NEWS & INSIGHTS
A power spectra tells how much of the power energy is contained in the frequency components of the signal.
Correlations are useful because they can indicate a predictive relationship that can be exploited in practice, or they can be used for explanation or better understanding of the process
Effective visualization helps us to analyze, reason, and make actionable decisions about data and evidence.
Design of Experiments
In industrial experimentation, the Design of Experiments (DOE) is usually the best and fastest way to learn cause-effect correlations between independent (factors, inputs) and dependent (responses, outputs) process variables. It allows for multiple factors to be systematically and simultaneously changed, determining their effect on a desired response. DOE is faster and better than the one-factor-at-a-time method (OFAT) of changing values, and also finds interactions that OFAT misses. DOE can be used to reduce product design and manufacturing costs by finding the optimal combination of values for the input factors.
In manufacturing troubleshooting, it is important to find causal correlations between process and product variables. E.g. variation line speed can cause excessive variation in product quality. Temperature fluctuation in a drying oven can cause product defects. A malfunctioning control system may result in abnormal process behavior. A failing equipment may even cause the process shutdown. To be able quickly determine and eliminate the source of variation in critical process and product variables can greatly increase process uptime and yield. Information about causal correlations is also needed when determining optimal process windows (best combinations of setup values) to develop and manufacture products. In order to control processes, we need to know the effect of changes in control handles on important variables.
New Service provided by Process Data Insights LLC- Low Cost Portable Sensing and Data Collection Toolkit Enables Advanced Analytics
One of the challenges of applying advanced analytics and machine learning to manufacturing processes is quickly obtaining the right data. In many cases maximum knowledge has already been wrung out of existing sensors and process data, and so new measurement capabilities are required to make substantial improvements. For large manufacturers adding new measurement and data collection infrastructure to an existing line takes time and is expensive, and there is no guarantee that the investment will be worthwhile.
Using machine learning it is possible to develop accurate predictive models of industrial processes. In this example we will show how these methods can be used to develop a predictive model for energy output of a combined power plant. We will show how to use the model for what-if analysis and visualization of the process performance and discuss some key considerations for online prediction. The goal of this article is to give a basic introduction to building a predictive model for an industrial process and give some basic understanding of the Machine Learning modeling process and related technology.
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