Method for extracting current envelope for broken rotor bar fault detection of induction motors at time‐varying loads

Wang, Panpan; Lu, Junjie; Shi, Liping; Zhang, Yang; Tong, Zhigang; Wang, Nanding

doi:10.1049/iet-epa.2019.0779

Cited by 10 publications

(3 citation statements)

References 36 publications

(71 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared with Figure 8(a), the BRB fault causes harmonic components at the frequencies of (1 ± 2s)f 1 in the currents and the frequency interval between the harmonic components and the fundamental component is very small. So the amplitude of the fundamental component is modulated and the currents have periodic fluctuations [38]. e PVR of the currents with two broken bars is shown in Figure 10(b).…”

Section: Pvr Of Motor With Brb Faultmentioning

confidence: 99%

Method for Identifying Stator and Rotor Faults of Induction Motors Based on Machine Vision

Lipeng

Xiang

Wang

et al. 2021

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

The detection results need to be analyzed and distinguished by professional technicians in the fault detection methods for induction motors based on signal processing and it is difficult to realize the automatic identification of stator and rotor faults. A method for identifying stator and rotor faults of induction motors based on machine vision is proposed to solve this problem. Firstly, Park’s vector approach (PVA) is used to analyze the three-phase currents of the motor to obtain Park’s vector ring (PVR). Then, the local binary patterns (LBP) and gray level cooccurrence matrix (GLCM) are combined to extract the image features of PVR. Finally, the vectors of image features are used as input and the types of induction motor faults are identified with the help of a random forest (RF) classifier. The proposed method has achieved high identification accuracy in both the Maxwell simulation experiment and the actual motor experiment, which are 100% and 95.83%, respectively.

show abstract

Section: Pvr Of Motor With Brb Faultmentioning

confidence: 99%

Method for Identifying Stator and Rotor Faults of Induction Motors Based on Machine Vision

Lipeng

Xiang

Wang

et al. 2021

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…In general, signal-based approaches are classified into the two categories of invasive and non-invasive methods [6]. Various tools are used for analyzing the signals such as, in the time domain correlation functions [7], in the frequency domain the Fast Fourier Transform (FFT) [8], and in the time-frequency domain the Short Time Fourier Transform (STFT) [9] and Wavelet Transform (WT) [10]. However, they all have limitations and drawbacks that require further investigation.…”

Section: Motivation and Literature Reviewmentioning

confidence: 99%

A robust principal component analysis-based approach for detection of a stator inter-turn fault in induction motors

Namdar

2022

Prot Control Mod Power Syst

View full text Add to dashboard Cite

Health condition monitoring of induction motors is important because of their vital role and wide us in a variety of industries. A stator inter-turn fault (SITF) is considered to be the most common electrical failure according to statistical studies. In this paper, an algorithm for the detection of an SITF is presented. It is based on one of the blind source separation techniques called principal component analysis (PCA). The proposed algorithm uses PCA to discriminate between the faulty components of motor current signatures and motor voltage signatures from other components. The standard deviation of one of the decomposed vectors is used as a statistical SITF criterion. The proposed criterion is robust to non-fault conditions including voltage quality problems and large mechanical load changes as well as harmonic contaminants in the voltage supply. In addition, with a straightforward and low computational burden in the fault detection process, the proposed method is computationally efficient. To evaluate the performance of the proposed method, large numbers of practical and simulation scenarios are considered, and the results confirm the good performance, high degree of accuracy, and good convergence speed of the proposed method.

show abstract

“…Traditional harmonic extraction methods and their improvement algorithms can be divided into two types: frequency domain methods and time domain methods. Frequency domain harmonic extraction methods are mainly based on Fast Fourier Transform (FFT) analysis [6] and the wavelet transform (WT) [7] method, etc. FFT is one of the most typical and dominant methods used for harmonic detection.…”

Section: Introductionmentioning

confidence: 99%

Rapid Harmonic Detection Scheme Based on Expanded Input Observer

Zhu

et al. 2022

Electronics

View full text Add to dashboard Cite

The existence of harmonics will cause the quality of power supply in a power system to decline and will affect the normal use of the power system. Therefore, it is important to suppress harmonics in the power system, and the first step of harmonic suppression is harmonic detection. To address this phenomenon, a fast harmonic detection method is proposed in this paper. It is based on the input observer theory to construct a state space model based on the original signal and harmonic components and estimate the state variables so as to achieve harmonic extraction. The characteristic roots are used to prove the convergence of the observer. In addition, the Second-Order Generalized Integration (SOGI) frequency estimation method is chosen to cascade with it so that harmonic detection can be accomplished with unknown frequencies. The simulation results prove that the proposed method can quickly converge and accurately extract each harmonic in the case of fluctuations in the fundamental amplitude, fundamental frequency and phase of the input signal, and the whole process can be completed in 0.02 s. The possible effects of white noise on harmonic extraction are also simulated, and the results prove that the accuracy of harmonic extraction can still be guaranteed in the presence of white noise. By using the Speedgoat real-time target machine built Rapid Control Prototype (RCP) as a testbed, experiments with similar simulation conditions were performed. The results show that the method has fast and accurate harmonic detection performance.

show abstract

Method for extracting current envelope for broken rotor bar fault detection of induction motors at time‐varying loads

Cited by 10 publications

References 36 publications

Method for Identifying Stator and Rotor Faults of Induction Motors Based on Machine Vision

Method for Identifying Stator and Rotor Faults of Induction Motors Based on Machine Vision

A robust principal component analysis-based approach for detection of a stator inter-turn fault in induction motors

Rapid Harmonic Detection Scheme Based on Expanded Input Observer

Contact Info

Product

Resources

About