Fault Diagnosis Method for Marine Hydraulic System

ICLEM 2012 ◽  
2012 ◽  
Author(s):  
Changwei He ◽  
Dawei Liu
Keyword(s):  
Fault Diagnosis ◽  
Hydraulic System ◽  
Diagnosis Method

scholarly journals Hydraulic system fault diagnosis method based on a multi-feature fusion support vector machine

2019 ◽  
Vol 2019 (13) ◽  
pp. 215-218 ◽  
Author(s):  
Lihua Wang ◽  
Xiao-qiang Wu ◽  
Chunyou Zhang ◽  
Hongyan Shi
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Hydraulic System ◽  
Feature Fusion ◽  
Support Vector ◽  
Diagnosis Method

scholarly journals Symbolic Important Point Perceptually and Hidden Markov Model Based Hydraulic Pump Fault Diagnosis Method

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4460 ◽  
Author(s):  
Yunzhao Jia ◽  
Minqiang Xu ◽  
Rixin Wang
Keyword(s):  
Fault Diagnosis ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hydraulic System ◽  
Hidden Markov ◽  
Operating Conditions ◽  
Fault Classification ◽  
Hydraulic Pump ◽  
Diagnosis Method ◽  
Original Time

Hydraulic pump is a driving device of the hydraulic system, always working under harsh operating conditions, its fault diagnosis work is necessary for the smooth running of a hydraulic system. However, it is difficult to collect sufficient status information in practical operating processes. In order to achieve fault diagnosis with poor information, a novel fault diagnosis method that is the based on Symbolic Perceptually Important Point (SPIP) and Hidden Markov Model (HMM) is proposed. Perceptually important point technology is firstly imported into rotating machine fault diagnosis; it is applied to compress the original time-series into PIP series, which can depict the overall movement shape of original time series. The PIP series is transformed into symbolic series that will serve as feature series for HMM, Genetic Algorithm is used to optimize the symbolic space partition scheme. The Hidden Markov Model is then employed for fault classification. An experiment involves four operating conditions is applied to validate the proposed method. The results show that the fault classification accuracy of the proposed method reaches 99.625% when each testing sample only containing 250 points and the signal duration is 0.025 s. The proposed method could achieve good performance under poor information conditions.

The Design of Detect Equipment of the Self-Propelled Artillery Hydraulic System Based on Data Fusion Technology

2014 ◽  
Vol 940 ◽  
pp. 128-131
Author(s):  
Xue Bing Liao ◽  
Li Juan Huang ◽  
Lei Peng ◽  
Chang Hong Gong
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Hydraulic System ◽  
Sensor Data ◽  
State Testing ◽  
Technical Requirements ◽  
Knowledge Reasoning ◽  
Multi Sensor Data Fusion ◽  
Diagnosis Method ◽  
Using Data

According to the detecting technical requirements of the new self-propelled artillery hydraulic system, the design of critical hardware components is described in detail. Online state testing, technical evaluation and fault diagnosis of multi-model hydraulic self-propelled artillery system is realized by using data fusion techniques and using fault diagnosis method on knowledge reasoning of multi-sensor data fusion based. After the trial, the device has strong comprehensive, good versatility and high stability of the characteristics.

scholarly journals Fault Diagnosis Method of the Construction Machinery Hydraulic System Based on Artificial Intelligence Dynamic Monitoring

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xin Zhou ◽  
Xianqing Lei
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Fault Diagnosis ◽  
Hydraulic System ◽  
Principal Component ◽  
Component Analysis ◽  
Functional Principal Component Analysis ◽  
Dynamic Monitoring ◽  
Functional Principal Component ◽  
Diagnosis Method

This paper aims to study the fault diagnosis method of the mechanical hydraulic system based on artificial intelligence dynamic monitoring. According to the characteristics of functional principal component analysis (FPCA) and neural network in the fault diagnosis method in the feature extraction process, the fault diagnosis method combining functional principal component analysis and BP neural network is studied and it is applied to the fault of the coordinator hydraulic system diagnosis. This article mainly completed the following tasks: analyzing the structure and working principle of the mechanical hydraulic system, studying the failure mechanism and failure mode of the mechanical hydraulic system, summarizing the common failures of the hydraulic system and the individual failures of the mechanical hydraulic system, and establishing the mechanical hydraulic system. Description of failure mode and effects analysis (FMEA): then, a joint simulation model of the mechanical hydraulic system was established in ADAMS and AMESim, and the fault detection signal of the hydraulic system was determined and compared with the experimental data. At the same time, the simulation data of the cosimulation model were compared with the simulation data of the hydraulic model in MATLAB to further verify the correctness of the model. The functional principal component analysis is used to perform functional processing on sample data, feature parameters are extracted, and the BP neural network is used to train the mapping relationship between feature parameters and fault parameters. The consistency is verified, and the fault diagnosis method is finally completed. The experimental results show that the diagnostic accuracy rates are 0.9848 and 0.9927, respectively, the reliability is significantly improved, close to 100%, and the uncertainty is basically 0, which significantly improves the accuracy of fault diagnosis.

Fault Diagnosis Expert System Applied on Machinery Industry

2013 ◽  
Vol 347-350 ◽  
pp. 461-465
Author(s):  
Ji Ming Huang ◽  
Min Jie Chai ◽  
Rong Ying Zhang
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Expert Systems ◽  
Hydraulic System ◽  
Machinery Industry ◽  
Rotating Machine ◽  
Key Technologies ◽  
Diagnosis Method ◽  
Design Idea

This paper introduced the fault diagnosis technology and analyzed the shortcomings of traditional fault diagnosis method. Then simply introduced the intelligence fault diagnosis expert system. At last respectively introduced the design idea of three intelligence fault diagnosis expert systems, they respectively is used in rotating machine, mechanical maintenance and hydraulic system. At the same time the key technologies of each intelligence fault diagnosis expert system was respectively introduced.

Helicopter main reduction planetary gear fault diagnosis method based on SVDD

2020 ◽  
Vol 64 (1-4) ◽  
pp. 137-145
Author(s):  
Yubin Xia ◽  
Dakai Liang ◽  
Guo Zheng ◽  
Jingling Wang ◽  
Jie Zeng
Keyword(s):  
Fault Diagnosis ◽  
Planetary Gear ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Energy Characteristics ◽  
Gear Fault ◽  
Channel Information ◽  
Diagnosis Method

Aiming at the irregularity of the fault characteristics of the helicopter main reducer planetary gear, a fault diagnosis method based on support vector data description (SVDD) is proposed. The working condition of the helicopter is complex and changeable, and the fault characteristics of the planetary gear also show irregularity with the change of working conditions. It is impossible to diagnose the fault by the regularity of a single fault feature; so a method of SVDD based on Gaussian kernel function is used. By connecting the energy characteristics and fault characteristics of the helicopter main reducer running state signal and performing vector quantization, the planetary gear of the helicopter main reducer is characterized, and simultaneously couple the multi-channel information, which can accurately characterize the operational state of the planetary gear’s state.

Towards High Accuracy Fault Diagnosis of Digital Circuits

2004 ◽  
Author(s):  
Camelia Hora ◽  
Stefan Eichenberger
Keyword(s):  
Fault Diagnosis ◽  
Fault Location ◽  
Digital Circuits ◽  
High Accuracy ◽  
Manufacturing Processes ◽  
Diagnosis Method ◽  
Technology Trend

Abstract Due to the development of smaller and denser manufacturing processes most of the hardware localization techniques cannot keep up satisfactorily with the technology trend. There is an increased need in precise and accurate software based diagnosis tools to help identify the fault location. This paper describes the software based fault diagnosis method used within Philips, focusing on the features developed to increase its accuracy.

Fault Diagnosis Method of Mine Motor Based on Support Vector Machine

2019 ◽  
Vol 13 ◽  
Author(s):  
Yan Zhang ◽  
Ren Sheng
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Optimization Method ◽  
Support Vector ◽  
Optimal Parameters ◽  
Instantaneous Power ◽  
Practical Usefulness ◽  
Sample Data ◽  
Diagnosis Method ◽  
Fault Characteristic

Background: In order to improve the efficiency of fault treatment of mining motor, the method of model construction is used to construct the type of kernel function based on the principle of vector machine classification and the optimization method of parameters. Methodology: One-to-many algorithm is used to establish two kinds of support vector machine models for fault diagnosis of motor rotor of crusher. One of them is to obtain the optimal parameters C and g based on the input samples of the instantaneous power fault characteristic data of some motor rotors which have not been processed by rough sets. Patents on machine learning have also shows their practical usefulness in the selction of the feature for fault detection. Results: The results show that the instantaneous power fault feature extracted from the rotor of the crusher motor is obtained by the cross validation method of grid search k-weights (where k is 3) and the final data of the applied Gauss radial basis penalty parameter C and the nuclear parameter g are obtained. Conclusion: The model established by the optimal parameters is used to classify and diagnose the sample of instantaneous power fault characteristic measurement of motor rotor. Therefore, the classification accuracy of the sample data processed by rough set is higher.

Sign in / Sign up

Export Citation Format

Share Document