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What are some effective ways to verify data preprocessing techniques for control systems?

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Data preprocessing is a crucial step in control engineering, as it can affect the performance, accuracy, and robustness of the control systems. Data preprocessing involves transforming, cleaning, and normalizing the data collected from sensors, actuators, and other sources, to make it suitable for control design, analysis, and implementation. However, how can you verify that your data preprocessing techniques are effective and appropriate for your control problem? In this article, we will discuss some methods and criteria that can help you evaluate your data preprocessing techniques and ensure data quality for control systems.

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1 Data quality dimensions

One way to verify your data preprocessing techniques is to check whether they improve the data quality along four dimensions: accuracy, completeness, timeliness, and consistency. Accuracy refers to how close the data values are to the true or desired values, and how well they reflect the physical phenomena or system behavior. Completeness refers to how much of the data is available and usable, and how well it covers the relevant aspects of the system or process. Timeliness refers to how current and fresh the data is, and how well it matches the temporal requirements of the control system. Consistency refers to how coherent and compatible the data is across different sources, formats, and units, and how well it follows the standards and rules of the control system.

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2 Data preprocessing methods

Verifying data preprocessing techniques can be done by comparing and contrasting different methods that can achieve the same or similar goals, and then selecting the one that best suits your control problem. Scaling, for instance, adjusts the range or magnitude of the data values to a common scale to avoid numerical issues or improve performance. This can be done through min-max normalization, standardization, or other methods. Filtering is another technique which removes noise, outliers, or irrelevant data from the data set in order to enhance the signal-to-noise ratio or reduce complexity. Low-pass, high-pass, band-pass, and other filters are some of the available options. Feature extraction is used to derive new or reduced features from the original data in order to capture essential or relevant information or patterns for the control system. Principal component analysis (PCA) and linear discriminant analysis (LDA) are two popular methods for this purpose. Finally, feature selection selects a subset of features from the original data to eliminate redundant or irrelevant features and reduce dimensionality. Filter, wrapper, and embedded methods are some of the available options for this technique.

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Chaitanya Bhagyawan

Data quality and governance at Cummins - College of engineering pune
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Filters works good if the data sets are of single variable. The problem is generally faces by people when we have multivariable problem. When used filter we miss entire row where in some of the important data is vanished. I belive using different techniques for dealing with blank, NA, outliers, and garbage data is to use mean, median, mode or other techniques even before discarding the entire row. The box plot and normal curve gives you better understanding but there are some process which are not normal and we should respect the process as is. The pre processing of data should be bound to the assumptions and those should be documented because we may miss out on some critical data and want to revisit the original work.

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3 Data preprocessing evaluation

Verifying the data preprocessing techniques can be done by evaluating their impact and effectiveness on the control system performance and quality. This can be done by using various metrics and indicators, such as control error, control stability, control robustness, and control efficiency. Control error can be measured by mean absolute error (MAE) or root mean square error (RMSE), while control stability can be measured by eigenvalues or poles. Control robustness can be measured by gain margin or phase margin, and control efficiency can be measured by performance index or cost function. By doing this, you will gain insight into how well the preprocessed data meets the control objectives and specifications.

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4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are some effective ways to verify data preprocessing techniques for control systems?

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1 Data quality dimensions

2 Data preprocessing methods

3 Data preprocessing evaluation

4 Here’s what else to consider

Control Engineering

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