Prefatory data analysis approach to synthetically generated pneumatic actuator data set

Abstract

This article illustrates the importance of exploratory data analysis as the key step in the data validation process and also for further model development. Descriptive statistics is performed by applying the exploratory data analysis approach to a data set that is derived from the simulink library of a benchmark industrial actuator process provided by the Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems research group. In this work, the data set is synthetically generated from an actuator model by simulating it under different fault conditions. The exploratory data analysis is performed as an initial investigation on the data set to reveal the anomalies and patterns for making suitable assumptions and test hypothesis to develop more accurate models. The raw data are visualised using different visualisation techniques to find patterns and behaviours. The data distribution, class distribution, feature correlation and presence of outliers are revealed through data visualisation. Data processing is performed to transform the data, and treat outliers and missing values in the data set. The treated data set after performing data processing is visually confirmed using appropriate visualisation techniques. The inferences from the visualisation methods are validated quantitatively with statistical results to support the exploratory data analysis. Data profiling is dealt with by collecting the metadata on the Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems data set and aids in enhancing the data set quality and content to procure accurate predictive models employed to foresee the actuator performance.

Integrated fault detection filter and fault-tolerant control for the unmanned surface vehicle with deception attacks

“…with e 1 and e 2 denoted as the given scalars, Y 1 = DiagfP À1 , P À1 , P À1 , I, P À1 , X, Ig, Y 2 =DiagfI, Ig, and O = DiagfY 1 , I, I, I, Y 2 , Y 2 , Y 2 g. Inequality(20) can be confirmed by pre-and post-multiplying(14) with O and O T , respectively. Finally, the matrices of the observer-based FDF and FTC in an integrated framework are obtained using the LMI technique.…”

“…This implies that the USV remains stable under other delay conditions, t(t) 2 ½0, t M and h(t) 2 ½0, h M . Theorem 2 uses mathematical methods and LMI techniques to solve indefinite matrix (14), enabling the computation of FDF gains L, V and the FTC gain K, thereby obtaining the values of matrices A, B, C in Theorem 1.…”

“…10 A dynamic event-triggered FDF, 11 an adaptive memory-based event-triggered FDF, 12 and an asynchronous event-triggered FDF 13 are designed with the higher FD performance and faster FD time. In addition, a variety of FD schemes, such as FD based on data-driven, 14 FD based on deep learning technique, 15 and FD based on neural networks, 16 are designed to detect physical faults. On the other hand, the development of robust and effective fault-tolerant control (FTC) mechanisms 17 is of the utmost importance to ensure the successful operation and navigation safety of the USV.…”

Application of Machine Learning Techniques to Discern Optimal Rearing Conditions for Improved Black Soldier Fly Farming

“…Table 2 shows how this research checked outliers against each input variable and how they were handled. The techniques employed were flooring and capping, which have been used by other researchers such as [21] to handle outlier entries. These techniques involve limiting the extreme variables to fall within the selected percentiles of the maximum and minimum variables [20].…”