Signal Processing in Manufacturing Applications

What is signal processing and how does it affect manufacturing? In manufacturing, being able to create, price and distribute products as efficiently as possible is critical to profitability. Data science has become a powerful tool to accomplish this, particularly when paired with machine learning. While these technologies are impressive, they’re also complex and full of risk for those who aren’t yet experts in applying them.  Machine learning models in manufacturing applications often rely on data provided by sensors, and sensors in production environments are imperfect devices. Because of their smaller size, they sacrifice signal or measurement quality compared to more expensive laboratory-grade equipment. 

So, how do you avoid being caught in a “garbage in, garbage out” situation? Using the signal processing techniques listed in this whitepaper, your engineering team will be able to identify sources of noise in your system, refine the data, and reveal the actual signal coming from your systems. You can feed this refined signal data into machine learning models applied to predictive maintenance, quality control, forecasting, process control and optimization, and more.

As more and more devices generate more data at increasing levels of sensitivity, noise becomes a major factor that can interfere with the data collected by your sensors. How do you determine the valuable information and ignore the distracting noise? Signal processing, as a discipline, solves this problem. Since industrial environments generate a lot of noise, signal processing has become an integral part of the Industry 4.0 landscape.

In this Very white paper, we’ll discuss common sources of noise in industrial environments that your engineering team is likely to encounter. We’ll also provide a few standard and advanced signal processing techniques to help you get started.