Fault Diagnosis of Wet-Shift Clutch Based on STFT and Wavelet

2011 ◽  
Vol 301-303 ◽  
pp. 1560-1567 ◽  
Author(s):  
Man Chen ◽  
Biao Ma
Keyword(s):  
Fault Diagnosis ◽  
Fourier Analysis ◽  
Wavelet Decomposition ◽  
Pulse Signal ◽  
Background Signal ◽  
Vibration Signals ◽  
Diagnosis Method ◽  
Friction Plate ◽  
Short Time ◽  
Shift Clutch

The paper analyzes the failure mechanism of the wet shifting clutch, and puts forward the concept that the deformation of the clutch friction plate leads to the irregular collision between the driving and driven sides of disengaged clutch and accordingly forms the transient pulse signal; the short-time Fourier analysis on the vibration signals of failed clutch obtained via test proves such concept. The transient pulse signal in the relatively strong background signal is clearly extracted through the wavelet decomposition after zero setting, and an efficient wet shifting clutch fault diagnosis method is hereby formed.

scholarly journals Fault Diagnosis of Rotating Machinery Based on One-Dimensional Deep Residual Shrinkage Network with a Wide Convolution Layer

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jingli Yang ◽  
Tianyu Gao ◽  
Shouda Jiang ◽  
Shijie Li ◽  
Qing Tang
Keyword(s):  
Fault Diagnosis ◽  
Noise Immunity ◽  
Rotating Machinery ◽  
Experimental Results ◽  
Vibration Signals ◽  
One Dimensional ◽  
Noise Interference ◽  
Diagnosis Method ◽  
Diagnosis Model ◽  
Short Time

In actual engineering applications, inevitable noise seriously affects the accuracy of fault diagnosis for rotating machinery. To effectively identify the fault classes of rotating machinery under noise interference, an efficient fault diagnosis method without additional denoising procedures is proposed. First, a one-dimensional deep residual shrinkage network, which directly takes the raw vibration signals contaminated by noise as input, is developed to realize end-to-end fault diagnosis. Then, to further enhance the noise immunity of the diagnosis model, the first layer of the model is set to a wide convolution layer to extract short time features. Moreover, an adaptive batch normalization algorithm (AdaBN) is introduced into the diagnosis model to enhance the adaptability to noise. Experimental results illustrate that the fault diagnosis model for rotating machinery based on one-dimensional deep residual shrinkage network with a wide convolution layer (1D-WDRSN) can accurately identify the fault classes even under noise interference.

Research on Fault Diagnosis Method of Wind Turbine Based on Wavelet Analysis and LS-SVM

2013 ◽  
Vol 724-725 ◽  
pp. 593-597 ◽  
Author(s):  
Chang Liang Liu ◽  
Wei Xue Qi
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Wind Turbine ◽  
High Speed ◽  
Wavelet Decomposition ◽  
Least Square ◽  
Support Vector ◽  
Vibration Signals ◽  
Training Samples ◽  
Diagnosis Method

Aiming at the fault characteristics of high-speed gearbox fault diagnosis of wind turbine, a fault diagnosis method of combining wavelet analysis with least square-support vector machine (LS-SVM) is proposed. According to the method, the energy of frequency bands generated by wavelet decomposition and reconstruction of the high-speed gearbox's vibration signals in different fault states is normalized as eigenvectors, forming training and testing samples of LS-SVM fault classifier. Train the LS-SVM fault diagnosis model with the training samples and test the accuracy with the testing samples. The result of research shows that the fault diagnosis method based on the wavelet analysis and LS-SVM has good diagnostics effect.

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.

Fault Detection and Discrimination of Rotating Machinery Using Frequency Symptom Parameter and Bayesian Network

2013 ◽  
Vol 470 ◽  
pp. 683-688
Author(s):  
Hai Yang Jiang ◽  
Hua Qing Wang ◽  
Peng Chen
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Bayesian Network ◽  
Frequency Domain ◽  
Evaluation Method ◽  
Roller Bearing ◽  
Rotating Machinery ◽  
Discrimination Index ◽  
Vibration Signals ◽  
Diagnosis Method

This paper proposes a novel fault diagnosis method for rotating machinery based on symptom parameters and Bayesian Network. Non-dimensional symptom parameters in frequency domain calculated from vibration signals are defined for reflecting the features of vibration signals. In addition, sensitive evaluation method for selecting good non-dimensional symptom parameters using the method of discrimination index is also proposed for detecting and distinguishing faults in rotating machinery. Finally, the application example of diagnosis for a roller bearing by Bayesian Network is given. Diagnosis results show the methods proposed in this paper are effective.

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.

scholarly journals An SDP Characteristic Information Fusion-Based CNN Vibration Fault Diagnosis Method

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Xiaoxun Zhu ◽  
Jianhong Zhao ◽  
Dongnan Hou ◽  
Zhonghe Han
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Fault Identification ◽  
High Complexity ◽  
Test Rig ◽  
Rotor Vibration ◽  
Vibration Signals ◽  
Image Identification ◽  
Diagnosis Method

This study proposes a symmetrized dot pattern (SDP) characteristic information fusion-based convolutional neural network (CNN) fault diagnosis method to resolve issues of high complexity, nonlinearity, and instability in original rotor vibration signals. The method was used to conduct information fusion of real modal components of vibration signals and SDP image identification using CNN in order to achieve vibration fault diagnosis. Compared with other graphic processing methods, the proposed method more fully expressed the characteristics of different vibration signals and thus presented variations between different vibration states in a simpler and more intuitive way. The proposed method was experimentally investigated using simulation signals and rotor test-rig signals, and its validity and advancements were demonstrated using experimental analysis. By using CNN through deep learning to adaptively extract SDP characteristic information, vibration fault identification was ultimately realized.

A Rolling Bear Fault Diagnosis Method Based on Improved LMD and Order Analysis

2014 ◽  
Vol 530-531 ◽  
pp. 256-260
Author(s):  
Hui Juan Yuan ◽  
Jia Qi ◽  
Hong Mei Li ◽  
Jun Zhong Li ◽  
Xue Jiang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Amplitude Modulation ◽  
Frequency Modulation ◽  
Rolling Bearing ◽  
Analysis Method ◽  
Vibration Signals ◽  
Order Analysis ◽  
Diagnosis Method ◽  
Bearing Vibration

This document explains and demonstrates how to predict the fault point of rolling bear. Rolling bearing vibration signals are decomposed by the LMD method to get several single components including amplitude modulation and frequency modulation signals. Combing the order analysis method can get the fault point of rolling bear.

Fault Diagnosis Method for Network Control Systems Applying SVM Disturbance Compensation

2013 ◽  
Vol 427-429 ◽  
pp. 1799-1802
Author(s):  
Jiao Meng ◽  
Qi Hua Xu ◽  
Lei Han
Keyword(s):  
Fault Diagnosis ◽  
Estimation Error ◽  
State Observer ◽  
Network Control ◽  
Ideal Condition ◽  
Disturbance Compensation ◽  
Network Control Systems ◽  
Diagnosis Method ◽  
Short Time ◽  
Short Time Delay

According to a network control system--NCS with short time-delay and packet loss, an state observer is designed firstly in this paper to obtain a state estimation error equation which is equivalent to an asynchronous dynamical system having event incidence constraint. Secondly, SVM is used to identify interferences of the NCS. Finally, making the identification result as compensation term adding to the state observer can make the residual only represent fault term under ideal condition and increase the robustness of NCS for interference, which can improve the fault diagnosis precision. The simulation results prove that the designed observer can diagnose faults effective and the disturbance compensation based on SVM has attained the expected effect.

Applying Full Vector Spectrum for Electric Hoist Gearbox Fault Diagnosis

2013 ◽  
Vol 365-366 ◽  
pp. 725-728 ◽  
Author(s):  
Hao Li ◽  
Xin Min Dong ◽  
Wang Sheng Hao ◽  
Ai Guo Liu ◽  
Xian De Yin ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Decomposition ◽  
Vibration Signal ◽  
Noise Signal ◽  
Complete Characterization ◽  
Vibration Signals ◽  
Dual Channel ◽  
Reconstructed Signal ◽  
Feature Information ◽  
Full Vector

To resolve the noise and incomplete information of rotor vibration signals for electric hoist gearbox, the full vector spectrum technology is applied for information fusion and wavelet analysis. The full vector wavelet is used to de-noising process for the vibration signals, which extract the more complete characterization information of the noise signal. First, the dual-channel vibration signal wavelet decomposition and reconstruction, and then take advantage of the full vector spectrum technology fusion for the reconstructed signal. In this way, which ensure the integrity of the vibration signal feature information, more reliably reflect the vibration characteristics of the rotor, and improve the accuracy of fault diagnosis.

scholarly journals An Automatic Bearing Fault Diagnosis Method Based on Characteristics Frequency Ratio

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1519 ◽  
Author(s):  
Dengyun Wu ◽  
Jianwen Wang ◽  
Hong Wang ◽  
Hongxing Liu ◽  
Lin Lai ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Frequency Ratio ◽  
Characteristic Frequency ◽  
Automatic Monitoring ◽  
Ensemble Empirical Mode Decomposition ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Eemd Method

Bearing is a key component of satellite inertia actuators such as moment wheel assemblies (MWAs) and control moment gyros (CMGs), and its operating state is directly related to the performance and service life of satellites. However, because of the complexity of the vibration frequency components of satellite bearing assemblies and the small loading, normal running bearings normally present similar fault characteristics in long-term ground life experiments, which makes it difficult to judge the bearing fault status. This paper proposes an automatic fault diagnosis method for bearings based on a presented indicator called the characteristic frequency ratio. First, the vibration signals of various MWAs were picked up by the bearing vibration test. Then, the improved ensemble empirical mode decomposition (EEMD) method was introduced to demodulate the envelope of the bearing signals, and the fault characteristic frequencies of the vibration signals were acquired. Based on this, the characteristic frequency ratio for fault identification was defined, and a method for determining the threshold of fault judgment was further proposed. Finally, an automatic diagnosis process was proposed and verified by using different bearing fault data. The results show that the presented method is feasible and effective for automatic monitoring and diagnosis of bearing faults.

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