Faster Multiscale Dictionary Learning Method With Adaptive Parameter Estimation for Fault Diagnosis of Traction Motor Bearings

Abstract Voice conversion (VC) is a technique of exclusively converting speaker-specific information in the source speech while preserving the associated phonemic information. Non-negative matrix factorization (NMF)-based VC has been widely researched because of the natural-sounding voice it achieves when compared with conventional Gaussian mixture model-based VC. In conventional NMF-VC, models are trained using parallel data which results in the speech data requiring elaborate pre-processing to generate parallel data. NMF-VC also tends to be an extensive model as this method has several parallel exemplars for the dictionary matrix, leading to a high computational cost. In this study, an innovative parallel dictionary-learning method using non-negative Tucker decomposition (NTD) is proposed. The proposed method uses tensor decomposition and decomposes an input observation into a set of mode matrices and one core tensor. The proposed NTD-based dictionary-learning method estimates the dictionary matrix for NMF-VC without using parallel data. The experimental results show that the proposed method outperforms other methods in both parallel and non-parallel settings.

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Transfer Learning Method for Bearing Fault Diagnosis Based on Fully Convolutional Conditional Wasserstein Adversarial Networks

Measurement ◽

10.1016/j.measurement.2021.109553 ◽

2021 ◽

pp. 109553

Author(s):

Yong zhi Liu ◽

Ke ming Shi ◽

Zhi xuan Li ◽

Guo fu Ding ◽

Yi sheng Zou

Keyword(s):

Fault Diagnosis ◽

Transfer Learning ◽

Learning Method ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Adversarial Networks

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Modeling and Fault Diagnosis of Interturn Short Circuit for Five-Phase Permanent Magnet Synchronous Motor

Journal of Electrical and Computer Engineering ◽

10.1155/2015/168786 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Jian-wei Yang ◽

Man-feng Dou ◽

Zhi-yong Dai

Keyword(s):

Parameter Estimation ◽

Fault Diagnosis ◽

Permanent Magnet ◽

Fault Tolerant ◽

Short Circuit ◽

Estimation Method ◽

Trust Region ◽

Fault Tolerant Control ◽

Trust Region Algorithm ◽

Parameter Estimation Method

Taking advantage of the high reliability, multiphase permanent magnet synchronous motors (PMSMs), such as five-phase PMSM and six-phase PMSM, are widely used in fault-tolerant control applications. And one of the important fault-tolerant control problems is fault diagnosis. In most existing literatures, the fault diagnosis problem focuses on the three-phase PMSM. In this paper, compared to the most existing fault diagnosis approaches, a fault diagnosis method for Interturn short circuit (ITSC) fault of five-phase PMSM based on the trust region algorithm is presented. This paper has two contributions. (1) Analyzing the physical parameters of the motor, such as resistances and inductances, a novel mathematic model for ITSC fault of five-phase PMSM is established. (2) Introducing an object function related to the Interturn short circuit ratio, the fault parameters identification problem is reformulated as the extreme seeking problem. A trust region algorithm based parameter estimation method is proposed for tracking the actual Interturn short circuit ratio. The simulation and experimental results have validated the effectiveness of the proposed parameter estimation method.

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