Therefore, regular repair will cause obvious losses, but if not regularly maintained, it can cause problems and even cost businesses.
Therefore, automated feature engineering is very important for businesses. Traditional ML algorithms for predictive maintenance depend on narrow, domain-specific expertise on manual features to detect machine health problems. Whereas a neural network can automatically infer those features with sufficiently high quality training data.
So to make the most of this explosion of data, it must be integrated into the toolkit of modern manufacturing systems, as they are many times more powerful than traditional statistical prediction and learning systems. system.
The increase in productivity and quality is expected to go beyond the company's goal of meeting profitability. In the future, smart manufacturing will enrich the lives of consumers by providing high-quality goods and services at an affordable cost.
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