A Combined Model and Data-Driven Approach for the Determination of Rotor Temperature in an Induction Machine

Mocanu, Razvan; Onea, Alexandru; Dosoftei, Constantin-Cätälin

doi:10.3390/s21134512

Cited by 2 publications

(1 citation statement)

References 30 publications

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“…Most of the studies focus on fault diagnosis of induction motors. For instance, Razvan [ 12 ] applied the Nonlinear Autoregressive Network with Exogenous (NARX) method whereas Hai Guo [ 13 ] proposed a deep neural network for temperature prediction of the motor. However, deficiency in historical faulty data became the drawback of developing an efficient data-driven model.…”

Section: Introductionmentioning

confidence: 99%

Modeling of high voltage induction motor cooling system using linear regression mathematical models

et al. 2022

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Achieving reliable power efficiency from a high voltage induction motor (HVIM) is a great challenge, as the rigorous control strategy is susceptible to unexpected failure. External cooling is commonly used in an HVIM cooling system, and it is a vital part of the motor that is responsible for keeping the motor at the proper operating temperature. A malfunctioning cooling system component can cause motor overheating, which can destroy the motor and cause the entire plant to shut down. As a result, creating a dynamic model of the motor cooling system for quality performance, failure diagnosis, and prediction is critical. However, the external motor cooling system design in HVIM is limited and separately done in the past. With this issue in mind, this paper proposes a combined modeling approach to the HVIM cooling system which consists of integrating the electrical, thermal, and cooler model using the mathematical model for thermal performance improvement. Firstly, the development of an electrical model using an established mathematical model. Subsequently, the development of a thermal model using combined mathematical and linear regression models to produce motor temperature. Then, a modified cooler model is developed to provide cold air temperature for cooling monitoring. All validated models are integrated into a single model called the HVIM cooling system as the actual setup of the HVIM. Ultimately, the core of this modeling approach is integrating all models to accurately represent the actual signals of the motor cooler temperature. Then, the actual signals are used to validate the whole structure of the model using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) analysis. The results demonstrate the high accuracy of the HVIM cooling system representation with less than 1% error tolerance based on the industrial plant experts. Thus, it will be helpful for future utilization in quality maintenance, fault identification and prediction study.

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Section: Introductionmentioning

confidence: 99%

Modeling of high voltage induction motor cooling system using linear regression mathematical models

et al. 2022

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Bayesian Architecture for Predictive Monitoring of Unbalance Faults in a Turbine Rotor–Bearing System

Bera,

Huang,

Lin

et al. 2024

Sensors

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Unbalance faults are among the common causes of interruptions and unexpected failures in rotary systems. Therefore, monitoring unbalance faults is essential for predictive maintenance. While conventional time-invariant mathematical models can assess the impact of these faults, they often rely on proper assumptions of system factors like bearing stiffness and damping characteristics. In reality, continuous high-speed operation and environmental factors like load variations cause these parameters to change. This work presents a novel architecture for unbalance fault monitoring and prognosis, in which the bearing parameters are treated as variables that change with operating conditions. This enables the development of a more reliable mathematical model for continuous monitoring and prognosis of unbalance faults in rotor systems. This Bayesian inference framework uses Markov Chain Monte Carlo (MCMC) sampling to identify dynamic bearing parameters. Specifically, the Metropolis algorithm is employed to systematically evaluate the range of acceptable parameter values within the framework. A novel dual-MCMC loops explore and assess the parameter space, resulting in more accurate and reliable bearing parameter estimations. These updated parameters improve the demonstrated turbine rotor–bearing system’s unbalance assessment up to 74.48% of the residual error compared to models with fixed parameters. This validates the Bayesian framework for predictive monitoring and maintenance-oriented solutions.

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A Combined Model and Data-Driven Approach for the Determination of Rotor Temperature in an Induction Machine

Cited by 2 publications

References 30 publications

Modeling of high voltage induction motor cooling system using linear regression mathematical models

Modeling of high voltage induction motor cooling system using linear regression mathematical models

Bayesian Architecture for Predictive Monitoring of Unbalance Faults in a Turbine Rotor–Bearing System

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