scholarly journals An Underwater Thruster Fault Diagnosis Simulator and Thrust Calculation Method Based on Fault Clustering

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jian Yuan ◽  
Junhe Wan ◽  
Wenxia Zhang ◽  
Hailin Liu ◽  
Hao Zhang
Keyword(s):  
Fault Diagnosis ◽  
Experimental System ◽  
Experimental Device ◽  
Analysis Method ◽  
Diagnosis Method ◽  
Upper Computer ◽  
Monitoring Software ◽  
And Storage ◽  
Types Of Faults

In order to study the fault diagnosis method of small underwater thruster, an experimental device for fault diagnosis of underwater thruster is designed, and a controller hardware and monitoring software of upper computer and lower computer are developed to realize the acquisition and storage of parameters for underwater propeller. The experimental device can simulate four kinds of thruster faults, collect the hydrophone data, classify the fault types by fault clustering analysis, analyze the spectrum of four types of faults, and calculate the thrust under different fault conditions based on the results of spectrum analysis. The experimental results show that the experimental system effectively simulates different faults of the thruster, and the analysis method realizes the classification of different faults. The thrust loss of different faults is also calculated based on the analysis method.

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.

Fuzzy Diagnosis of Tapping Process Fault

2009 ◽  
Vol 626-627 ◽  
pp. 207-212 ◽  
Author(s):  
H.L. Xue ◽  
Le Wang ◽  
Xue Yong Chen ◽  
Gui Cheng Wang
Keyword(s):  
Experimental Study ◽  
Fault Diagnosis ◽  
Fuzzy Theory ◽  
Weight Matrix ◽  
Diagnosis System ◽  
Process Failure ◽  
Diagnosis Method ◽  
Fault Diagnosis System ◽  
Fuzzy Diagnosis ◽  
Types Of Faults

Based on analyzing the problem of tap worn and broken in the tapping process, the faults in tapping process are classified into four types: tap worn, chips jamming, uneven hardness of material and the tapping process failure; According to the fuzzy theory, this paper describes the torque characteristic of the four types of faults, ascertains the characteristic vector of fault, presents the weight matrix among faults, puts forward the judgment method of system faults and establishes the fuzzy fault diagnosis system in tapping process. The experimental study shows that the fuzzy diagnosis method can effectively identify the four types of faults in tapping process and guard against tap broken.

Research on the Time-Frequency Analysis Method to Extract Early Fault Features of Rotating Machinery

2012 ◽  
Vol 190-191 ◽  
pp. 1371-1375
Author(s):  
Ping Hua Ju ◽  
Gen Bao Zhang
Keyword(s):  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Mechanical Equipment ◽  
Analysis Method ◽  
Time Frequency ◽  
Fault Features ◽  
Common Diagnosis ◽  
Diagnosis Method ◽  
Weak Fault ◽  
Basic Features

Early fault features of rotating machinery is very weak and is disturbed by strong noise generally. how to more accurately extract early (weak) fault features from signals is still a hot and difficult point of research of the discipline. An intensive study is given to basic features of rotating machinery early faults and common diagnosis method, And also summarized the research status of early diagnosis in the field of mechanical equipment signal feature extraction and fault diagnosis, analyzed the current problems, and finally briefly pointed out the development of early fault diagnosis in machinery applications.

scholarly journals Research on Pump Product Intelligent Fault Diagnosis System Based on Dig Data

2021 ◽  
Vol 2121 (1) ◽  
pp. 012024
Author(s):  
Yimei Xu ◽  
Xiaoqing Xu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Fault Diagnosis ◽  
Manufacturing Industry ◽  
Big Data Analysis ◽  
Identification Accuracy ◽  
Analysis Method ◽  
Data Analysis Method ◽  
Diagnosis Method ◽  
Fault Diagnosis System

Abstract With the integration of information technology and manufacturing industry and the improvement of equipment monitoring data and computer computing ability, equipment fault diagnosis has entered the era of “big data”. Using big data analysis method for fault diagnosis, the fault diagnosis model can learn its own characteristics and complete fault identification, so that the fault diagnosis is more intelligent and automatic on the basis of high identification accuracy. This paper mainly studies the application of fault diagnosis method and big data analysis method in pump product fault diagnosis.

scholarly journals The Fault Diagnosis of Rolling Bearing Based on Improved Deep Forest

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xiwen Qin ◽  
Dingxin Xu ◽  
Xiaogang Dong ◽  
Xueteng Cui ◽  
Siqi Zhang
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Computer Hardware ◽  
Great Effort ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Deep Forest ◽  
Diagnosis Method ◽  
Feature Information ◽  
Types Of Faults

Rolling bearing fault diagnosis is a meaningful and challenging task. Most methods first extract statistical features and then carry out fault diagnosis. At present, the technology of intelligent identification of bearing mostly relies on deep neural network, which has high requirements for computer equipment and great effort in hyperparameter tuning. To address these issues, a rolling bearing fault diagnosis method based on the improved deep forest algorithm is proposed. Firstly, the fault feature information of rolling bearing is extracted through multigrained scanning, and then the fault diagnosis is carried out by cascade forest. Considering the fitting quality and diversity of the classifier, the classifier and the cascade strategy are updated. In order to verify the effectiveness of the proposed method, a comparison is made with the traditional machine learning method. The results suggest that the proposed method can identify different types of faults more accurately and robustly. At the same time, it has very few hyperparameters and very low requirements on computer hardware.

scholarly journals Compound Fault Diagnosis of Rolling Bearing Based on ALIF-KELM

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jie Ma ◽  
Shitong Liang ◽  
Zhengyu Du ◽  
Ming Chen
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Fault Classification ◽  
Training Set ◽  
Sample Data ◽  
Diagnosis Method ◽  
Main Fault ◽  
Bp Model ◽  
Fault Information

Aiming at the shortcomings of difficult classification of rolling bearing compound faults and low recognition accuracy, a composite fault diagnosis method of rolling bearing combined with ALIF and KELM is proposed. First, the basic concepts of ALIF and KELM are introduced, and then ALIF is used to decompose the sample data of vibration signals of different bearing states so that each sample can get several IMFs, select the top K IMFs containing the main fault information from each sample, calculate the energy feature and sample entropy of each IMF, and construct a fault feature vector with a dimension of 2K. Finally, the feature vectors of the training set and the test set are input into the KELM model for fault classification. Experimental results show that, compared with EMD-KELM model, ALIF-ELM model, ALIF-BP model, and IFD-KELM model, the rolling bearing composite fault diagnosis method based on the ALIF-KELM model has higher classification accuracy.

scholarly journals Fine-Grained Fault Diagnosis Method of Rolling Bearing Combining Multisynchrosqueezing Transform and Sparse Feature Coding Based on Dictionary Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Guodong Sun ◽  
Yuan Gao ◽  
Kai Lin ◽  
Ye Hu
Keyword(s):  
Fault Diagnosis ◽  
Dictionary Learning ◽  
Nonnegative Matrix ◽  
Rolling Bearing ◽  
Fault Classification ◽  
Support Vector ◽  
Time Frequency ◽  
Fine Grained ◽  
Diagnosis Method

To accurately diagnose fine-grained fault of rolling bearing, this paper proposed a new fault diagnosis method combining multisynchrosqueezing transform (MSST) and sparse feature coding based on dictionary learning (SFC-DL). Firstly, the high-resolution time-frequency images of raw vibration signals, including different kinds of fine-grained faults of rolling bearing, were constructed by MSST. Then, the basis dictionary was trained through nonnegative matrix factorization with sparseness constraints (NMFSC), and the trained basis dictionary was employed to extract features from time-frequency matrixes by using nonnegative linear equations. Finally, a linear support vector machine (LSVM) was trained with features of training samples, and the trained LSVM was employed to diagnosis the fault classification of test samples. Compared with state-of-the-art fault diagnosis methods, the proposed method, which was tested on the bearing dataset from Case Western Reserve University (CWRU), achieved the fine-grained classification of 10 mixed fault states. Meanwhile, the proposed method was applied on the dataset from the Machinery Failure Prevention Technology (MFPT) Society and realized the classification of 3 fault states under different working conditions. These results indicate that the proposed method has great robustness and could better meet the needs of practical engineering.

scholarly journals Compound Fault Diagnosis of Gearbox Based on RLMD and SSA-PNN

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Shitong Liang ◽  
Jie Ma
Keyword(s):  
Fault Diagnosis ◽  
Feature Vector ◽  
Search Algorithm ◽  
Probabilistic Neural Network ◽  
Permutation Entropy ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Local Mean ◽  
Fault Information

In order to solve the difficulty in the classification of gearbox compound faults, a gearbox fault diagnosis method based on the sparrow search algorithm (SSA) improved probabilistic neural network (PNN) is proposed. Firstly, the gearbox fault signal is decomposed into a series of product functions (PFs) by robust local mean decomposition (RLMD). Then, the permutation entropy of PFs, which contains much fault information, is calculated to construct the feature vector and input it into the SSA-PNN model. The experimental results show that compared with the traditional fault diagnosis methods based on EMD-BP and EEMD-PNN, the gearbox fault diagnosis method based on RLMD and SSA-PNN has higher diagnosis accuracy.

Improved Fault Diagnosis Method Based on Probabilistic Neural Network

2012 ◽  
Vol 433-440 ◽  
pp. 6084-6088 ◽  
Author(s):  
Gu Qing Liu ◽  
Shu Hua Yin ◽  
Xin Tian Wang ◽  
Yan Qing Sun
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Probabilistic Neural Network ◽  
Digital Data ◽  
Improved Method ◽  
Attribute Weights ◽  
Novel Approach ◽  
Diagnosis Method ◽  
Fault Diagnosis System

In order to enhancing the accuracy of fault diagnosis system, an improved method based on the probabilistic neural network (PNN) is proposed, in which the synthetic attribute weights of faults are introduced that are obtained by integrating algebra view and information theory view of rough set. The synthetic attribute weights are utilized to training the classical PNN and dealing with the classification of faults so as to improving the PNN model. The new model is more accurate and can represent expertise. This novel approach is applied in digital data network to diagnose failures, and the results of the experiment verify that the method is practical and effective in raising accuracy of diagnosis as well as avoiding misdirection in fault remedy.

Gear Fault Diagnosis with Support Vector Machine

2012 ◽  
Vol 455-456 ◽  
pp. 1169-1174 ◽  
Author(s):  
Jia Li Tang ◽  
Chen Rong Huang ◽  
Jian Min Zuo
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Experimental System ◽  
Support Vector ◽  
Signal Acquisition ◽  
Gear Fault ◽  
The Common ◽  
Gear Fault Diagnosis ◽  
Set Up ◽  
Types Of Faults

Because of the complexity of gear working condition, there are non-linear relationship between characteristic parameters and fault types. This paper proposes to apply the Support Vector Machine to set up the nonlinear mapping to solve the difficulties of gear fault diagnosis. Taking a certain gearbox fault signal acquisition experimental system for instance, Matlab software and its neural network toolbox are used to model and simulate. The simulation result shows the founded model has preferable learning and generalization capabilities, which performs effectively in the common gear fault diagnosis and it can identify various types of faults stably and accurately.

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