Yubin Zhao

Bio: Yubin Zhao is an academic researcher from China University of Petroleum. The author has contributed to research in topics: Physics & Fault (power engineering). The author has an hindex of 1, co-authored 1 publications receiving 206 citations.

A Data-Driven Fault Diagnosis Methodology in Three-Phase Inverters for PMSM Drive Systems

Abstract: Permanent magnet synchronous motor and power electronics-based three-phase inverter are the major components in the modern industrial electric drive system, such as electrical actuators in an all-electric subsea Christmas tree. Inverters are the weakest components in the drive system, and power switches are the most vulnerable components in inverters. Fault detection and diagnosis of inverters are extremely necessary for improving drive system reliability. Motivated by solving the uncertainty problem in fault diagnosis of inverters, which is caused by various reasons, such as bias and noise of sensors, this paper proposes a Bayesian network-based data-driven fault diagnosis methodology of three-phase inverters. Two output line-to-line voltages for different fault modes are measured, the signal features are extracted using fast Fourier transform, the dimensions of samples are reduced using principal component analysis, and the faults are detected and diagnosed using Bayesian networks. Simulated and experimental data are used to train the fault diagnosis model, as well as validate the proposed fault diagnosis methodology.

Fault diagnosis of driving gear in rack and pinion drives based on multi-scale local binary pattern extraction and sparse representation

Abstract: Rack and pinion drives (RPDs) are key components of battery-swapping systems (BSSs) used in electric heavy trucks; the faults occurring in these drives reduce the efficiency, accuracy, quality of battery swapping, and even pose potential safety risks. The operating conditions of RPD driving gear in BSSs are characterized by speed fluctuations, relatively low speeds, and reciprocating motion. To assess the driving gear fault characteristics under these conditions, based on the solution of image recognition under complex and low illumination conditions, this study proposes a fault diagnosis framework that includes adaptive down-sampling, three-dimensional acceleration data fusion, multi-scale local binary pattern (MS-LBP) extraction, and sparse representation. First, adaptive down-sampling is used to smooth out the speed fluctuation. Subsequently, MS-LBP extraction is employed to obtain efficient fault features at low speed. Finally, dictionary learning and sparse representations are conducted on the MS-LBP features. The effectiveness and advantages of the proposed diagnosis approach are demonstrated using monitoring data acquired from a BSS. Moreover, comparative studies demonstrate that the proposed fault diagnosis method yields improved performance.

Bayesian Networks in Fault Diagnosis

Abstract: Fault diagnosis is useful in helping technicians detect, isolate, and identify faults, and troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals with various uncertainty problems. This model is increasingly utilized in fault diagnosis. This paper presents bibliographical review on use of BNs in fault diagnosis in the last decades with focus on engineering systems. This work also presents general procedure of fault diagnosis modeling with BNs; processes include BN structure modeling, BN parameter modeling, BN inference, fault identification, validation, and verification. The paper provides series of classification schemes for BNs for fault diagnosis, BNs combined with other techniques, and domain of fault diagnosis with BN. This study finally explores current gaps and challenges and several directions for future research.

An Overview of Artificial Intelligence Applications for Power Electronics

Abstract: This article gives an overview of the artificial intelligence (AI) applications for power electronic systems. The three distinctive life-cycle phases, design, control, and maintenance are correlated with one or more tasks to be addressed by AI, including optimization, classification, regression, and data structure exploration. The applications of four categories of AI are discussed, which are expert system, fuzzy logic, metaheuristic method, and machine learning. More than 500 publications have been reviewed to identify the common understandings, practical implementation challenges, and research opportunities in the application of AI for power electronics. This article is accompanied by an Excel file listing the relevant publications for statistical analytics.

Bearing Fault Diagnosis Method Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning.

Abstract: Recently, research on data-driven bearing fault diagnosis methods has attracted increasing attention due to the availability of massive condition monitoring data. However, most existing methods still have difficulties in learning representative features from the raw data. In addition, they assume that the feature distribution of training data in source domain is the same as that of testing data in target domain, which is invalid in many real-world bearing fault diagnosis problems. Since deep learning has the automatic feature extraction ability and ensemble learning can improve the accuracy and generalization performance of classifiers, this paper proposes a novel bearing fault diagnosis method based on deep convolutional neural network (CNN) and random forest (RF) ensemble learning. Firstly, time domain vibration signals are converted into two dimensional (2D) gray-scale images containing abundant fault information by continuous wavelet transform (CWT). Secondly, a CNN model based on LeNet-5 is built to automatically extract multi-level features that are sensitive to the detection of faults from the images. Finally, the multi-level features containing both local and global information are utilized to diagnose bearing faults by the ensemble of multiple RF classifiers. In particular, low-level features containing local characteristics and accurate details in the hidden layers are combined to improve the diagnostic performance. The effectiveness of the proposed method is validated by two sets of bearing data collected from reliance electric motor and rolling mill, respectively. The experimental results indicate that the proposed method achieves high accuracy in bearing fault diagnosis under complex operational conditions and is superior to traditional methods and standard deep learning methods.

Comprehensive Diagnosis and Tolerance Strategies for Electrical Faults and Sensor Faults in Dual Three-Phase PMSM Drives

Abstract: In this paper, fault diagnosis and fault-tolerant control strategies have been studied comprehensively for dual three-phase permanent-magnet synchronous motor (PMSM) drives to improve the reliability. Based on direct torque control (DTC) with space vector modulation, a series of diagnostic and tolerant control methods have been proposed for five types of faults, namely, speed-sensor fault, dc-link voltage-sensor fault, current-sensor fault, open-phase fault, and open-switch fault. First, diagnosis and tolerant schemes are proposed for speed-sensor fault by estimating the rotor angle speed with the rotating speed of stator flux. Second, diagnosis and tolerant schemes are proposed for dc-link voltage-sensor fault by combining the current model based stator flux observer with the voltage model based stator flux observer. Third, a three-step method is designed to diagnose three types of faults related to current signals, namely, current-sensor fault, open-phase fault, and open-switch fault simultaneously. A vector space decomposition based current estimation method is proposed to achieve fault-tolerant control for the current-sensor fault, and the voltage compensation based fault-tolerant control is presented for both open-phase and open-switch faults. The experiments have been taken on a laboratory prototype to verify the effectiveness of the proposed fault diagnosis and fault-tolerant schemes.