Fault Diagnosis and Analysis for Marine Planetary Gearbox

2009 ◽  
Vol 419-420 ◽  
pp. 149-152 ◽  
Author(s):  
Li Dong Jiang ◽  
Shan Chang ◽  
Guang Hao Dai ◽  
Zhen Rong Zhu
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Engineering Application ◽  
Vibration Signal ◽  
Frequency Domain Analysis ◽  
Load Test ◽  
Test Rig ◽  
Theory Analysis ◽  
Planetary Gearbox ◽  
The Time Domain

The abnormal noise was found in a marine planetary gearbox during the experiment. Then, the load test of the gearbox was done on a gearbox test rig and the vibration signal was measured and collected. The fault of the gearbox was analyzed by the time domain and frequency domain analysis. The trouble part was diagnosed and treated. The method used in this paper combined the theory analysis with engineering application. Simultaneously, it has provided a properly feasible method and valuable reference for the fault diagnosis of planetary gearbox.

scholarly journals Wavelet-Prototypical Network Based on Fusion of Time and Frequency Domain for Fault Diagnosis

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1483
Author(s):  
Yu Wang ◽  
Lei Chen ◽  
Yang Liu ◽  
Lipeng Gao
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Time Domain ◽  
Vibration Signal ◽  
Training Phase ◽  
Practical Applications ◽  
The Time Domain ◽  
Domain Information ◽  
Machine Learning Models ◽  
Better Than

Neural networks for fault diagnosis need enough samples for training, but in practical applications, there are often insufficient samples. In order to solve this problem, we propose a wavelet-prototypical network based on fusion of time and frequency domain (WPNF). The time domain and frequency domain information of the vibration signal can be sent to the model simultaneously to expand the characteristics of the data, a parallel two-channel convolutional structure is proposed to process the information of the signal. After that, a wavelet layer is designed to further extract features. Finally, a prototypical layer is applied to train this network. Experimental results show that the proposed method can accurately identify new classes that have never been used during the training phase when the number of samples in each class is very small, and it is far better than other traditional machine learning models in few-shot scenarios.

Vibration Test and Fault Diagnosis for Main Hoist Gearbox of Heavy Casting Crane

2014 ◽  
Vol 532 ◽  
pp. 374-377
Author(s):  
Zhang Li ◽  
Xing Dong Wang ◽  
Chang Yi Hu ◽  
Chi Zhong Chen ◽  
Li Ming
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Planetary Gear ◽  
Vibration Signal ◽  
Vibration Test ◽  
Signal Characteristic ◽  
Engineering Software ◽  
Fault Type ◽  
The Time Domain ◽  
Main Hoist

In view of the structure and running characteristics of gearbox of large and special crane, we have respectively carried out vibration test of fault and free-fault gearbox containing planetary gear in the work. With the help of Matlab engineering software, we can read and process the collected vibration signal of gearbox and draw the time-domain and frequency-domain graph. Through the comparative analysis of vibration information of gearboxes, we can determine the link between fault type and signal characteristic value, effectively realize the fault diagnosis of gearbox.

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 Fault Diagnosis of Planetary Gearbox Based on Motor Current Signal Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Ziyuan Jiang ◽  
Qinkai Han ◽  
Xueping Xu
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Time Domain ◽  
Signal Analysis ◽  
Current Signal ◽  
Learning Models ◽  
Planetary Gearbox ◽  
Motor Current ◽  
The Time Domain ◽  
Machine Learning Models

Planetary gearbox is one of the most widely used core parts in heavy machinery. Once it breaks down, it can lead to serious accidents and economic loss. Induction motor current signal analysis (MCSA) is a noninvasive method that uses current to detect faults. Currently, most MCSA-based fault diagnosis studies focus on the parallel shaft gearbox. However, there is a paucity of studies on the planetary gearbox. The effect of various signal processing methods on motor current and the performance of different machine learning models are rarely compared. Therefore, fault diagnosis of planetary gearbox based MCSA is conducted in this study. First, the effects of various faults on motor currents are studied. Specifically, the characteristic frequencies of a fault in sun/planet/ring gears and supporting bearings of the planetary gearbox are derived. Then, a signal preprocessing method, namely, singular spectrum analysis (SSA), is proposed to remove the supply frequency component in the current signal. Subsequently, four classical machine learning models, including the support vector machine (SVM), decision tree (DT), random forest (RF), and AdaBoost, are used for fault classifications based on the features extracted via principal component analysis (PCA). The convolutional neural network (CNN), which can automatically extract features, is also adopted. The dynamic experiment of the planetary gearbox with seven types of faults, including tooth chipping in sun/planet/ring gears, inner race spall in planet bearing, inner/outer races, and ball spalls in input support bearing, is conducted. Raw current signal in the time domain, reconstructed signal by SSA, and the current spectra in the frequency domain are used as the inputs of various models. The classification results show that the PCA-SVM is the best model for learned data while CNN is the best model for unlearned data on average. Furthermore, SSA mainly increases the accuracy of CNN in the time domain and exhibits a positive effect on unlearned data in the time domain. The classification accuracy increases significantly after transforming the time domain current data to the frequency domain.

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.

Research of Gear Fault Comprehensive Diagnosis Method Based on Analysis of Vibration Signal

2014 ◽  
Vol 898 ◽  
pp. 892-895
Author(s):  
Zhan Jie Lv ◽  
Wen Xu ◽  
Gui Ji Tang ◽  
Guo Dong Han ◽  
Shu Ting Wan
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
The Time Domain ◽  
Envelope Spectrum ◽  
Signal Processing Methods

For gearbox common type of fault, leads to common methods gear fault diagnosis, according to the various parameters of the gearbox, to give a gearbox fault frequencies. Using mat lab signal analysis, by the time domain analysis, frequency domain analysis, cestrum analysis, signal processing methods envelope spectrum consolidated results there is a fault in the gearbox countershaft. This papers they have certain significance to gear fault diagnosis.

Vibration Fault Diagnosis on Gearbox of Belt Conveyor in Coal Mine

2014 ◽  
Vol 543-547 ◽  
pp. 1145-1148 ◽  
Author(s):  
Shi Gang Zhu ◽  
Wei Jian Ding ◽  
Guang Hui Xue
Keyword(s):  
Fault Diagnosis ◽  
Power Spectrum ◽  
Coal Mine ◽  
Time Domain ◽  
Vibration Signal ◽  
Vibration Monitoring ◽  
Belt Conveyor ◽  
Underground Coal Mine ◽  
Input Shaft ◽  
The Time Domain

Belt conveyor gearbox is one of key equipments in coal mine which makes sure that the coal mine runs continuously and smoothly. Once it has faults, it will greatly influence the production and the benefits of coalmine. What is more, the raw coal cut by shearer could not be transported to the ground and serious accidents may occur. The authors carry out the vibration monitoring trials on belt conveyor gearbox in an underground coal mine using self-developed mining portable vibration recorder and obtain amount of on-site vibration signal. After analyzing the variation trend of the time domain indexes and power spectrum, we find that the gear of the input shaft of the reducer has severe wear and broken gears. Overhaul results verifies the correctness of the above analysis and the validity of the data sampled by the recorder.

A Peak Identification Method of Vibration Signal on Diesel Engine Cylinder Head in the Absence of TDC Signal

2010 ◽  
Vol 34-35 ◽  
pp. 296-300
Author(s):  
Zhao Qian Zhu ◽  
Wen Ming Zhang ◽  
Xiao Ming Zhang ◽  
Yu Peng Shi
Keyword(s):  
Fault Diagnosis ◽  
Diesel Engine ◽  
Time Domain ◽  
Cylinder Head ◽  
Vibration Signal ◽  
Computer Algorithms ◽  
Diagnosis System ◽  
Peak Identification ◽  
Engine Cylinder ◽  
The Time Domain

This paper studied the time-domain waveform characteristics of diesel engine cylinder head vibration. The relative relationship between the various explosions of diesel engine were analyzed and compared. Seven criteria were proposed under which to achieve the time domain waveform peak identification in the absence of TDC signal. According to the criteria computer algorithms for the time domain waveform peak identification were given. The method can reduce the vibration diagnosis system using request such as can achieve misfire fault diagnosis in the absence of TDC signal.

Separating Multiple Moving Sources by Microphone Array Signals for Wayside Acoustic Fault Diagnosis

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
Wei Xiong ◽  
Qingbo He ◽  
Zhike Peng
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Feature Matching ◽  
Matching Pursuit ◽  
Microphone Array ◽  
Time Frequency ◽  
Moving Sources ◽  
The Time Domain ◽  
Envelope Spectrum ◽  
Threshold Setting

Wayside acoustic defective bearing detector (ADBD) system is a potential technique in ensuring the safety of traveling vehicles. However, Doppler distortion and multiple moving sources aliasing in the acquired acoustic signals decrease the accuracy of defective bearing fault diagnosis. Currently, the method of constructing time-frequency (TF) masks for source separation was limited by an empirical threshold setting. To overcome this limitation, this study proposed a dynamic Doppler multisource separation model and constructed a time domain-separating matrix (TDSM) to realize multiple moving sources separation in the time domain. The TDSM was designed with two steps of (1) constructing separating curves and time domain remapping matrix (TDRM) and (2) remapping each element of separating curves to its corresponding time according to the TDRM. Both TDSM and TDRM were driven by geometrical and motion parameters, which would be estimated by Doppler feature matching pursuit (DFMP) algorithm. After gaining the source components from the observed signals, correlation operation was carried out to estimate source signals. Moreover, fault diagnosis could be carried out by envelope spectrum analysis. Compared with the method of constructing TF masks, the proposed strategy could avoid setting thresholds empirically. Finally, the effectiveness of the proposed technique was validated by simulation and experimental cases. Results indicated the potential of this method for improving the performance of the ADBD system.

scholarly journals A Multimodal Feature Fusion-Based Deep Learning Method for Online Fault Diagnosis of Rotating Machinery

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3521 ◽  
Author(s):  
Funa Zhou ◽  
Po Hu ◽  
Shuai Yang ◽  
Chenglin Wen
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Real Time ◽  
Time Domain ◽  
Feature Fusion ◽  
Rotating Machinery ◽  
Time Domain Data ◽  
Diagnosis Method ◽  
The Time Domain ◽  
Potential Frequency

Rotating machinery usually suffers from a type of fault, where the fault feature extracted in the frequency domain is significant, while the fault feature extracted in the time domain is insignificant. For this type of fault, a deep learning-based fault diagnosis method developed in the frequency domain can reach high accuracy performance without real-time performance, whereas a deep learning-based fault diagnosis method developed in the time domain obtains real-time diagnosis with lower diagnosis accuracy. In this paper, a multimodal feature fusion-based deep learning method for accurate and real-time online diagnosis of rotating machinery is proposed. The proposed method can directly extract the potential frequency of abnormal features involved in the time domain data. Firstly, multimodal features corresponding to the original data, the slope data, and the curvature data are firstly extracted by three separate deep neural networks. Then, a multimodal feature fusion is developed to obtain a new fused feature that can characterize the potential frequency feature involved in the time domain data. Lastly, the fused new feature is used as the input of the Softmax classifier to achieve a real-time online diagnosis result from the frequency-type fault data. A simulation experiment and a case study of the bearing fault diagnosis confirm the high efficiency of the method proposed in this paper.

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