Vibration Analysis of Coupled Faults Diagnosis in a Rotor System Using Wavelet De-Noising and KPCA Data Fusion

2012 ◽  
Vol 192 ◽  
pp. 233-236
Author(s):  
Xiu Mei Zhu
Keyword(s):  
Fault Diagnosis ◽  
Principal Component ◽  
Rotor System ◽  
Kernel Principal Component Analysis ◽  
Vibration Signals ◽  
Rotor Systems ◽  
Fault Features ◽  
Simultaneous Existence ◽  
Faults Diagnosis

In a rotor system, simultaneous existence of coupled faults, i.e. a crack couples with a misalignment, is very common. However, the single fault diagnosis has been investigated extensively in previous work while the issue of coupled faults diagnosis (i.e. considering two or more than two faults at a time) has been addressed insufficiently. In order to detect the existence of coupled faults and to prevent a fatigue crack in the rotor shaft, a new method is proposed to analyze the vibration signals using the Wavelet de-nosing and kernel principal component analysis (KPCA) in this work. The Wavelet was firstly used to de-noise the original vibration signals, and then the KPCA was adopted to extract useful fault features for the coupled faults detection. A case study on the coupled fault diagnosis of the rotor system has been implemented. The diagnosis results demonstrate that the proposed method is feasible for the coupled fault diagnosis of rotor systems. The fault detection rate is 91.0%.

scholarly journals A Novel Method for Identifying Crack and Shaft Misalignment Faults in Rotor Systems under Noisy Environments Based on CNN

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5158 ◽  
Author(s):  
Wang Zhao ◽  
Chunrong Hua ◽  
Dawei Dong ◽  
Huajiang Ouyang
Keyword(s):  
Signal To Noise Ratio ◽  
Principal Component ◽  
Rotor System ◽  
Fault Classification ◽  
Vibration Signals ◽  
Rotor Systems ◽  
Response Characteristics ◽  
Mode Decomposition ◽  
Shaft Misalignment ◽  
Band Limited

Crack and shaft misalignment are two common types of fault in a rotor system, both of which have very similar dynamic response characteristics, and the vibration signals are vulnerable to noise contamination because of the interaction among different components of rotating machinery in the actual industrial environment, resulting in great difficulties in fault identification of a rotor system based on vibration signals. A method for identification of faults in the form of crack and shaft misalignments is proposed in this paper, which combines variational mode decomposition (VMD) and probabilistic principal component analysis (PPCA) to denoise the collected vibration signals from a test rig and then achieve signal feature extraction and fault classification with convolutional artificial neural network (CNN). The key parameters of the CNN are optimized and determined by genetic algorithm (GA) firstly, and the domain adaptability of the trained network is verified by the signals with different signal-to-noise ratio (SNR) values; then, the noisy vibration signals are decomposed into multiple band-limited intrinsic modal functions by VMD, and further data dimension reduction is performed by PPCA to realize the separation of the useful signals from noise; finally, the crack and shaft misalignment of the rotor system are identified by the optimized CNN. The results show that the proposed method can effectively remove the interference noise and extract the intrinsic features of the vibration signals, and the recognition rates of crack and shaft misalignment faults for the rotor system with different SNR values are more than 99%, which is considered to be very effective and useful.

scholarly journals KPCA and AE Based Local-Global Feature Extraction Method for Vibration Signals of Rotating Machinery

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Xiao Hu ◽  
Zhihuai Xiao ◽  
Dong Liu ◽  
Yongjun Tang ◽  
O. P. Malik ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Frequency Spectrum ◽  
Extraction Method ◽  
Principal Component ◽  
Rotating Machinery ◽  
Kernel Principal Component Analysis ◽  
Global Feature ◽  
Vibration Signals ◽  
Feature Extraction Method

Feature extraction plays a key role in fault diagnosis of rotating machinery. Many methods reported in the literature are based on masses of labeled data and need much prior knowledge to select the most discriminating features or establish a complex deep-learning model. To solve the dilemma, a novel feature extraction method based on kernel principal component analysis (KPCA) and an autoencoder (AE), namely, SFS-KPCA-AE, is presented in this paper to automatically extract the most discriminative features from the frequency spectrum of vibration signals. First, fast Fourier transform is calculated on the entire vibration signal to get the frequency spectrum. Next, the spectrum is divided into several segments. Then, local-global feature extraction is performed by applying KPCA to these segments. Finally, an AE is employed to obtain the low-dimensional representations of the high-dimensional global feature. The proposed feature extraction method combined with a classifier achieves fault diagnosis for rotating machinery. A rotor dataset and a bearing dataset are utilized to validate the performance of the proposed method. Experimental results demonstrate that the proposed method achieved satisfactory performance in feature extraction when training samples or motor load changed. By comparing with other methods, the superiority of the proposed SFS-KPCA-AE is verified.

Local rub-impact fault diagnosis of a rotor system based on adaptive local iterative filtering

2015 ◽  
Vol 39 (5) ◽  
pp. 748-753 ◽  
Author(s):  
Xueli An
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rotor System ◽  
Periodic Signal ◽  
Rotor Vibration ◽  
Fault Features ◽  
Iterative Filtering ◽  
Main Components ◽  
Fault Information

When rotor rubbing occurs, the vibration signal comprises a periodic signal, a transient impact signal and noise. The main components of the periodic signal are the rotating frequency and harmonics thereof. The transient impact signal includes the rotor fault information. According to the characteristics of the local rub-impact fault in a rotor system, an adaptive local iterative filtering (ALIF) method is applied to fault diagnosis of the rotor local rub-impact. The ALIF method is used to decompose rotor vibration signals and can separate the rub, background and noise signals. The fault features of rotor local rub-impact can be extracted from the rotor vibration signal. A case study showed that the ALIF method can be effectively applied to the fault diagnosis of rotor local rub-impact.

Application Study on Resonance Demodulation Technique in Fault Diagnosis of Gear Box

2010 ◽  
Vol 37-38 ◽  
pp. 198-202 ◽  
Author(s):  
Qian Hao ◽  
Li Xin Gao ◽  
Xu Wang ◽  
Hui Ye
Keyword(s):  
Fault Diagnosis ◽  
Basic Principle ◽  
Application Study ◽  
Vibration Signals ◽  
Fault Features ◽  
Gear Box

Fault vibration signals of gear box are faint shock and are hardly extracted, which bring a big difficulty for diagnosis. This paper mainly introduces the basic principle of the resonance demodulation technique and its application study in the gear box’s fault diagnosis. The developed resonance demodulation technique can effectively extract the fault features from complex vibration signals through the resonance demodulation technique according to the vibration features of the gear box, and improve the equipments’ fault diagnosis precision.

Sensitivity analysis of factors affecting motion reliability of manipulator and fault diagnosis based on kernel principal component analysis

Robotica ◽  
2021 ◽  
pp. 1-20
Author(s):  
Jing Yang ◽  
Lingyan Jin ◽  
Zejie Han ◽  
Deming Zhao ◽  
Ming Hu
Keyword(s):  
Principal Component Analysis ◽  
Fault Diagnosis ◽  
Principal Component ◽  
Component Analysis ◽  
Joint Clearance ◽  
Kernel Principal Component Analysis ◽  
Joint Rotation ◽  
Object A ◽  
Motion Performance ◽  
Flexibility Factor

Abstract As an important index to quantitatively measure the motion performance of a manipulator, motion reliability is affected by many factors, such as joint clearance. The present research utilized a UR10 manipulator as the research object. A factor mapping model for influencing the motion reliability was established. The link flexibility factor, joint flexibility factor, joint clearance factor, and Denavit–Hartenberg (DH) parameters were comprehensively considered in this model. The coupling relationship among the various factors was concisely expressed. Subsequently, the nonlinear response surface method was used to calculate the reliability and sensitivity of the manipulator, which provided an applicable reference for its trajectory planning and motion control. In addition, a data-driven fault diagnosis method based on the kernel principal component analysis (KPCA) was used to verify the motion accuracy and sensitivity of the manipulator, and joint rotation failure was considered as an example to verify the accuracy of the KPCA method. This study on the motion reliability of the manipulator is of great significance for the current motion performance, adjusting the control strategy and optimizing the completion effect of the motion task of a manipulator.

scholarly journals Combining DBN and FCM for Fault Diagnosis of Roller Element Bearings without Using Data Labels

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Fan Xu ◽  
Yan jun Fang ◽  
Dong Wang ◽  
Jia qi Liang ◽  
Kwok Leung Tsui
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Vibration Signals ◽  
Fuzzy C Means ◽  
Roller Bearings ◽  
Mode Decomposition

Because deep belief networks (DBNs) in deep learning have a powerful ability to extract useful information from the raw data without prior knowledge, DBNs are used to extract the useful feature from the roller bearings vibration signals. Unlike classification methods, the clustering method can classify the different fault types without data label. Therefore, a method based on deep belief networks (DBNs) in deep learning (DL) and fuzzy C-means (FCM) clustering algorithm for roller bearings fault diagnosis without a data label is presented in this paper. Firstly, the roller bearings vibration signals are extracted by using DBN, and then principal component analysis (PCA) is used to reduce the dimension of the vibration signal features. Secondly, the first two principal components (PCs) are selected as the input of fuzzy C-means (FCM) for roller bearings fault identification. Finally, the experimental results show that the fault diagnosis of the method presented is better than that of other combination models, such as variation mode decomposition- (VMD-) singular value decomposition- (SVD-) FCM, and ensemble empirical mode decomposition- (EEMD-) fuzzy entropy- (FE-) PCA-FCM.

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