Degradation Assessment of Reciprocating Seal Using Support Vector Regression

2019 ◽  
Author(s):  
Madhumitha Ramachandran ◽  
Jon Keegan ◽  
Zahed Siddique
Keyword(s):  
Support Vector Regression ◽  
Friction Force ◽  
Manufacturing Industry ◽  
Oil And Gas ◽  
Principal Component ◽  
Oil And Gas Industry ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Failure Data ◽  
Set Up

Abstract Reciprocating seal located directly on the rod/piston of a reciprocating equipment is used for preventing leakage and reducing wear between two parts that are in relative motion. Degradation assessment of reciprocating seal is extremely important in the manufacturing industry to avoid fatal breakdown of reciprocating equipment and machines. In this paper, we have proposed a data-driven prognostics approach using friction force to predict the degradation of reciprocating seal using Support Vector Regression. Statistical time domain features are extracted from friction force signal to reduce the complexity of raw data. Principal Component Analysis is used to fuse the relevant features and remove the redundant features from the process. Based on the selected features, a Support Vector Regression model is then built and trained for the prediction of seal degradation. A Grid search method is used to tune the hyperparameters in the SVR model. Run-to-failure data collected from an experimental test set-up is used to validate the proposed methodology. The study findings indicate that a small set of relevant features which can represent the pattern related to degradation is sufficient to have a high prediction accuracy. The seal tested for this study comes from oil and gas industry, but the proposed method can be implemented in any industry with reciprocating equipment and machines.

Stress evaluation in seven-wire strands based on singular value feature of ultrasonic guided waves

2021 ◽  
pp. 147592172110053
Author(s):  
Qian Ji ◽  
Li Jian-Bin ◽  
Liu Fan-Rui ◽  
Zhou Jian-Ting ◽  
Wang Xu
Keyword(s):  
Dispersion Curve ◽  
Support Vector Regression ◽  
Stress Level ◽  
Guided Waves ◽  
Guided Wave ◽  
Singular Value ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Various Boundary Conditions ◽  
Wave Methods

The seven-wire strands are the crucial components of prestressed structures, though their performance inevitably degrades with the passage of time. The ultrasonic guided wave methods have been intensely studied, owing to its tremendous potential for full-scale applications, among the existing nondestructive testing methods, for evaluating the stress status of strands. We have employed the theoretical and finite element methods to solve the dispersion curve of single wire and steel strands under various boundary conditions. Thereafter, the singular value decomposition was adopted to work with the simulated and experimental signals for extracting a feature vector that carries valuable stress status information. The effectiveness of the vector was verified by analyzing the relationship between the vector and the stress level. The vector was also used as an input to establish a support vector regression model. The accuracy of the model has been discussed for different sample sizes. The results show that the fundamental mode dispersion curve offset on the high-frequency part and cut-off frequency increases as the boundary constraints enhance. Simulated and experimental results have demonstrated the effectiveness and potential of the proposed support vector regression method for evaluating the stress level in the strands. This method performs well even at low stress levels and the reliability can be enhanced by adding more samples.

scholarly journals Hotspot Temperature Prediction of Dry-Type Transformers Based on Particle Filter Optimization with Support Vector Regression

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1320
Author(s):  
Yuanyuan Sun ◽  
Gongde Xu ◽  
Na Li ◽  
Kejun Li ◽  
Yongliang Liang ◽  
...  
Keyword(s):  
Particle Filter ◽  
Support Vector Regression ◽  
Heat Dissipation ◽  
Hot Spot ◽  
Load Capacity ◽  
Regression Method ◽  
Support Vector ◽  
Temperature Prediction ◽  
Support Vector Regression Model ◽  
Load Rate

Both poor cooling methods and complex heat dissipation lead to prominent asymmetry in transformer temperature distribution. Both the operating life and load capacity of a power transformer are closely related to the winding hotspot temperature. Realizing accurate prediction of the hotspot temperature of transformer windings is the key to effectively preventing thermal faults in transformers, thus ensuring the reliable operation of transformers and accurately predicting transformer operating lifetimes. In this paper, a hot spot temperature prediction method is proposed based on the transformer operating parameters through the particle filter optimization support vector regression model. Based on the monitored transformer temperature, load rate, transformer cooling type, and ambient temperature, the hotspot temperature of a dry-type transformer can be predicted by a support vector regression method. The hyperparameters of the support vector regression are dynamically optimized here according to the particle filter to improve the optimization accuracy. The validity and accuracy of the proposed method are verified by comparing the proposed method with a traditional support vector regression method based on the real operating data of a 35 kV dry-type transformer.

Using the AR–SVR–CPSO hybrid model to forecast vibration signals in a high-speed train transmission system

2018 ◽  
Vol 233 (7) ◽  
pp. 701-714
Author(s):  
Yumei Liu ◽  
Ningguo Qiao ◽  
Congcong Zhao ◽  
Jiaojiao Zhuang ◽  
Guangdong Tian
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Support Vector Regression ◽  
Hybrid Model ◽  
High Speed ◽  
Support Vector ◽  
High Speed Train ◽  
Support Vector Regression Model ◽  
Vibration Time ◽  
Auto Regression

Accurate vibration time series modeling can mine the internal law of data and provide valuable references for reliability assessment. To improve the prediction accuracy, this study proposes a hybrid model – called the AR–SVR–CPSO hybrid model – that combines the auto regression (AR) and support vector regression (SVR) models, with the weights optimized by the chaotic particle swarm optimization (CPSO) algorithm. First, the auto regression model with the difference method is employed to model the vibration time series. Second, the support vector regression model with the phase space reconstruction is constructed for predicting the vibration time series once more. Finally, the predictions of the AR and SVR models are weighted and summed together, with the weights being optimized by the CPSO. In addition, the data collected from the reliability test platform of high-speed train transmission systems and the “NASA prognostics data repository” are used to validate the hybrid model. The experimental results demonstrate that the hybrid model proposed in this study outperforms the traditional AR and SVR models.

scholarly journals Multiphase gas-flow model of an electrical submersible pump

2018 ◽  
Vol 73 ◽  
pp. 29
Author(s):  
Diana Marcela Martinez Ricardo ◽  
German Efrain Castañeda Jiménez ◽  
Janito Vaqueiro Ferreira ◽  
Pablo Siqueira Meirelles
Keyword(s):  
Gas Flow ◽  
Oil And Gas ◽  
Learning Algorithm ◽  
Estimation Error ◽  
Oil And Gas Industry ◽  
Support Vector ◽  
System Response ◽  
Submersible Pump ◽  
Two Phase ◽  
Electrical Submersible Pump

Various artificial lifting systems are used in the oil and gas industry. An example is the Electrical Submersible Pump (ESP). When the gas flow is high, ESPs usually fail prematurely because of a lack of information about the two-phase flow during pumping operations. Here, we develop models to estimate the gas flow in a two-phase mixture being pumped through an ESP. Using these models and experimental system response data, the pump operating point can be controlled. The models are based on nonparametric identification using a support vector machine learning algorithm. The learning machine’s hidden parameters are determined with a genetic algorithm. The results obtained with each model are validated and compared in terms of estimation error. The models are able to successfully identify the gas flow in the liquid-gas mixture transported by an ESP.

The Research of Fuzzy Weighted Twin Support Vector Regression in the MBR Simulation Prediction

2014 ◽  
Vol 556-562 ◽  
pp. 3648-3653 ◽  
Author(s):  
Chan Juan Ji ◽  
Chun Qing Li ◽  
Tao Wang
Keyword(s):  
Support Vector Regression ◽  
Membrane Fouling ◽  
Principal Component ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Negative Effects ◽  
Twin Support Vector Regression ◽  
Output Layer ◽  
Input Layer

This paper using the way of Support Vector Data Description (SVDD) and considering the tightness between the Membrane Bio-Reactor (MBR) samples, applies the Fuzzy Weighted Twin Support Vector Regression (FTSVR) to the MBR simulation prediction research. Firstly,adopt the principal component analysis (PCA) on membrane fouling factors to achieve dimension reduction and de-correlation, then put the PCA output layer as the input layer of FTSVR, flux as the output layer, eventually, the MBR Membrane Fouling Prediction Model is built. This method considers the different effects on the regression hyperplane of different MBR samples,and effectively eliminates the negative effects due to error even outliers in the process of MBR data measurement.

Sign in / Sign up

Export Citation Format

Share Document