scholarly journals A Segmented Preprocessing Method for the Vibration Signal of an On-Load Tap Changer

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 131
Author(s):  
Rongyan Shang ◽  
Changqing Peng ◽  
Ruiming Fang
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Vibration Signals ◽  
Contact Wear ◽  
Preprocessing Method ◽  
Mechanical Faults ◽  
Tap Changer ◽  
The Time Domain ◽  
Load Tap Changer ◽  
Selection Of

The vibration signal of an on-load tap changer (OLTC) consists of a series of sharp vibration bursts, and its fault feature in certain periods is easily missed. This study considered that preprocessing the vibration signal of the OLTC in segments could effectively solve the aforementioned problem. First, the collection of the signal is discussed, the waveform characteristics of the vibration signal when the OLTC was in normal action was described, and the selection of the signal was analyzed. Second, the time domain characteristics and frequency spectrum analyses were carried out to demonstrate the necessity of segmented preprocessing. Further, the segmented preprocessing method for the vibration signal of the OLTC was presented. Finally, the main mechanical faults of the OLTC were simulated, and the vibration signals were collected to carry out the fault diagnosis experiment on the OLTC. The experimental results showed that the accuracy of the fault diagnosis increased from 94.30% of the nonsegmented preprocessing to 98.46% of the segmented preprocessing. The increase was greater, especially for contact wear faults. The method was successfully applied to the actual project.

Gearbox Fault Diagnosis Using Vibration Signal with Wavelet De-Noising

2011 ◽  
Vol 86 ◽  
pp. 735-738
Author(s):  
Zhi Feng Dong ◽  
Hui Cheng ◽  
Hui Jia Yang ◽  
Wei Fu ◽  
Ji Wei Chen ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Frequency Domain Analysis ◽  
Sensor Data ◽  
Spectrum Estimation ◽  
Vibration Signals ◽  
Fault Features ◽  
Vibration Signal Analysis ◽  
Simulation Signal ◽  
The Time Domain

This paper dealt with the gearbox fault diagnosis with vibration signal analysis. The vibration signals from experiment contained a lot of noises which result from motor, gears, bears and box, and were collected through accelerate sensor, data collector and computer. The wavelet de-noising stratification was used to de-noise the vibration signals before the frequency-domain analysis was done. The effects of the simulation signal de-noising was contrasted, and the noise cancellation the power spectrum estimation was carried out. The experimental and analytical results show that the different features are indicated with vibration signal of the normal gearbox and the signal with bolts loosened of the gearbox. The gearbox fault with bolts loosened can be diagnosed by extracting the time-domain fault features of vibration signals.

scholarly journals Experimental Investigation and Fault Diagnosis for Buckled Wet Clutch Based on Multi-Speed Hilbert Spectrum Entropy

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1704
Author(s):  
Jiaqi Xue ◽  
Biao Ma ◽  
Man Chen ◽  
Qianqian Zhang ◽  
Liangjie Zheng
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Vibration Signal ◽  
Bench Test ◽  
Vehicle Performance ◽  
Vibration Signals ◽  
Hilbert Spectrum ◽  
Wet Clutch ◽  
Diagnosis Method ◽  
The Time Domain

The multi-disc wet clutch is widely used in transmission systems as it transfers the torque and power between the gearbox and the driving engine. During service, the buckling of the friction components in the wet clutch is inevitable, which can shorten the lifetime of the wet clutch and decrease the vehicle performance. Therefore, fault diagnosis and online monitoring are required to identify the buckling state of the friction components. However, unlike in other rotating machinery, the time-domain features of the vibration signal lack efficiency in fault diagnosis for the wet clutch. This paper aims to present a new fault diagnosis method based on multi-speed Hilbert spectrum entropy to classify the buckling state of the wet clutch. Firstly, the wet clutch is classified depending on the buckling degree of the disks, and then a bench test is conducted to obtain vibration signals of each class at varying speeds. By comparing the accuracy of different classifiers with and without entropy, Hilbert spectrum entropy shows higher efficiency than time-domain features for the wet clutch diagnosis. Thus, the classification results based on multi-speed entropy achieve even better accuracy.

scholarly journals Fault Diagnosis of On-load Tap-changer Based on Vibration Signal

2019 ◽  
Vol 605 ◽  
pp. 012005
Author(s):  
Wenquan Huang ◽  
Changqing Peng ◽  
Liang Jin ◽  
Jiacheng Liu ◽  
Yuqi Cai
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Tap Changer ◽  
Load Tap Changer

scholarly journals Vibration-Based Fault Diagnosis of Commutator Motor

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Adam Glowacz ◽  
Witold Glowacz
Keyword(s):  
Fault Diagnosis ◽  
Extraction Methods ◽  
Total Efficiency ◽  
Practical Application ◽  
Backpropagation Neural Network ◽  
Vibration Signals ◽  
Linear Discriminant ◽  
Recognition Efficiency ◽  
Signal Processing Methods ◽  
Selection Of

This paper presents a study on vibration-based fault diagnosis techniques of a commutator motor (CM). Proposed techniques used vibration signals and signal processing methods. The authors analysed recognition efficiency for 3 states of the CM: healthy CM, CM with broken tooth on sprocket, CM with broken rotor coil. Feature extraction methods called MSAF-RATIO-50-SFC (method of selection of amplitudes of frequencies ratio 50 second frequency coefficient), MSAF-RATIO-50-SFC-EXPANDED were implemented and used for an analysis. Feature vectors were obtained using MSAF-RATIO-50-SFC, MSAF-RATIO-50-SFC-EXPANDED, and sum of RSoV. Classification methods such as nearest mean (NM) classifier, linear discriminant analysis (LDA), and backpropagation neural network (BNN) were used for the analysis. A total efficiency of recognition was in the range of 79.16%–93.75% (TV). The proposed methods have practical application in industries.

scholarly journals Engine Fault Detection Approach Based on Angle Domain Signal Model

2021 ◽  
Vol 2068 (1) ◽  
pp. 012034
Author(s):  
Hai Zeng ◽  
Ning Zeng ◽  
Jin Han ◽  
Yan Ding
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Failure Detection ◽  
Vibration Signal ◽  
Bench Test ◽  
Signal Model ◽  
Vibration Signals ◽  
Engine Vibration ◽  
Detection Approach ◽  
Diagnosis Approach

Abstract Engine vibration signals include strong noise and non-stationary signals. By the time domain signal processing approach, it is hard to extract the failure features of engine vibration signals, so it is hard to identify engine failures. For improving the success rate of engine failure detection, an engine angle domain vibration signal model is established and an engine fault detection approach based on the signal model is proposed. The angle domain signal model reveals the modulation feature of the engine angular signal. The engine fault diagnosis approach based on the angle domain signal model involves equal angle sampling and envelope analysis of engine vibration signals. The engine bench test verifies the effectiveness of the engine fault diagnosis approach based on the angle domain signal model. In addition, this approach indicates a new path of engine fault diagnosis and detection.

Mine Ventilator Fault Diagnosis Based on Harmonic Wavelet Analysis

2011 ◽  
Vol 143-144 ◽  
pp. 613-617
Author(s):  
Shuang Xi Jing ◽  
Yong Chang ◽  
Jun Fa Leng
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Frequency Domain ◽  
Vibration Signal ◽  
Wavelet Function ◽  
Frequency Region ◽  
Analysis Method ◽  
Vibration Signals ◽  
Harmonic Wavelet ◽  
Frequency Components

Harmonic wavelet function, with the strict box-shaped characteristic of spectrum, has strong ability of identifying signal in frequency domain, and can extract weak components form vibration signals in frequency domain. Using harmonic wavelet analysis method, the selected frequency region and other frequency components of vibration signal of mine ventilator were decomposed into independent frequency bands without any over-lapping or leaking. Simulation and diagnosis example show that this method has good fault diagnosis effect, and the ventilator fault is diagnosed successfully.

Research on Vibration Signal of Transformer On- load Tap Changer Based on Kurtosis and Tunable Q-factor Wavelet Transform

2021 ◽  
Author(s):  
Jiajun Lin ◽  
Yiming Zheng ◽  
Lin Zhao ◽  
Chen Li ◽  
Xiang Sun ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Vibration Signal ◽  
Q Factor ◽  
Tap Changer ◽  
Load Tap Changer

scholarly journals A Reliable Fault Diagnosis Method for a Gearbox System with Varying Rotational Speeds

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3105 ◽  
Author(s):  
Cong Dai Nguyen ◽  
Alexander Prosvirin ◽  
Jong-Myon Kim
Keyword(s):  
Fault Diagnosis ◽  
Reference Signal ◽  
Vibration Signal ◽  
Support Vector ◽  
Health States ◽  
Informative Signal ◽  
Vibration Signals ◽  
Optimal Output ◽  
Gear Fault ◽  
Diagnosis Method

The vibration signals of gearbox gear fault signatures are informative components that can be used for gearbox fault diagnosis and early fault detection. However, the vibration signals are normally non-linear and non-stationary, and they contain background noise caused by data acquisition systems and the interference of other machine elements. Especially in conditions with varying rotational speeds, the informative components are blended with complex, unwanted components inside the vibration signal. Thus, to use the informative components from a vibration signal for gearbox fault diagnosis, the noise needs to be properly distilled from the informational signal as much as possible before analysis. This paper proposes a novel gearbox fault diagnosis method based on an adaptive noise reducer–based Gaussian reference signal (ANR-GRS) technique that can significantly reduce noise and improve classification from a one-against-one, multiclass support vector machine (OAOMCSVM) for the fault types of a gearbox. The ANR-GRS processes the shaft rotation speed to access and remove noise components in the narrowbands between two consecutive sideband frequencies along the frequency spectrum of a vibration signal, enabling the removal of enormous noise components with minimal distortion to the informative signal. The optimal output signal from the ANR-GRS is then extracted into many signal feature vectors to generate a qualified classification dataset. Finally, the OAOMCSVM classifies the health states of an experimental gearbox using the dataset of extracted features. The signal processing and classification paths are generated using the experimental testbed. The results indicate that the proposed method is reliable for fault diagnosis in a varying rotational speed gearbox system.

Study on the Method of Vibration Signal De-Noising Using Wavelet Packet Based on QPSO

2014 ◽  
Vol 599-601 ◽  
pp. 1738-1744
Author(s):  
Kai Zhao ◽  
Ben Wei Li ◽  
Jing Chen
Keyword(s):  
Experimental Verification ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Wavelet Function ◽  
Threshold Function ◽  
Vibration Signals ◽  
Selection Of

Although many wavelet de-noising methods have been studied and proposed, the parameters of them are obtained by experience mostly, which makes the de-noising effect instable. To solve the issues, the solutions, such as the selection of wavelet function and threshold function, the calculation of decomposition levels, the optimal wavelet packet basis and the thresholds obtained based on QPSO, have been studied in this paper. Every parameter is obtained by calculation. This method is applied to the de-noising experiment of sine and vibration signals. Through the experimental verification, the effect of this de-noising method is obvious.

scholarly journals Fault Diagnosis of On-Load Tap-Changer Based on Variational Mode Decomposition and Relevance Vector Machine

Energies ◽  
2017 ◽  
Vol 10 (7) ◽  
pp. 946 ◽  
Author(s):  
Jinxin Liu ◽  
Guan Wang ◽  
Tong Zhao ◽  
Li Zhang
Keyword(s):  
Fault Diagnosis ◽  
Relevance Vector Machine ◽  
Variational Mode Decomposition ◽  
Mode Decomposition ◽  
Tap Changer ◽  
Load Tap Changer
Sign in / Sign up

Export Citation Format

Share Document