NEWS & INSIGHTS


Time-Frequency Data Analysis: Wigner Distribution Functions

In signal processing, time–frequency analysis comprises those techniques that study a signal in both the time and frequency domains simultaneously, using various time–frequency representations. The motivation for this analysis is that variables and their transform representation are often tightly connected, and they can be understood better by studying them jointly, as a two-dimensional object, rather than separately.

Time-Frequency Data Analysis: Hilbert Transforms and Manufacturing Data Scientists.

In signal processing, time–frequency analysis comprises those techniques that study a signal in both the time and frequency domains simultaneously, using various time–frequency representations. The motivation for this analysis is that variables and their transform representation are often tightly connected, and they can be understood better by studying them jointly, as a two-dimensional object, rather than separately.

Time-Frequency Data Analysis

In signal processing, time–frequency analysis comprises those techniques that study a signal in both the time and frequency domains simultaneously, using various time–frequency representations. The motivation for this analysis is that variables and their transform representation are often tightly connected, and they can be understood better by studying them jointly, as a two-dimensional object, rather than separately.

CORRELATIONS IN MANUFACTURING PROCESS DATA ANALYSIS – Part 5 of 5

In signal processing, the coherence is a statistic that can be used to examine the relation between two signals in frequency domain. It is based on the correlation between two signals. It is commonly used to estimate the power transfer between input and output of a linear system. If the signals meets certain statistical criteria, and the system function is linear, it can be used to estimate the causality between the input and output.

Coherence measures the normalized correlation between two power spectra…

CORRELATIONS IN MANUFACTURING PROCESS DATA ANALYSIS – Part 4

As mentioned in Part 1 of this blog series, correlation or dependence is any statistical relationship, whether causal or not, between two random variables. 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. However, in general, the presence of a correlation is not sufficient to infer the presence of a causal relationship.

Two random variables are uncorrelated if their covariance is zero…

CORRELATIONS IN MANUFACTURING PROCESS DATA ANALYSIS – Part 3

Data visualization is a graphic representation of data and information. It is a quick and effective way to detect visible correlations that could be otherwise hard to find in large complex manufacturing datasets. Our eyes are drawn to colors and patterns. We can quickly identify red from blue, square from circle. By using visual elements like charts, graphs, maps, colors, shapes, sizes, etc., data visualization tools provide a way to quickly see trends, outliers, clusters, correlations, patterns, and other non-random information in data. They give insight into the structure of the data. Effective visualization helps us to analyze, reason, and make actionable decisions about data and evidence.

CORRELATIONS IN MANUFACTURING PROCESS DATA ANALYSIS – Part 2

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.

CORRELATIONS IN MANUFACTURING PROCESS DATA ANALYSIS – Part 1

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.

Industrial Machine Learning using XGBoost

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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