Dynamic Performance Improvement of Five-Phase Permanent-Magnet Motor With Short-Circuit Fault

Zhou, Huawei; Zhao, Wenxiang; Yu, Xiaodong; Gao, Menghu

doi:10.1109/tie.2017.2714144

Cited by 42 publications

(27 citation statements)

References 25 publications

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“…Dimensionality reduction of the training set sample data by the PCA method not only has the advantages of reducing the dimension of the training set and improving the speed of the model training, but also has the functions of eliminating the outliers of the signal and denoising the signal. Based on the dimensionality reduction principle and analysis steps of the PCA method described above, the variance contribution rates δ i of the principal components of the PS1 and PS2 training set samples are calculated, according to Equation (16). The variance contribution rates δ i of the first 1-18 principal elements are shown in Table 3 According to Equation (17), the relationship between the cumulative contribution rate η k of the principal components of the PS1 and PS2 training set samples and the number of principal components is further plotted, as shown in Figure 4.…”

Section: Dimensionality Reduction Of a Pca-based Training Set Samplementioning

confidence: 99%

“…In particular, the hydraulic valve is the core control component of the hydraulic system, and it is widely used in numerous engineering applications to control the flow and pressure of fluids [4][5][6]. In the hydraulic system, a vibration analysis (VA) is the most popular and efficient condition monitoring technique for rotating systems including the hydraulic pump, electric motor, bearing, and more [7][8][9][10][11][12][13][14][15][16]. However, the working process of the valve core of the hydraulic valve is a reciprocating motion.…”

Section: Introductionmentioning

confidence: 99%

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Fault Diagnosis Method for Hydraulic Directional Valves Integrating PCA and XGBoost

Lei

Jiang

et al. 2019

Processes

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A novel fault diagnosis method is proposed, depending on a cloud service, for the typical faults in the hydraulic directional valve. The method, based on the Machine Learning Service (MLS) HUAWEI CLOUD, achieves accurate diagnosis of hydraulic valve faults by combining both the advantages of Principal Component Analysis (PCA) in dimensionality reduction and the eXtreme Gradient Boosting (XGBoost) algorithm. First, to obtain the principal component feature set of the pressure signal, PCA was utilized to reduce the dimension of the measured inlet and outlet pressure signals of the hydraulic directional valve. Second, a machine learning sample was constructed by replacing the original fault set with the principal component feature set. Third, the MLS was employed to create an XGBoost model to diagnose valve faults. Lastly, based on model evaluation indicators such as precision, the recall rate, and the F1 score, a test set was used to compare the XGBoost model with the Classification And Regression Trees (CART) model and the Random Forests (RFs) model, respectively. The research results indicate that the proposed method can effectively identify valve faults in the hydraulic directional valve and have higher fault diagnosis accuracy.

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Section: Dimensionality Reduction Of a Pca-based Training Set Samplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method for Hydraulic Directional Valves Integrating PCA and XGBoost

Lei

Jiang

et al. 2019

Processes

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“…In [9], [10], B. Tian et al constructs a model based on the concept similar to the studies in [7], [8] for 5-phase PMSM machines using sliding mode control to eliminate the speed pulsation. In [11], [12] [14] proposes a new model for non-sinusoidal back-EMF machines. The torque ripples caused by the third harmonic air-gap flux is analyzed and eliminated by imposing third harmonic currents in the rotating frame.…”

Section: Overview Of the Recent Methodsmentioning

confidence: 99%

“…In [11], [12], the new transformation matrix is proposed for a sinusoidal back-EMF five-phase PMSM according to (5) without an additional coefficient x as in equation (12). ] are the back EMF and current vectors in natural frame respectively.…”

Section: (3) Methodsmentioning

confidence: 99%

An Overview of Methods using Reduced-Ordered Transformation Matrices for Fault-Tolerant Control of 5-phase Machines with an Open Phase

Nguyen

Semail

2019

2019 IEEE International Conference on Industrial Technology (ICIT)

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This paper studies control strategies using modified transformation matrices when five-phase machines operate in oneopen-phase faults. The basic idea of these methods is to maintain the rotating field under asymmetrical conditions as the same as in healthy condition by determining new transformation matrices. The dimension of the new matrices is equal to the number of remaining healthy phases in post-fault conditions. There have been different ways to determine the new transformation matrices applied for different types of five-phase machines in recent decades. In this study, an overview and analyses on these methods will be presented. In addition, advantages and drawbacks of these control strategies are clarified by numerical results.

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“…The hydraulic pump is an important power component, and it has been applied in the fields of robotics [17], engineering machinery [18], underwater machinery [19,20], and wind power machinery [21]. These fields often involve working conditions with high temperature, high pressure, high speed, high humidity, and heavy load, leading to a high failure rate and significant casualties and economic losses [22][23][24][25][26][27][28][29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

A Fault Feature Extraction Method for the Fluid Pressure Signal of Hydraulic Pumps Based on Autogram

Zheng

Zhu

2019

Processes

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Center spring wear faults in hydraulic pumps can cause fluid pressure fluctuations at the outlet, and the fault feature information on fluctuations is often contaminated by different types of fluid flow interferences. Aiming to resolve the above problems, a fluid pressure signal method for hydraulic pumps based on Autogram was applied to extract the fault feature information. Firstly, maximal overlap discrete wavelet packet transform (MODWPT) was adopted to decompose the contaminated fault pressure signal of center spring wear. Secondly, based on the squared envelope of each node, three kinds of kurtosis of unbiased autocorrelation (AC) were computed in order to describe the fault feature information comprehensively. These are known as standard Autogram, upper Autogram and lower Autogram. Then a node corresponding to the biggest kurtosis value was selected as a data source for further spectrum analysis. Lastly, the data source was processed by threshold values, and then the fault could be diagnosed based on the fluid pressure signal.

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Dynamic Performance Improvement of Five-Phase Permanent-Magnet Motor With Short-Circuit Fault

Cited by 42 publications

References 25 publications

Fault Diagnosis Method for Hydraulic Directional Valves Integrating PCA and XGBoost

Fault Diagnosis Method for Hydraulic Directional Valves Integrating PCA and XGBoost

An Overview of Methods using Reduced-Ordered Transformation Matrices for Fault-Tolerant Control of 5-phase Machines with an Open Phase

A Fault Feature Extraction Method for the Fluid Pressure Signal of Hydraulic Pumps Based on Autogram

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