Fuzzy Intelligent System for Detection of Multiple Switch Open Fault in Induction Motor Drive System

Kumar, Rakesh; Mini, V. P.; Mayadevi, N.

doi:10.1109/catcon47128.2019.cn0003

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…The fuzzy control-based driving behavior modeling can directly reflect the selflearning and fuzzy reasoning abilities in the driving process, showing the fuzziness and self-learning characteristics of vehicle control. The authors in [13] constructed a driving model with fuzzy characteristics that has better tracking performance for target paths through experimental verification. This type of model is characterized by a large amount of computation and requires a large number of samples for training.…”

Section: Related Workmentioning

confidence: 99%

Driving Violation Prediction Based on an Emotional Style Transfer Network

Wang

2023

Sustainability

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Emotions are closely related to driving behavior, and drivers with different emotions have different degrees of bad driving behavior. In order to explore the relationship between emotions and driving violations, a prediction model based on an emotional style transfer network is proposed. First, inspired by the idea of generative adversarial networks (GAN), the eigenvalues of emotions are extracted. Secondly, the one-way propagation method of the GAN network is improved to cyclic generation, which avoids the problems of non-convergence and long periods in the data training process, improving the utilization of training data. Thirdly, a driving violation prediction model is designed. In this model, the emotion factors are designed as time-related sequences, and by improving the Long Short-Term Memory (LSTM) model, the encoding and decoding processes of the time-related sequences are added to form the context, which improves the accuracy of prediction. Finally, the experimental and simulation data show that the proposed model has significant advantages in loss value, accuracy rate, macro-average score, and other indicators. At the same time, an emotion-induction scheme is given to reduce the possibility of driving violations. Furthermore, the proposed model can provide a theoretical basis for the impact of emotions on driving safety.

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Section: Related Workmentioning

confidence: 99%

Driving Violation Prediction Based on an Emotional Style Transfer Network

Wang

2023

Sustainability

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“…Some are happened at the motor side e.g., mechanical such as bearing faults and eccentricity faults and others are electrical faults. The electrical failure may hit the motor sides or it's driving system [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neuro Fuzzy Interface System Based Short Circuit Fault Diagnoses in a Three Phase Induction Motor Drive System

Mahafzah¹,

Fraihat²

2021

ujeee

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Induction motors are commonly used in different industrial, housing and medical applications, such as air conditioners, transportation, elevators and others. These motors are rugged, reliable and economical. Generally, the reliable operation of the motor and its drive system is very important in industry so that any expected fault must be detected immediately by a monitoring system. This paper reviews and simulates a three-phase induction motor model and different types of faults that may happen in the drive system. Due to short circuit severity on the overall drive system, this study focuses only on short circuit faults, e.g., when any Insulated Gate Bipolar Transistor (IGBT) is shorted out and DC link capacitor is shorted out. Also, an Adaptive Neuro Fuzzy Interface System (ANFIS) algorithm is programmed to detect different types of faults and classify them. The ANFIS system takes two parameters as input from the motor: the first one is the load torque whereas the second one is the stator current. Whereas, the previously published works analyze only the motor's stator current. MATLAB is used to train the ANFIS system and also to analyze samples. Additionally, LTSpice is used simulate the drive system. The ANFIS system shows very high accuracy in short circuit fault detection and classification.

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Fuzzy Intelligent System for Detection of Multiple Switch Open Fault in Induction Motor Drive System

Cited by 2 publications

References 13 publications

Driving Violation Prediction Based on an Emotional Style Transfer Network

Driving Violation Prediction Based on an Emotional Style Transfer Network

Adaptive Neuro Fuzzy Interface System Based Short Circuit Fault Diagnoses in a Three Phase Induction Motor Drive System

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