Artificial Intelligence (AI) is the ability for a machine to take in information, analyze it and come to conclusions based on that analysis, and it is taking the world by storm. The idea behind AI is that we can program a machine to mimic the learning and decision-making capability of humans, therefore allowing us to automate processes.
AI has many applications nowadays, such as:
- speech recognition;
- facial recognition;
- medical diagnosis;
- automated customer support;
- machine learning and deep learning
The AI global market share is expected to reach nearly $170 billion by 2025 and this technology is being adopted by numerous industries, including:
- healthcare and medicine;
- human resources;
- retail and eCommerce;
- public relations;
According to a McKinsey survey, 47% of respondents say they have incorporated at least one AI capability into their business operations. Within the manufacturing sector, AI, specifically machine learning, is being used on the factory floor. In particular, it has specific and impressive application when it comes to machine operations and maintenance.
Deep learning: how it works
Deep learning is a subset of machine learning. The latter uses an algorithm to analyze data, detect patterns in those data, learn from them and adjust processes and operations accordingly, without the need for human intervention. Usually, machine learning requires a structured and labelled data set to use as a reference for the analysis, and this is referred to as supervised machine learning. However, this poses limitations, which include the tediousness of labelling data, data bias, and difficulty in transferring what is learned in one situation to another.
When it comes to Deep Learning, these limitations are minimized because this is a form of unsupervised machine learning that does not require a structured and labelled reference data set.
While both types of machine learning are useful in an industrial setting, Deep Learning is particularly useful when it comes to exploring raw data and making inferences that will reveal hidden structures within those data, and this is highly relevant when it comes to machine operations on the tissue and nonwovens production line.
How deep learning is used to identify machine defects
On the production line, machines operates around the clock and under extreme pressure, and obviously they experience wear over time that can easily result in unexpected breakdowns and idle times that negatively impact production.
Read more: 4 Tools for Tissue and Nonwovens Machines to Reduce and Optimize Idle Times
This loss of productivity can, in turn, seriously impact profits. However, until now, workers on the production line had no way of monitoring machinery at a detailed level during operations. Getting close to machinery when it is working poses a safety risk and many critical machine parts are not within the line of sight of workers.
Thanks to Industry 4.0, smart machines outfitted with sensors that transmit real-time operational data over the cloud are becoming increasingly common. Hundreds, even thousands of data points flow in every minute and these data are received by a machine-and-software-independent platform, where the data will be integrated and analyzed thanks to machine learning.
It is important to understand that the data that flows in are chaotic and raw. Deep Learning can take these data and separate them into layers that forms the structure of the neural network, such as the input layer, hidden layer, and output layer. Deep Learning neural networks can analyze the data over time to map the degradation of machine parts and machine function and determine when any machine part is coming close to the end of its lifecycle.
Subsequently, predictive maintenance can be scheduled such that a particular machine can be taken offline at the most advantageous time. This will ensure that operations will continue to run smoothly and at optimal levels without abrupt interruptions.
Deep learning for better tissue and nonwovens machine productivity
Minimizing machine downtime is one of the most important factors to ensure that tissue and nonwovens production lines remain at optimal levels of operation. Deep learning is an AI tool that is leading the way in the detection of machine defects that are not visible to workers on the production line. These defects can be caught in time to schedule predictive maintenance in order to avoid unplanned machine idle times and loss of profit.
For more information about how AI, deep learning, and automation can improve productivity on your tissue and nowovens production line, download our eBook “5 Industry 4.0 Tools that Boost Productivity in the Tissue Paper and Nonwovens Industry”!