Fault Detection and Classification for Induction Motors Using Genetic Programming

Zhang, Yu; Hu, Ting; Liang, Xiaodong; Ali, Mohammad Zawad; Shabbir, Md. Nasmus Sakib Khan

doi:10.1007/978-3-030-16670-0_12

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…In this paper, the AI method, GP algorithm, is utilized, since this algorithm offers a possibility of creating mathematical expression from the given data which provides the best correlation between input and output data. Over the years, GP has been implemented in various fields such as curve fitting, data modeling and symbolic regression [20][21][22][23]; image and signal processing [24][25][26][27]; financial trading, time series prediction and economic modeling [28][29][30][31]; and industrial process control [32][33][34][35]. However, GP has also been implemented in medicine-based tasks.…”

Section: Introductionmentioning

confidence: 99%

Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm

Anđelić

Šegota

Lorencin

et al. 2021

IJERPH

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Estimation of the epidemiology curve for the COVID-19 pandemic can be a very computationally challenging task. Thus far, there have been some implementations of artificial intelligence (AI) methods applied to develop epidemiology curve for a specific country. However, most applied AI methods generated models that are almost impossible to translate into a mathematical equation. In this paper, the AI method called genetic programming (GP) algorithm is utilized to develop a symbolic expression (mathematical equation) which can be used for the estimation of the epidemiology curve for the entire U.S. with high accuracy. The GP algorithm is utilized on the publicly available dataset that contains the number of confirmed, deceased and recovered patients for each U.S. state to obtain the symbolic expression for the estimation of the number of the aforementioned patient groups. The dataset consists of the latitude and longitude of the central location for each state and the number of patients in each of the goal groups for each day in the period of 22 January 2020–3 December 2020. The obtained symbolic expressions for each state are summed up to obtain symbolic expressions for estimation of each of the patient groups (confirmed, deceased and recovered). These symbolic expressions are combined to obtain the symbolic expression for the estimation of the epidemiology curve for the entire U.S. The obtained symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for each state achieved R2 score in the ranges 0.9406–0.9992, 0.9404–0.9998 and 0.9797–0.99955, respectively. These equations are summed up to formulate symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for the entire U.S. with achieved R2 score of 0.9992, 0.9997 and 0.9996, respectively. Using these symbolic expressions, the equation for the estimation of the epidemiology curve for the entire U.S. is formulated which achieved R2 score of 0.9933. Investigation showed that GP algorithm can produce symbolic expressions for the estimation of the number of confirmed, recovered and deceased patients as well as the epidemiology curve not only for the states but for the entire U.S. with very high accuracy.

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Section: Introductionmentioning

confidence: 99%

Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm

Anđelić

Šegota

Lorencin

et al. 2021

IJERPH

View full text Add to dashboard Cite

show abstract

“…Inference-based intelligent methods include various techniques which can be used independently or combined to improve their efficiency. Some of these techniques use expert systems [9], fuzzy logic [10], artificial neural networks (ANNs) [11], Bayesian inference [12], genetic algorithms (GA) [13], and SVM [14], etc. Other combined tools use Fuzzy Logic ANN [15], Recurrent Neural Networks and Dynamic Bayesian networks [16], and Neuro-Genetic Algorithm [17].…”

Section: Introductionmentioning

confidence: 99%

Optimized multi layer perceptron artificial neural network based fault diagnosis of induction motor using vibration signals

Khoualdia¹,

Lakehal²,

Chelli³

et al. 2021

Diagnostyka

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Installations and the detection of their faults has become a major challenge. In order to develop a reliable approach for monitoring and diagnosis faults of these components, a test rig was mounted. In this article, a Multi Layer Perceptron (MLP) Artificial Neural Network (ANN) has been structured and optimized for online monitoring of induction motors. The input layer of our ANN used eight indicators calculated from the collected time signals and which represent the different states of the motor (Healthy, broken rotor bars, bearing fault and Misalignment) and the output layer used a codified matrix. However, based on L27 Taguchi design, the architecture for the hidden layers of our network is chosen, with the use of the Levenberg-Marquardt learning algorithm. Garson's algorithm and connection weight approach showed that there's a great sensitivity of the crest factor, the kurtosis and the variance on the effectiveness of our diagnostic system. Consequently, the obtained results are capable of detecting faults in the induction motor under different operating conditions.

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Classification of Autism Genes Using Network Science and Linear Genetic Programming

Chen

2020

Lecture Notes in Computer Science

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Fault Detection and Classification for Induction Motors Using Genetic Programming

Cited by 4 publications

References 25 publications

Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm

Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm

Optimized multi layer perceptron artificial neural network based fault diagnosis of induction motor using vibration signals

Classification of Autism Genes Using Network Science and Linear Genetic Programming

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