Model-based failure prediction for electric machines using particle filter

Yu, Ming; Wang, Danwei; Ukil, Abhisek; Vaiyapuri, Viswanathan; Sivakumar, Nadarajan; Jayampathi, Chandana; Gupta, Amit Kumar; Nguyen, Vu Quoc Huy

doi:10.1109/icarcv.2014.7064591

Cited by 7 publications

(1 citation statement)

References 15 publications

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“…Yu et al [13] developed a model-based remaining useful life (RUL) prediction method for induction motor with stator winding short circuit fault. The induction motor model with stator winding short circuit fault is introduced based on reference frame transformation theory.…”

Section: G) Related Workmentioning

confidence: 99%

Failure Prediction of Induction Motors: A Case Study using CSLGH900/6-214, 5.8 MW, 11 kV/3ph/50 Hz Sag Mill Motor at Goldfields, Damang Mine

Amuzuvi¹,

Warden²

2020

GJRE

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This paper proposes a generalised feed-forward artificial neural network model that fulfils the failure prediction of a three phase 5.8MW, 11 kV Slip-Ring SAG Mill Induction Motor at Goldfields Ghana Limited, Damang Mine. It provides a general understanding of three phase induction motors,faults associated with induction motors and also emphasizes the use of intelligent systems, particularly artificial neural network, a modern failure prediction technology of induction motors. Site analysis and motor data (Current, Power and Winding Temperatures) collection were conducted at the Damang Mine. Simulation results are presented using MATLAB software (2017a) package to develop the fault prediction model. The proposed feed-forward neural network used the Levenberg-Marquardt and Bayesian Regularisation in training.

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Section: G) Related Workmentioning

confidence: 99%

Failure Prediction of Induction Motors: A Case Study using CSLGH900/6-214, 5.8 MW, 11 kV/3ph/50 Hz Sag Mill Motor at Goldfields, Damang Mine

Amuzuvi¹,

Warden²

2020

GJRE

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Particle-filtering-based failure prognosis via sigma-points: Application to Lithium-Ion battery State-of-Charge monitoring

Acuña

Orchard

2017

Mechanical Systems and Signal Processing

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Comparing Extended Kalman Filter and Particle Filter for estimating field and damper bar currents in Brushless Wound Field Synchronous Generator for stator winding fault detection and diagnosis

Nadarajan

Bhangu²,

Panda

et al. 2016

2016 IEEE Applied Power Electronics Conference and Exposition (APEC)

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Condition monitoring of the Brushless Wound Field Synchronous Generator (BWFSG) is important as it iswidely used in mission and safety critical applications such as marine vessels. The frequency signatures in rotor field current are well known indicators to detect and diagnose stator winding short circuits in synchronous generators, in addition the damper bars current could also be used for fault detection and diagnosis. However, BWFSG these currents are not accessible. Hence, it is important to use the mathematical model and state estimation techniques to estimate these parameters. This paper compares the performance of state estimation techniques such as the Extended Kalman Filter (EKF) and Particle Filter (PF) in estimating field current and damper bars current for stator winding fault detection and diagnosis. The experimental validation results confirmed that the performance of the EKF is better than that of the PF in terms of number of stator winding fault signatures extracted from estimating the field current and damper bars currents. Thus, the future work proposed to use the EKF for developing Model-Based Condition Monitoring (MBCM) system for the BWFSG. NOMENCLATUREInstantaneous voltage Instantaneous current Inductance Resistance  Angular velocity of the rotor in rad/s Subscripts Leakage quantity Magnetizing quantity q-axis quantity d-axis quantity 0 Zero sequence quantity Stator quantity Rotor quantity Damper bar q-axis leakage quantity Rotor field d-axis leakage quantity Damper bar d-axis leakage quantity Superscript ′ Rotor quantity referred to the stator

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Model-based failure prediction for electric machines using particle filter

Cited by 7 publications

References 15 publications

Failure Prediction of Induction Motors: A Case Study using CSLGH900/6-214, 5.8 MW, 11 kV/3ph/50 Hz Sag Mill Motor at Goldfields, Damang Mine

Failure Prediction of Induction Motors: A Case Study using CSLGH900/6-214, 5.8 MW, 11 kV/3ph/50 Hz Sag Mill Motor at Goldfields, Damang Mine

Particle-filtering-based failure prognosis via sigma-points: Application to Lithium-Ion battery State-of-Charge monitoring

Comparing Extended Kalman Filter and Particle Filter for estimating field and damper bar currents in Brushless Wound Field Synchronous Generator for stator winding fault detection and diagnosis

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