Applying principal component analysis (PCA) to the selection of forensic analysis methodologies

Booker, Nigel K.; Knights, Peter; Gates, J.D.; Clegg, Richard

doi:10.1016/j.engfailanal.2021.105937

Cited by 8 publications

(8 citation statements)

References 2 publications

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“…The relationships between different extracts and antioxidant activity were analyzed by Principal Component Analysis (PCA) using Minitab 18 [ 52 – 54 ]. An analysis of the principal components reduces data dimensionality and identifies key variance components.…”

Section: Methodsmentioning

confidence: 99%

Performance optimization for an optimal operating condition for a shell and heat exchanger using a multi-objective genetic algorithm approach

Venkatesh,

Kiran,

Khan

et al. 2024

PLoS ONE

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In this study, shell and heat exchangers are optimized using an integrated optimization framework. In this research, A structured Design of Experiments (DOE) comprising 16 trials was first conducted to systematically determine the essential parameters, including mass flow rates (mh, mc), temperatures (T1, t1, T2, t2), and heat transfer coefficients (€, TR, U). By identifying the first four principal components, PCA was able to determine 87.7% of the variance, thereby reducing the dimensionality of the problem. Performance-related aspects of the system are the focus of this approach. Key outcomes (€, TR, U) were predicted by 99% R-squared using the RSM models. Multiple factors, such as the mass flow rate and inlet temperature, were considered during the design process. The maximizing efficiency, thermal resistance, and utility were achieved by considering these factors. By using genetic algorithms, Pareto front solutions that meet the requirements of decision-makers can be found. The combination of the shell and tube heat exchangers produced better results than expected. Engineering and designers can gain practical insight into the mass flow rate, temperature, and key responses (€, TR, U) if they quantify improvements in these factors. Despite the importance of this study, it has several potential limitations, including specific experimental conditions and the need to validate it in other situations as well. Future research could investigate other factors that influence system performance. A holistic optimization framework can improve the design and engineering of heat exchangers in the future. As a result of the study, a foundation for innovative advancements in the field has been laid with tangible improvements. The study exceeded expectations by optimizing shell and heat exchanger systems using an integrated approach, thereby contributing significantly to the advancement of the field.

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Section: Methodsmentioning

confidence: 99%

Performance optimization for an optimal operating condition for a shell and heat exchanger using a multi-objective genetic algorithm approach

Venkatesh,

Kiran,

Khan

et al. 2024

PLoS ONE

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“…Nonetheless, the researcher's expertise and experience are critical in determining the ideal technique for extracting and selecting the meaningful feature from the available dataset. Some of the feature extraction techniques reported in studies include statistical methods, principal component analysis (PCA), independent component analysis (ICA), linear discriminant analysis (LDA), and so on [11]- [14], provide feature selection techniques the ease of selecting the most important and useful features, especially in the case of fault classification and/or isolation. Feature selection techniques aid in minimizing complexity by reducing data size, selecting the most relevant and important features for the model, and, in most cases, reducing redundancy in the dataset; thus creating a platform where the model's storage, accuracy, and computation cost are positively affected.…”

Section: A Literature Review and Related Workmentioning

confidence: 99%

“…Where α i is the Larange multiplier that satisfy the constraint α ≥ 0, i = 1, ..., l. When the condition in (9) reaches its minimum or maximum points, the resultant should satisfy both ( 11) and (12).…”

Section: ) Linear Classification In Svmmentioning

confidence: 99%

An Intelligent Hybrid Feature Selection Approach for SCIM Inter-Turn Fault Classification at Minor Load Conditions Using Supervised Learning

Okwuosa

Hur

2023

IEEE Access

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In industries, squirrel cage induction motors are critical for supplying rotary motion in power tools. This research presents a robust but simple framework for an inter-turn fault classification at minor loading across diverse fault occurrence conditions, which is one of the most common defects in a squirrel cage induction motor. Early detection of this issue is critical to prevent the system from completely failing as a result of it evolving to a more severe stator winding fault. This study employs a hybrid feature selection strategy (a hybrid of a filterbased and a wrapper-based approach) using the Hilbert Transform signal processing technique and a statistical feature extraction approach, which is then fed to a support vector machine as the classifier. The suggested framework is tested and validated against other known classifier models. The results demonstrate that the model has a computationally low diagnostic performance process with exceptional accuracy. Furthermore, when compared to the other classifier models, the suggested model provided the best diagnostic outcome on the stator winding fault classification, demonstrating its dependability in fault diagnostic classification for squirrel cage induction motors.INDEX TERMS Diagnostics, squirrel cage induction motor, fault classification, turn-to-turn winding fault, machine learning, Hilbert transform, support vector machine.

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“…A principal component analysis (PCA) is an unsupervised learning method of feature extraction and dimensional reduction (moving p-dimensional data to a lower-dimensional m-dimensional linear subspace), retaining the original features of the data and selecting their key properties [42,[51][52][53]. It analyses a data table in which observations are described by several intercorrelated quantitative dependent variables and is widely used due to its ability to extract interpretable information by efficiently removing redundancies [54,55]. It is typically used to perform the dimensional reduction of large sets of time series observations [56], moving from representing possibly correlated variables to a new set of orthogonal, uncorrelated variables and preserving the highest percentage of information [40,46,55].…”

Section: Background 21 Principal Component Analysis (Pca)mentioning

confidence: 99%

“…It is typically used to perform the dimensional reduction of large sets of time series observations [56], moving from representing possibly correlated variables to a new set of orthogonal, uncorrelated variables and preserving the highest percentage of information [40,46,55]. In this way, it allows a rapid assessment of any relationships between variables [54]. In other words, it is a method of projecting large dimensional measurements towards a minimum dimensional space and preserving maximum variance [57] by compressing sensory data according to their spatial and temporal correlations [58].…”

Section: Background 21 Principal Component Analysis (Pca)mentioning

confidence: 99%

Predicting the Health Status of a Pulp Press Based on Deep Neural Networks and Hidden Markov Models

et al. 2023

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The maintenance paradigm has evolved over the last few years and companies that want to remain competitive in the market need to provide condition-based maintenance (CBM). The diagnosis and prognosis of the health status of equipment, predictive maintenance (PdM), are fundamental strategies to perform informed maintenance, increasing the company’s profit. This article aims to present a diagnosis and prognosis methodology using a hidden Markov model (HMM) classifier to recognise the equipment status in real time and a deep neural network (DNN), specifically a gated recurrent unit (GRU), to determine this same status in a future of one week. The data collected by the sensors go through several phases, starting by cleaning them. After that, temporal windows are created in order to generate statistical features of the time domain to better understand the equipment’s behaviour. These features go through a normalisation to produce inputs for a feature extraction process, via a principal component analysis (PCA). After the dimensional reduction and obtaining new features with more information, a clustering is performed by the K-means algorithm, in order to group similar data. These clusters enter the HMM classifier as observable states. After training using the Baum–Welch algorithm, the Viterbi algorithm is used to find the best path of hidden states that represent the diagnosis of the equipment, containing three states: state 1—“State of Good Operation”; state 2—“Warning State”; state 3—“Failure State”. Once the equipment diagnosis is complete, the GRU model is used to predict the future, both of the observable states as well as the hidden states coming out from the HMM. Thus, through this network, it is possible to directly obtain the health states 7 days ahead, without the necessity to run the whole methodology from scratch.

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Applying principal component analysis (PCA) to the selection of forensic analysis methodologies

Cited by 8 publications

References 2 publications

Performance optimization for an optimal operating condition for a shell and heat exchanger using a multi-objective genetic algorithm approach

Performance optimization for an optimal operating condition for a shell and heat exchanger using a multi-objective genetic algorithm approach

An Intelligent Hybrid Feature Selection Approach for SCIM Inter-Turn Fault Classification at Minor Load Conditions Using Supervised Learning

Predicting the Health Status of a Pulp Press Based on Deep Neural Networks and Hidden Markov Models

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