Feature engineering in fault diagnosis of induction motor

Panigrahy, Parth Sarathi; Santra, Deepjyoti; Chattopadhyay, Paramita

doi:10.1109/catcon.2017.8280234

Cited by 8 publications

(2 citation statements)

References 15 publications

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“…Therefore, this study uses feature engineering to improve the system. Feature engineering can be divided into three categories [18]: feature construction [19,20], feature extraction [21][22][23], and feature selection [24][25][26]. Feature construction can increase the number of features by creating the new features based on old features.…”

Section: Introductionmentioning

confidence: 99%

Application of ANN in Induction-Motor Fault-Detection System Established with MRA and CFFS

et al. 2022

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This paper proposes a fault-detection system for faulty induction motors (bearing faults, interturn shorts, and broken rotor bars) based on multiresolution analysis (MRA), correlation and fitness values-based feature selection (CFFS), and artificial neural network (ANN). First, this study compares two feature-extraction methods: the MRA and the Hilbert Huang transform (HHT) for induction-motor-current signature analysis. Furthermore, feature-selection methods are compared to reduce the number of features and maintain the best accuracy of the detection system to lower operating costs. Finally, the proposed detection system is tested with additive white Gaussian noise, and the signal-processing method and feature-selection method with good performance are selected to establish the best detection system. According to the results, features extracted from MRA can achieve better performance than HHT using CFFS and ANN. In the proposed detection system, CFFS significantly reduces the operation cost (95% of the number of features) and maintains 93% accuracy using ANN.

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Section: Introductionmentioning

confidence: 99%

Application of ANN in Induction-Motor Fault-Detection System Established with MRA and CFFS

et al. 2022

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“…Furthermore, several studies have been conducted that implemented feature engineering; however, none are related to stock prediction. Researchers in [23] used feature engineering fault diagnosis of induction motors. A semantic feature model in concurrent engineering was conducted by researchers in [24].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Stock Price Prediction Using Gradient Boosting Machine with Feature Engineering (GBM-wFE)

Nabi

Saeed

Harron

2020

KJAR

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The prediction of stock prices has become an exciting area for researchers as well as academicians due to its economic impact and potential business profits. This study proposes a novel multiclass classification ensemble learning approach for predicting stock prices based on historical data using feature engineering. The proposed approach comprises four main steps, which are pre-processing, feature selection, feature engineering, and ensemble methods. We use 11 datasets from Nasdaq and S&P 500 to ensure the accuracy of the proposed approach. Furthermore, eight feature selection algorithms are studied and implemented. More importantly, a feature engineering concept is applied to construct two new features, which are appears to be very auspicious in terms of improving classification accuracy, and this is considered the first study to use feature engineering for multiclass classification using ensemble methods. Finally, seven ensemble machine learning (ML) algorithms are used and compared to discover the ultimate collaboration prediction model. Besides, the best feature selection algorithm is proposed. This study proposes a novel multiclass classification approach called Gradient Boosting Machine with Feature Engineering (GBM-wFE) and Principal Component Analysis (PCA) as the feature selection. We find that GBM-wFE outperforms the previous studies and the overall prediction results are auspicious, as MAPE of 0.0406% is achieved, which is considered the best result compared to the available studies in the literature.

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Feature Enhancement-Based Stock Prediction Strategy to Forecast the Fiscal Market

Padhi

Padhy

Bhoi

2023

Lecture Notes in Networks and Systems

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Feature engineering in fault diagnosis of induction motor

Cited by 8 publications

References 15 publications

Application of ANN in Induction-Motor Fault-Detection System Established with MRA and CFFS

Application of ANN in Induction-Motor Fault-Detection System Established with MRA and CFFS

A Novel Approach for Stock Price Prediction Using Gradient Boosting Machine with Feature Engineering (GBM-wFE)

Feature Enhancement-Based Stock Prediction Strategy to Forecast the Fiscal Market

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