Health Assessment of Hydraulic Servo System Based on Fractal Analysis and Gaussian Mixture Model

2015 ◽  
Vol 764-765 ◽  
pp. 703-707
Author(s):  
Xuan Wang ◽  
Hong Mei Liu ◽  
Chen Lu
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Fractal Analysis ◽  
Health Assessment ◽  
Servo System ◽  
Gaussian Mixture ◽  
Servo Systems ◽  
Feature Vectors ◽  
Hydraulic Servo ◽  
Hydraulic Servo System

A hydraulic servo system is a typical feedback control system. Health assessment of a hydraulic servo system is usually difficult to realize when traditional methods based on sensor signals are utilized. An approach for health assessment of hydraulic servo systems based on multi-fractal analysis and Gaussian mixture model (GMM) is proposed in this study. A GRNN neural network is employed to establish a fault observer for the hydraulic servo system. The observer is utilized to simulate the system output under normal state. The residue is then generated by subtracting the estimated output from the actual output. The residue’s feature is extracted by fractal analysis. After the feature extraction, the overlap between the current feature vectors and the normal feature vectors is obtained by applying GMM. The confidence value (CV) can be obtained in advance; this value is employed to characterize the health degree of the current state and consequently implement the health assessment of the hydraulic servo system. Lastly, two common types of fault, namely, burst and gradual, are applied to validate the effectiveness of the proposed method.

Slide - Mode Variable Structure Adaptive Control of Electro-Hydraulic Servo Systems Based on LuGre Friction Model

2014 ◽  
Vol 687-691 ◽  
pp. 371-374 ◽  
Author(s):  
Rui Juan Guo ◽  
Shuang Du ◽  
Hui Min Yin
Keyword(s):  
Sliding Mode ◽  
Servo System ◽  
Variable Structure ◽  
Friction Model ◽  
Interference Factor ◽  
Servo Systems ◽  
Hydraulic Servo ◽  
Hydraulic Servo System ◽  
Sliding Mode Variable Structure ◽  
Noise Factors

Electro-hydraulic servo system has more disturbance and noise factors at low speed than in other situations of the whole operating process, so the stability and performances of the system would be greatly affected by these factors. It is generally accepted that nonlinear torque disturbance is the main interference factor. In order to compensate above disadvantages, a sliding mode variable structure control was proposed to adjust the electro-hydraulic servo system. The results indicate that the provided approach possesses satisfactory performances in stability, robustness, steady state error and so on.

scholarly journals POLARIMETRIC SAR DATA GMM CLASSIFICATION BASED ON IMPROVED FREEMAN INCOHERENT DECOMPOSITION

2016 ◽  
Vol XLI-B7 ◽  
pp. 341-345
Author(s):  
S. Rouabah ◽  
M. Ouarzeddine ◽  
B. Azmedroub
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Urban Areas ◽  
Classification Problem ◽  
Gaussian Mixture ◽  
Polarimetric Sar ◽  
Data Set ◽  
Feature Vectors ◽  
Sar Data

Due to the increasing volume of available SAR Data, powerful classification processings are needed to interpret the images. GMM (Gaussian Mixture Model) is widely used to model distributions. In most applications, GMM algorithm is directly applied on raw SAR data, its disadvantage is that forest and urban areas are classified with the same label and gives problems in interpretation. In this paper, a combination between the improved Freeman decomposition and GMM classification is proposed. The improved Freeman decomposition powers are used as feature vectors for GMM classification. The E-SAR polarimetric image acquired over Oberpfaffenhofen in Germany is used as data set. The result shows that the proposed combination can solve the standard GMM classification problem.

scholarly journals GMM-UBM Based Modeling for Language Identification using New Feature Vectors

2020 ◽  
Vol 9 (4) ◽  
pp. 3034-3039
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Language Identification ◽  
Training Data ◽  
Background Model ◽  
Indian Languages ◽  
Feature Vectors ◽  
Universal Background Model ◽  
New Feature

The most of the existing LID systems based on the Gaussian Mixture model. The main requirement of the GMM based LID system is it require large amount of speech data to train the GMM model. Most of the Indian languages have the similarity because they are derived from Devanagari. Even though common phonemes exists in phoneme sets across the Indian languages, each language contain its unique phonotactic constraints imposed by the language. Any modeling technique capable of capturing all these slight variations imposed by the language is one of the important language identification cue. To model the GMM based LID system which captures above variations it require large number of mixture components.To model the large number of mixture components using Gaussian Mixture Model (GMM), the technique requires a large number of training data for each language class, which is very difficult to get for Indian languages. The main objective of GMM-UBM based LID system is it require less amount of training data to train(model) the system. In this paper, the importance of GMM-UBM modeling for language identification (LID) task for Indian languages are explored using new set of feature vectors. In GMM-UBM LID system based on the new feature vectors, the phonotactic variations imparted by different Indian languages are modeled using Gaussian Mixture model and Universal Background Model (GMM-UBM) technique. In this type of modeling, some amount of data from each class of language is pooled to create a universal background model. From this UBM model each model class is adapted. In this study, it is found that the performance of new feature vectors GMM-UBM based LID system is superior when compared to conventional new feature vectors based GMM LID system.

Local preserving projections-based feature selection and Gaussian mixture model for machine health assessment

2011 ◽  
Vol 225 (7) ◽  
pp. 1703-1717 ◽  
Author(s):  
J Yu ◽  
M Liu ◽  
H Wu
Keyword(s):  
Feature Selection ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Health Assessment ◽  
Principal Component ◽  
Gaussian Mixture ◽  
Health State ◽  
Structure Information ◽  
Machine Health ◽  
Low Dimensional

The sensitivity of various features that are characteristics of machine health may vary significantly under different working conditions. Thus, it is critical to devise a systematic feature selection (FS) approach that provides a useful and automatic guidance on choosing the most effective features for machine health assessment. This article proposes a locality preserving projections (LPP)-based FS approach. Different from principal component analysis (PCA) that aims to discover the global structure of the Euclidean space, LPP can find a good linear embedding that preserves local structure information. This may enable LPP to find more meaningful low-dimensional information hidden in the high-dimensional observations compared with PCA. The LPP-based FS approach is based on unsupervised learning technique, which does not need too much prior knowledge to improve its utility in real-world applications. The effectiveness of the proposed approach was evaluated experimentally on bearing test-beds. A novel machine health assessment indication, Gaussian mixture model-based Mahalanobis distance is proposed to provide a comprehensible indication for quantifying machine health state. The proposed approach has shown to provide the better performance with reduced feature inputs than using all original candidate features. The experimental results indicate its potential applications as an effective tool for machine health assessment.

Design of an Experimental Platform for Hydraulic Servo System

2011 ◽  
Vol 69 ◽  
pp. 51-54
Author(s):  
Jin Fang Zhu
Keyword(s):  
Hydraulic System ◽  
Servo System ◽  
Hydraulic Cylinder ◽  
Hydraulic Motor ◽  
Electronic Components ◽  
Servo Systems ◽  
Experimental Platform ◽  
Overall Design ◽  
Hydraulic Servo ◽  
Hydraulic Servo System

VRLA (valve-regulated lead-acid) and Pump-control are the two kinds of power components for hydraulic servo system. With different command device, feedback measurement device and different corresponding electronic components, the hydraulic servo systems are different. To ensure maximum performance of the whole device, the overall design (including mechanical, electrical design) should be considered for hydraulic servo system. Machinery-electric-hydraulic should be in coordination with each other. The hydraulic system components are used to change the speed of hydraulic cylinder and alter direction of hydraulic cylinder and hydraulic motor. The solenoid valve for motor and hydraulic servo system and the control of pressure relay can implement by the electric section.

scholarly journals Experimental evaluation of the parameter-based closed-loop transfer function identification for electro-hydraulic servo systems

2017 ◽  
Vol 9 (1) ◽  
pp. 168781401668442 ◽  
Author(s):  
Guang-Da Liu ◽  
Ge Li ◽  
Gang Shen
Keyword(s):  
Closed Loop ◽  
Transfer Functions ◽  
Servo System ◽  
Proportional Integral Derivative ◽  
Servo Systems ◽  
Closed Loop Systems ◽  
Loop Transfer Function ◽  
Tracking Controllers ◽  
Hydraulic Servo ◽  
Hydraulic Servo System

Closed-loop systems of an electro-hydraulic servo system including position, acceleration, and force closed-loop systems and their closed-loop transfer functions based on parameter model are adaptive identified using a recursive extended least-squares algorithm. The position and force closed-loop tracking controllers are designed by a proportional–integral–derivative controller and are tuned by the position and force step signals. The acceleration closed-loop tracking controller is designed by a three-variable controller and the three states include position, velocity, and acceleration. Experimental results of the estimated position, acceleration, and force closed-loop transfer functions are performed on an actual electro-hydraulic servo system using xPC rapid prototyping technology, which clearly demonstrate the benefit of the adaptive identification method.

HEALTH ASSESSMENT FOR HYDRAULIC SERVO SYSTEM USING MANIFOLD LEARNING BASED ON EMD

2015 ◽  
Vol 39 (3) ◽  
pp. 581-591 ◽  
Author(s):  
Yujie Cheng ◽  
Chen Lu ◽  
Jian Ma
Keyword(s):  
Manifold Learning ◽  
Rbf Neural Network ◽  
Health Assessment ◽  
Servo System ◽  
Singular Values ◽  
Assessment Method ◽  
Residual Error ◽  
Error Signal ◽  
Hydraulic Servo ◽  
Hydraulic Servo System

This study proposes a health assessment method for the hydraulic servo system using manifold learning based on empirical mode decomposition (EMD). An RBF neural network is adopted as a fault observer for the hydraulic servo system to generate a residual error signal. Then, the residual error signal is decomposed by EMD to form the initial feature matrix. To extract more sensitive features and reduce time consumption, isometric mapping algorithm is introduced to reduce the dimensionality of the initial feature matrix. Furthermore, the singular values of the reduced feature matrix are extracted for the subsequent health assessment. Considering the traditional Euclidean distance metric can only reflect local consistency, this study utilizes manifold distance (ManiD) to measure the health condition of the hydraulic servo system. Finally, the ManiD is converted into a confidence value, which directly represents the health status. Experiment results demonstrate the effectiveness of the proposed method.

Model Reference Adaptive Control on a Hydraulic Servo System

2011 ◽  
Vol 268-270 ◽  
pp. 505-508
Author(s):  
Zhi Yong Qu ◽  
Zheng Mao Ye
Keyword(s):  
Servo System ◽  
Adaptive Controller ◽  
Fast Response ◽  
System Stability ◽  
Servo Systems ◽  
Hydraulic Servo ◽  
Hydraulic Servo System ◽  
Simulation Results ◽  
The Stability ◽  
Model Reference Adaptive

Hydraulic servo systems are usually used in industry. This kind of system is nonlinear in nature and generally difficult to control. The ordinary linear constant gain controller can cause overshoot or even loss of system stability. Application of adaptive controller to a nonlinear hydraulic servo system is investigated in this paper. The dynamic model of the system is given and the stability is also analyzed using Popov's criterion. The steady state error can be eliminated using adaptive controller combined with an integration term. Simulation results show the performance of adaptive controller with fast response and less overshoot

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