A Review of Modeling and Diagnostic Techniques for Eccentricity Fault in Electric Machines

Liu, Zijian; Zhang, Pinjia; He, Shan; Huang, Jin

doi:10.3390/en14144296

Cited by 18 publications

(6 citation statements)

References 127 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Measures like adding shims or modifying the bearing seat can be taken to reduce ε SE if γ of the motor is known. Besides, as the rotor is gradually dragged to a more eccentric position because the direction of UMP is roughly consistent with γ [5], strengthening the structures of bearing seats or end-caps alongside γ may delay this bad trend. Due to manufacturing tolerance, bearing wear, structure deformation, etc., all motors exhibit a certain level of eccentricity, in which ME is the most common issue.…”

Section: Statormentioning

confidence: 99%

Open-Circuit Radial Stray Magnetic Flux Density Based Noninvasive Diagnosis for Mixed Eccentricity Parameters of Interior Permanent Magnet Synchronous Motors in Electric Vehicles

Zhang

et al. 2023

IEEE Trans. Ind. Electron.

View full text Add to dashboard Cite

By measuring the open-circuit radial stray magnetic flux density (RSMFD) of three spatial points, a novel diagnostic method for the eccentricity of interior permanent magnetic motors (IPMSMs) in electric vehicles (EVs) is proposed. Outperforming traditional methods, the proposed method can non-invasively diagnose not only the severities of static eccentricity (SE), dynamic eccentricity (DE), and mixed eccentricity (ME), but also the eccentricity location. In this paper, diagnosis results are described through three eccentricity parameters: SE ratio, DE ratio, and SE angle. Firstly, an analytical model of the open-circuit RSMFD of the IPMSM is proposed, with its accuracy validated by experiments. The pattern of the impact of the eccentricity parameters on the open-circuit RSMFD is analyzed to form the basis of the proposed method. Then, an eccentricity prototype motor, which can continuously emulate the eccentricity according to any listed eccentricity parameters, is developed. A test rig is built to collect the open-circuit RSMFD and validate the analytical calculation results. Finally, an eccentricity diagnostic model based on the open-circuit RSMFD is established. The diagnosis results proved the non-invasiveness, effectiveness, and viability of the proposed method.

show abstract

Section: Statormentioning

confidence: 99%

Open-Circuit Radial Stray Magnetic Flux Density Based Noninvasive Diagnosis for Mixed Eccentricity Parameters of Interior Permanent Magnet Synchronous Motors in Electric Vehicles

Zhang

et al. 2023

IEEE Trans. Ind. Electron.

View full text Add to dashboard Cite

show abstract

“…Figure 13 shows the state of change of the center of rotation in operation and Table 8 shows the induction motor diagnosis methods related to eccentricity fault. [192,193].…”

Section: Mixed Eccentricity (Me)mentioning

confidence: 99%

Inverter-Fed Motor Drive System: A Systematic Analysis of Condition Monitoring and Practical Diagnostic Techniques

et al. 2023

View full text Add to dashboard Cite

Due to their efficiency and control capabilities, induction motors fed with inverters have become prevalent in various industrial applications. However, ensuring the reliable operation of the motor and diagnosing faults on time are crucial for preventing unexpected failures and minimizing downtime. This paper systematically analyzes condition monitoring and practical diagnostic techniques for inverter-fed motor drive systems. This study encompasses a thorough evaluation of different methods used for condition monitoring and diagnostics of induction motors, with the most crucial faults in their stator, rotor, bearings, eccentricity, shaft currents, and partial discharges. It also includes an assessment of their applicability. The presented analysis includes a focus on the challenges associated with inverter-fed systems, such as high-frequency harmonics, common-mode voltages causing the bearing currents, and high voltage gradients (dv/dt) due to fast switching frequency, which can impact the motor operation, as well as its faults analysis. Furthermore, this research explores the usefulness and efficiency of various available diagnostic methods, such as motor current signature analysis and other useful analyses using advanced signal processing techniques. This study aims to present findings that provide valuable insights for developing comprehensive condition monitoring strategies, and practical diagnostic techniques that enable proactive maintenance, enhanced system performance, and improved operational reliability of inverter-fed motor drive systems.

show abstract

“…RR is a linear least squares method with an additional regularization parameter. The selection of the varied parameters presented in Table 8 was carried out according to previous [68][69][70] articles on related issues. The parametersalpha and max_iter are explained in the previous subsections, tol indicates the precision of the solution, while the novelty is the solver where the solver is the principle of model optimization.…”

Section: Ridge Regressormentioning

confidence: 99%

Estimation of Excitation Current of a Synchronous Machine Using Machine Learning Methods

et al. 2022

View full text Add to dashboard Cite

A synchronous machine is an electro-mechanical converter consisting of a stator and a rotor. The stator is the stationary part of a synchronous machine that is made of phase-shifted armature windings in which voltage is generated and the rotor is the rotating part made using permanent magnets or electromagnets. The excitation current is a significant parameter of the synchronous machine, and it is of immense importance to continuously monitor possible value changes to ensure the smooth and high-quality operation of the synchronous machine itself. The purpose of this paper is to estimate the excitation current on a publicly available dataset, using the following input parameters: Iy: load current; PF: power factor; e: power factor error; and df: changing of excitation current of synchronous machine, using artificial intelligence algorithms. The algorithms used in this research were: k-nearest neighbors, linear, random forest, ridge, stochastic gradient descent, support vector regressor, multi-layer perceptron, and extreme gradient boost regressor, where the worst result was elasticnet, with R2 = −0.0001, MSE = 0.0297, and MAPE = 0.1442; the best results were provided by extreme boosting regressor, with R2¯ = 0.9963, MSE¯ = 0.0001, and MAPE¯ = 0.0057, respectively.

show abstract

A Review of Modeling and Diagnostic Techniques for Eccentricity Fault in Electric Machines

Cited by 18 publications

References 127 publications

Open-Circuit Radial Stray Magnetic Flux Density Based Noninvasive Diagnosis for Mixed Eccentricity Parameters of Interior Permanent Magnet Synchronous Motors in Electric Vehicles

Open-Circuit Radial Stray Magnetic Flux Density Based Noninvasive Diagnosis for Mixed Eccentricity Parameters of Interior Permanent Magnet Synchronous Motors in Electric Vehicles

Inverter-Fed Motor Drive System: A Systematic Analysis of Condition Monitoring and Practical Diagnostic Techniques

Estimation of Excitation Current of a Synchronous Machine Using Machine Learning Methods

Contact Info

Product

Resources

About