Vibration Based Modal Parameters Identification and Wear Fault Diagnosis Using Laplace Wavelet

2005 ◽  
Vol 293-294 ◽  
pp. 183-192 ◽  
Author(s):  
Yanyang Zi ◽  
Xue Feng Chen ◽  
Zheng Jia He ◽  
Peng Chen
Keyword(s):  
Fault Diagnosis ◽  
Impulse Response ◽  
Combustion Engine ◽  
Intake Valve ◽  
Impulse Responses ◽  
Transient Responses ◽  
Vibration Signals ◽  
Modal Parameters Identification ◽  
Non Stationary Signal ◽  
Correlation Filtering

Wavelet transform is a powerful technique well suited to non-stationary signal processing. The properties of wavelet are determined by its basis function. In the fields of modal analysis, mechanical condition monitoring and fault diagnosis, impulse responses or transient responses are very common signals to be analyzed. The Laplace wavelet is a single-sided damped exponential wavelet and is a desirable wavelet basis to analyze signals of impulse response. A correlation filtering approach is introduced using the Laplace wavelet to identify the impulse response from vibration signals. Successful results are obtained in identifying the natural frequency of a hydro-generator shaft, and diagnosing the wear fault of intake valve of an internal combustion engine.

Wavelet denoising using different mother wavelets for fault diagnosis of engine spark plug

2015 ◽  
Vol 231 (3) ◽  
pp. 359-370 ◽  
Author(s):  
Ashkan Moosavian ◽  
Meghdad Khazaee ◽  
Gholamhassan Najafi ◽  
Majid Khazaee ◽  
Babak Sakhaei ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Combustion Engine ◽  
Wavelet Denoising ◽  
Performance Comparison ◽  
Support Vector ◽  
Vibration Signals ◽  
Spark Plug ◽  
Different Levels ◽  
Mother Wavelets

This paper deals with vibration-fault diagnosis of spark plug of an internal combustion engine using wavelet analysis and support vector machine. In order to reduce the noises of the vibration signals, wavelet denoising technique was used. A performance comparison was made between different mother wavelets as well as different levels of decomposition in order to find the best cases for the system under study. The results showed that the maximum classification accuracies were obtained by 13 different wavelets, namely, db1_4, db1_5, db2_4, db3_4, coif1_4, coif1_5, coif2_4, coif3_3, coif3_4, coif3_5, dmey_2, dmey_4 and bior3.7_6. It was also demonstrated that db1, coif1, coif3 and dmey were valuable mother wavelets for this study. Moreover, the results indicated that the proposed approach can reliably be used for spark plug fault diagnosis.

Fault Diagnosis for Misfire and Abnormal Clearance in a Diesel Engine Based on EEMD

2011 ◽  
Vol 97-98 ◽  
pp. 702-705
Author(s):  
Jin Ming Lu ◽  
Fan Lin Meng ◽  
Hua Shen ◽  
Li Bin Ding ◽  
Jie Ma
Keyword(s):  
Fault Diagnosis ◽  
Energy Density ◽  
Valve Train ◽  
Intake Valve ◽  
Density Spectrum ◽  
Vibration Signals ◽  
Diagnosis Method ◽  
Diesel Cylinder ◽  
Instantaneous Energy ◽  
Mode Mixing

The misfire of one or more diesel cylinder and the abnormal clearance in the intake valve train of cylinder are common faults which affect the safety and the performance of the engine seriously. A new fault diagnosis method based on EEMD and instantaneous energy density spectrum is proposed here. The IMFs generated by EEMD can alleviate the problem of mode mixing and approach the reality IMFs. The instantaneous energy density of these IMFs can distinguish the faulty impacts clearly. The effectiveness of this method was demonstrated by analysis the vibration signals of misfire fault and abnormal clearance in the intake valve train of 3110 diesel.

scholarly journals Gear fault diagnosis using Autogram analysis

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401881253 ◽  
Author(s):  
Adel Afia ◽  
Chemseddine Rahmoune ◽  
Djamel Benazzouz
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Discrete Wavelet ◽  
Modern Approach ◽  
Vibration Signals ◽  
Discrete Wavelet Packet Transform ◽  
Gear Fault ◽  
Data Signal ◽  
Non Gaussian ◽  
Non Stationary Signal

Rotary machines consist of various devices such as gears, bearings, and shafts that operate simultaneously. As a result, vibration signals have nonlinear and non-stationary behavior, and the fault signature is always buried in overwhelming and interfering contents, especially in the early stages. As one of the most powerful non-stationary signal processing techniques, Kurtogram has been widely used to detect gear failure. Usually, vibration signals contain a relatively strong non-Gaussian noise which makes the defective frequencies non-dominant in the spectrum compared to the discrete components, which reduce the performance of the above method. Autogram is a new sophisticated enhancement of the conventional Kurtogram. The modern approach decomposes the data signal by Maximal Overlap Discrete Wavelet Packet Transform into frequency bands and central frequencies called nodes. Subsequently, the unbiased autocorrelation of the squared envelope for each node is computed to select the node with the highest kurtosis value. Finally, Fourier transform is applied to that squared envelope to extract the fault signature. In this article, the proposed method is tested and compared to Fast Kurtogram for gearbox fault diagnosis using experimental vibration signals. The experimental results improve the detectability of the proposed method and affirm its effectiveness.

COMPOUND FAULT DIAGNOSIS OF BEARINGS USING AN IMPROVED SPECTRAL KURTOSIS BY MAXIMUM CORRELATION KURTOSIS DECONVOLUTION (MCKD).

2020 ◽  
Vol 9 (8) ◽  
pp. 66-72
Keyword(s):  
Fault Diagnosis ◽  
Resonance Frequency ◽  
Background Noise ◽  
Maximum Correlation ◽  
Vibration Signals ◽  
Filter Length ◽  
Frequency Bands ◽  
Noise Interference ◽  
Spectral Kurtosis

This paper discusses the use of Maximum Correlation kurtosis deconvolution (MCKD) method as a pre-processor in fast spectral kurtosis (FSK) method in order to find the compound fault characteristics of the bearing, by enhancing the vibration signals. FSK only extracts the resonance bands which have maximum kurtosis value, but sometimes it might possible that faults occur in the resonance bands which has low kurtosis value, also the faulty signals missed due to noise interference. In order to overcome these limitations FSK used with MCKD, MCKD extracts various faults present in different resonance frequency bands; also detect the weak impact component, as MCKD also dealt with strong background noise. By obtaining the MCKD parameters like, filter length & deconvolution period, we can extract the compound fault feature characteristics.

scholarly journals A Hydraulic Pump Fault Diagnosis Method Based on the Modified Ensemble Empirical Mode Decomposition and Wavelet Kernel Extreme Learning Machine Methods

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2599
Author(s):  
Zhenbao Li ◽  
Wanlu Jiang ◽  
Sheng Zhang ◽  
Yu Sun ◽  
Shuqing Zhang
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Recognition Accuracy ◽  
Ensemble Empirical Mode Decomposition ◽  
Hydraulic Pump ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

To address the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an integrated hydraulic pump fault diagnosis method based on the modified ensemble empirical mode decomposition (MEEMD), autoregressive (AR) spectrum energy, and wavelet kernel extreme learning machine (WKELM) methods is presented in this paper. First, the non-linear and non-stationary hydraulic pump vibration signals are decomposed into several intrinsic mode function (IMF) components by the MEEMD method. Next, AR spectrum analysis is performed for each IMF component, in order to extract the AR spectrum energy of each component as fault characteristics. Then, a hydraulic pump fault diagnosis model based on WKELM is built, in order to extract the features and diagnose faults of hydraulic pump vibration signals, for which the recognition accuracy reached 100%. Finally, the fault diagnosis effect of the hydraulic pump fault diagnosis method proposed in this paper is compared with BP neural network, support vector machine (SVM), and extreme learning machine (ELM) methods. The hydraulic pump fault diagnosis method presented in this paper can diagnose faults of single slipper wear, single slipper loosing and center spring wear type with 100% accuracy, and the fault diagnosis time is only 0.002 s. The results demonstrate that the integrated hydraulic pump fault diagnosis method based on MEEMD, AR spectrum, and WKELM methods has higher fault recognition accuracy and faster speed than existing alternatives.

Optimal multi-kernel local fisher discriminant analysis for feature dimensionality reduction and fault diagnosis

2021 ◽  
pp. 1748006X2110093
Author(s):  
Qing Zhang ◽  
Heng Li ◽  
Xiaolong Zhang ◽  
Haifeng Wang
Keyword(s):  
Fault Diagnosis ◽  
Discriminant Analysis ◽  
Feature Space ◽  
High Dimensional ◽  
Fisher Discriminant Analysis ◽  
Vibration Signals ◽  
Fisher Discriminant ◽  
Local Fisher Discriminant Analysis ◽  
Diagnosis Accuracy ◽  
Diagnosis Model

To achieve a more desirable fault diagnosis accuracy by applying multi-domain features of vibration signals, it is significative and challenging to refine the most representative and intrinsic feature components from the original high dimensional feature space. A novel dimensionality reduction method for fault diagnosis is proposed based on local Fisher discriminant analysis (LFDA) which takes both label information and local geometric structure of the high dimensional features into consideration. Multi-kernel trick is introduced into the LFDA to improve its performance in dealing with the nonlinearity of mapping high dimensional feature space into a lower one. To obtain an optimal diagnosis accuracy by the reduced features of low dimensionality, binary particle swarm optimization (BPSO) algorithm is utilized to search for the most appropriate parameters of kernels and K-nearest neighbor (kNN) recognition model. Samples with labels are used to train the optimal multi-kernel LFDA and kNN (OMKLFDA-kNN) fault diagnosis model to obtain the optimal transformation matrix. Consequently, the trained fault diagnosis model implements the recognition of machinery health condition with the most representative feature space of vibration signals. A bearing fault diagnosis experiment is conducted to verify the effectiveness of proposed diagnostic approach. Performance comparison with some other methods are investigated, and the improvement for fault diagnosis of the proposed method are confirmed in different aspects.

scholarly journals A Fusion Feature Extraction Method Using EEMD and Correlation Coefficient Analysis for Bearing Fault Diagnosis

2018 ◽  
Vol 8 (9) ◽  
pp. 1621 ◽  
Author(s):  
Fan Jiang ◽  
Zhencai Zhu ◽  
Wei Li ◽  
Yong Ren ◽  
Gongbo Zhou ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Correlation Coefficient ◽  
Feature Space ◽  
Support Vector ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Acceleration Sensors ◽  
Reconstructed Signal ◽  
Original Feature

Acceleration sensors are frequently applied to collect vibration signals for bearing fault diagnosis. To fully use these vibration signals of multi-sensors, this paper proposes a new approach to fuse multi-sensor information for bearing fault diagnosis by using ensemble empirical mode decomposition (EEMD), correlation coefficient analysis, and support vector machine (SVM). First, EEMD is applied to decompose the vibration signal into a set of intrinsic mode functions (IMFs), and a correlation coefficient ratio factor (CCRF) is defined to select sensitive IMFs to reconstruct new vibration signals for further feature fusion analysis. Second, an original feature space is constructed from the reconstructed signal. Afterwards, weights are assigned by correlation coefficients among the vibration signals of the considered multi-sensors, and the so-called fused features are extracted by the obtained weights and original feature space. Finally, a trained SVM is employed as the classifier for bearing fault diagnosis. The diagnosis results of the original vibration signals, the first IMF, the proposed reconstruction signal, and the proposed method are 73.33%, 74.17%, 95.83% and 100%, respectively. Therefore, the experiments show that the proposed method has the highest diagnostic accuracy, and it can be regarded as a new way to improve diagnosis results for bearings.

The Application of Local Mean Decomposition and Variable Predictive Model-Based Class Discriminate in Gear Fault Diagnosis

2014 ◽  
Vol 1014 ◽  
pp. 510-515 ◽  
Author(s):  
You Cai Xu ◽  
Xin Shi Li ◽  
Ran Tao ◽  
Shu Guo ◽  
Min Gou ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Predictive Model ◽  
Feature Vector ◽  
Local Mean Decomposition ◽  
Outer Race ◽  
Model Based ◽  
Gear Fault ◽  
Local Mean ◽  
Gear Fault Diagnosis ◽  
Non Stationary Signal

The time-domain energy message conveyed by vibration signals of different gear fault are different, so a method based on local mean decomposition (LMD) and variable predictive model-based class discriminate (VPMCD) is proposed to diagnose gear fault model. The vibration signal of gear which is the research object in this paper is decomposed into a series of product functions (PF) by LMD method. Then a further analysis is to select the PF components which contain main fault information of gear, the energy feature parameters of the selected PF components are used to form a fault feature vector. The variable predictive model-based class discriminate is a new multivariate classification approach for pattern recognition, through taking fully advantages of the fault feature vector. Finally, gear fault diagnosis is distinguished into normal state, inner race fault and outer race fault. The results show that LMD method can decompose a complex non-stationary signal into a number of PF components whose frequency is from high to low. And the method based on LMD and VPMCD has a high fault recognition function by analyzing the fault feature vector of PF.

scholarly journals Energy Analysis Method Based on Wavelet Transform for Sensor Fault Diagnosis

2011 ◽  
Vol 2-3 ◽  
pp. 117-122 ◽  
Author(s):  
Peng Peng Qian ◽  
Jin Guo Liu ◽  
Wei Zhang ◽  
Ying Zi Wei
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Energy Analysis ◽  
Sensor Faults ◽  
Analysis Method ◽  
Sensor Fault ◽  
Fault Type ◽  
Ideal Tool ◽  
Non Stationary Signal

Wavelet analysis with its unique features is very suitable for analyzing non-stationary signal, and it can also be used as an ideal tool for signal processing in fault diagnosis. The characteristics of the faults and the necessary information on the diagnosis can be constructed and extracted respectively by wavelet analysis. Though wavelet analysis is specialized in characteristics extraction, it can not determine the fault type. So this paper has proposed an energy analysis method based on wavelet transform. Experiment results show the method is very effective for sensor fault diagnosis, because it can not only detect the sensor faults, but also determine the fault type.

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