Quantitative Evaluation on Valve Leakage of Reciprocating Compressor Using System Characteristic Diagnosis Method

High impact and strong noise complicate the response of reciprocating compressor (RC). It requires a complex signal processing method that is a single response-based or excitation-based fault diagnosis method applied to RC valve leakage fault diagnosis. This paper proposes a quantitative diagnosis method of RC valve leakage that is based on system characteristic diagnosis method. First, the current signal of the RC induction motor and the cylinder vibration signal are introduced as the excitation and response signals, the mathematical model of the RC motor current is established, and the influence mechanism of the valve leakage on the RC vibration is analyzed. Subsequently, the ensemble empirical mode decomposition and comb filter are respectively used to extract the fault characteristic information of excitation signal and response signal to obtain the excitation condition indicators (CIs), response CIs, and system CIs. Finally, the support vector machine based on the obtained CIs classified the valve leakage failure patterns of different severity, and a fault diagnoser was constructed for the quantitative diagnosis of valve leakage fault. The results of experiment and application proved that the proposed method could realize the quantitative diagnosis of RC valve leakage fault while using simple signal processing technology.

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Fault Diagnosis Method Based on Modified Multiscale Entropy and Global Distance Evaluation for Valve Fault of Reciprocating Compressor

Strojniški vestnik – Journal of Mechanical Engineering ◽

10.5545/sv-jme.2018.5487 ◽

2019 ◽

Cited By ~ 1

Author(s):

Ying Li ◽

Jin Dong Wang ◽

Hai Yang Zhao ◽

Mei Ping Song ◽

Ling Fei Ou

Keyword(s):

Fault Diagnosis ◽

Large Scale ◽

Small Sample ◽

Coarse Grained ◽

Multiscale Entropy ◽

Support Vector ◽

Reciprocating Compressor ◽

Vibration Signals ◽

Fault Type ◽

Diagnosis Method

According to the nonlinearity, nonstationarity and multi-component coupling characteristics of reciprocating compressor vibration signals, a fault diagnosis method of reciprocating compressor valve based on the modified multiscale entropy (MMSE) and global distance evaluation (GDE) is proposed. Firstly, to eliminate the noise interference, the VMD method with superior anti-interference performance was utilized to a strong non-stationarity vibration signal for all fault states. To overcome the obstacle of the conventional multiscale entropy (MSE) may yield undefined entropy or an imprecise estimation of entropy by the large scale factor, the MMSE method provided for moving-average procedure by replacing mean-average coarse-grained procedure was developed to the vibration signals, and then the GDE method of overall parameter selection was introduced to evaluate the extracted MMSE and select the optimal sensitivity scale feature. Finally, binary tree of Support Vector Machine (BTSVM), suitable for small sample classification, was selected as the classifier to identify the reciprocating compressor valve fault type. By analyzing the experimental data, it can be shown that the method can effectively identify the fault type of reciprocating compressor valve.

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Helicopter main reduction planetary gear fault diagnosis method based on SVDD

International Journal of Applied Electromagnetics and Mechanics ◽

10.3233/jae-209316 ◽

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.

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Fault Diagnosis Method of Mine Motor Based on Support Vector Machine

Recent Patents on Engineering ◽

10.2174/1872212113666191121122720 ◽

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.

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Analog Circuit Fault Diagnosis Based on Support Vector Machine Classifier and Fuzzy Feature Selection

Electronics ◽

10.3390/electronics10121496 ◽

2021 ◽

Vol 10 (12) ◽

pp. 1496

Author(s):

Hao Liang ◽

Yiman Zhu ◽

Dongyang Zhang ◽

Le Chang ◽

Yuming Lu ◽

...

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Mutual Information ◽

Analog Circuit ◽

Fault Classification ◽

Support Vector ◽

Svm Classifier ◽

Fault Parameters ◽

Diagnosis Method ◽

Circuit Fault Diagnosis

In analog circuit, the component parameters have tolerances and the fault component parameters present a wide distribution, which brings obstacle to classification diagnosis. To tackle this problem, this article proposes a soft fault diagnosis method combining the improved barnacles mating optimizer(BMO) algorithm with the support vector machine (SVM) classifier, which can achieve the minimum redundancy and maximum relevance for feature dimension reduction with fuzzy mutual information. To be concrete, first, the improved barnacles mating optimizer algorithm is used to optimize the parameters for learning and classification. We adopt six test functions that are on three data sets from the University of California, Irvine (UCI) machine learning repository to test the performance of SVM classifier with five different optimization algorithms. The results show that the SVM classifier combined with the improved barnacles mating optimizer algorithm is characterized with high accuracy in classification. Second, fuzzy mutual information, enhanced minimum redundancy, and maximum relevance principle are applied to reduce the dimension of the feature vector. Finally, a circuit experiment is carried out to verify that the proposed method can achieve fault classification effectively when the fault parameters are both fixed and distributed. The accuracy of the proposed fault diagnosis method is 92.9% when the fault parameters are distributed, which is 1.8% higher than other classifiers on average. When the fault parameters are fixed, the accuracy rate is 99.07%, which is 0.7% higher than other classifiers on average.

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Fault Diagnosis Method of Low Noise Amplifier Based on Support Vector Machine and Hidden Markov Model

Journal of Electronic Testing ◽

10.1007/s10836-021-05938-0 ◽

2021 ◽

Author(s):

Lu Sun ◽

Yang Li ◽

Han Du ◽

Peipei Liang ◽

Fushun Nian

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Low Noise ◽

Low Noise Amplifier ◽

Support Vector ◽

Diagnosis Method ◽

Noise Amplifier

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A transformer fault diagnosis method based on sub-clustering reduction and multiclass multi-kernel support vector machine

2017 IEEE Conference on Energy Internet and Energy System Integration (EI2) ◽

10.1109/ei2.2017.8245354 ◽

2017 ◽

Cited By ~ 1

Author(s):

Jieshan Li ◽

Yonghu Zhu ◽

Yibin Xu ◽

Chuangxin Guo

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Support Vector ◽

Kernel Support Vector Machine ◽

Diagnosis Method ◽

Transformer Fault

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Research on Gyro Fault Diagnosis Method Based on Wavelet Packet Decomposition and Multi-class Least Squares Support Vector Machine

Recent Trends in Intelligent Computing, Communication and Devices - Advances in Intelligent Systems and Computing ◽

10.1007/978-981-13-9406-5_94 ◽

2019 ◽

pp. 789-797

Author(s):

Qiang Liu ◽

Jinjin Cheng ◽

Wenhao Guo

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Least Squares ◽

Wavelet Packet ◽

Support Vector ◽

Wavelet Packet Decomposition ◽

Diagnosis Method

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A Reliable Fault Diagnosis Method for a Gearbox System with Varying Rotational Speeds

Sensors ◽

10.3390/s20113105 ◽

2020 ◽

Vol 20 (11) ◽

pp. 3105 ◽

Cited By ~ 1

Author(s):

Cong Dai Nguyen ◽

Alexander Prosvirin ◽

Jong-Myon Kim

Keyword(s):

Fault Diagnosis ◽

Reference Signal ◽

Vibration Signal ◽

Support Vector ◽

Health States ◽

Informative Signal ◽

Vibration Signals ◽

Optimal Output ◽

Gear Fault ◽

Diagnosis Method

The vibration signals of gearbox gear fault signatures are informative components that can be used for gearbox fault diagnosis and early fault detection. However, the vibration signals are normally non-linear and non-stationary, and they contain background noise caused by data acquisition systems and the interference of other machine elements. Especially in conditions with varying rotational speeds, the informative components are blended with complex, unwanted components inside the vibration signal. Thus, to use the informative components from a vibration signal for gearbox fault diagnosis, the noise needs to be properly distilled from the informational signal as much as possible before analysis. This paper proposes a novel gearbox fault diagnosis method based on an adaptive noise reducer–based Gaussian reference signal (ANR-GRS) technique that can significantly reduce noise and improve classification from a one-against-one, multiclass support vector machine (OAOMCSVM) for the fault types of a gearbox. The ANR-GRS processes the shaft rotation speed to access and remove noise components in the narrowbands between two consecutive sideband frequencies along the frequency spectrum of a vibration signal, enabling the removal of enormous noise components with minimal distortion to the informative signal. The optimal output signal from the ANR-GRS is then extracted into many signal feature vectors to generate a qualified classification dataset. Finally, the OAOMCSVM classifies the health states of an experimental gearbox using the dataset of extracted features. The signal processing and classification paths are generated using the experimental testbed. The results indicate that the proposed method is reliable for fault diagnosis in a varying rotational speed gearbox system.

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A Novel Fault Diagnosis Method for Power Transformer Based on Dissolved Gas Analysis Using Hypersphere Multiclass Support Vector Machine and Improved D–S Evidence Theory

Energies ◽

10.3390/en12204017 ◽

2019 ◽

Vol 12 (20) ◽

pp. 4017 ◽

Cited By ~ 7

Author(s):

Haikun Shang ◽

Junyan Xu ◽

Zitao Zheng ◽

Bing Qi ◽

Liwei Zhang

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Evidence Theory ◽

Gas Analysis ◽

Power Transformers ◽

Support Vector ◽

Dissolved Gas ◽

Dissolved Gas Analysis ◽

Diagnosis Method ◽

Multiclass Support Vector Machine

Power transformers are important equipment in power systems and their reliability directly concerns the safety of power networks. Dissolved gas analysis (DGA) has shown great potential for detecting the incipient fault of oil-filled power transformers. In order to solve the misdiagnosis problems of traditional fault diagnosis approaches, a novel fault diagnosis method based on hypersphere multiclass support vector machine (HMSVM) and Dempster–Shafer (D–S) Evidence Theory (DET) is proposed. Firstly, proper gas dissolved in oil is selected as the fault characteristic of power transformers. Secondly, HMSVM is employed to diagnose transformer fault with selected characteristics. Then, particle swarm optimization (PSO) is utilized for parameter optimization. Finally, DET is introduced to fuse three different fault diagnosis methods together, including HMSVM, hybrid immune algorithm (HIA), and kernel extreme learning machine (KELM). To avoid the high conflict between different evidences, in this paper, a weight coefficient is introduced for the correction of fusion results. Results indicate that the fault diagnosis based on HMSVM has the highest probability to identify transformer faults among three artificial intelligent approaches. In addition, the improved D–S evidence theory (IDET) combines the advantages of each diagnosis method and promotes fault diagnosis accuracy.

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