scholarly journals Fault Detection of a Flow Control Valve Using Vibration Analysis and Support Vector Machine

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1062 ◽  
Author(s):  
Venkata ◽  
Rao
Keyword(s):  
Support Vector Machine ◽  
High Pressures ◽  
Control Valve ◽  
Processing Technique ◽  
Support Vector ◽  
Flow Control Valve ◽  
Control Loop ◽  
Signal Processing Technique ◽  
Flow Process ◽  
Vibration Data

A control valve plays a very significant role in the stable and efficient working of a control loop for any process. In a fluid flow process, the probability of failure of a control valve may increase for many reasons pertaining to a flow process such as high pressures at the inlet, different properties of the liquid flowing through the pipe, mechanical issue related to a control valve, ageing, etc. A method to detect faults in the valve can lead to better stability of the control loop. In the proposed work, a technique is developed to determine the fault in a pneumatic control valve by analyzing the vibration data at the outlet of the valve. The fault diagnosis of the valve is carried out by analyzing the change in vibration of the pipe due to the change in flow pattern induced by the control valve. The faults being considered are inflow and insufficient supply pressure faults. Vibration data obtained is processed using a signal processing technique like amplification, Fourier transform, etc. The support vector machine (SVM) algorithm is used to classify the vibration data into two classes, one normal and the other faulty. The designed algorithm is trained to identify faults and subjected to test with a practical setup; test results show an accuracy of 97%.

Experimental Study of Cyclic Synchronous Vibration of an Integrally Geared Centrifugal Compressor for Air Separation Application

2020 ◽  
Author(s):  
Zhusan Luo ◽  
Carl L. Schwarz ◽  
Fabio Martins ◽  
Anthony Rosati
Keyword(s):  
Experimental Study ◽  
Centrifugal Compressor ◽  
Processing Technique ◽  
Air Separation ◽  
Signal Processing Technique ◽  
Frequency Spectra ◽  
Vibration Data ◽  
Morton Effect ◽  
Operation Test ◽  
Effective Remedy

Abstract This paper presents an experimental study of high cyclic synchronous vibration observed on the top pinion of an integrally geared centrifugal compressor. This cyclic synchronous vibration was different from the previously reported Morton effect. In a typical cycle, the vibration began with a long quiet period, then took off and followed by settle-down. Sometimes, vibration peaked above a shutdown limit, which subsequently tripped the compressor and then the air separation plant. Frequency spectra showed this new cyclic vibration was dominated by synchronous vibration. To obtain reliable and meaningful phase information for the diagnosis, a new signal processing technique was developed to analyze the historic vibration data captured without a key phasor. An experimental study of this new rotordynamic phenomenon was conducted on the machine in operation. Test data showed the high cyclic synchronous vibration was closely related to Morton effect though it does not have a significant phase shift. An effective remedy measure was therefore taken, and the cyclic synchronous vibration was eliminated. Since then, this compressor has been running smoothly for 17 months. A possible mechanism of the cyclic vibration is discussed in this paper.

scholarly journals A Comprehensive Machine-Learning-Based Software Pipeline to Classify EEG Signals: A Case Study on PNES vs. Control Subjects

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1235
Author(s):  
Giuseppe Varone ◽  
Sara Gasparini ◽  
Edoardo Ferlazzo ◽  
Michele Ascoli ◽  
Giovanbattista Gaspare Tripodi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical History ◽  
Processing Technique ◽  
Support Vector ◽  
Eeg Signals ◽  
Signal Processing Technique ◽  
Software Pipeline ◽  
Linear Discriminant ◽  
Eyes Closed ◽  
Average Accuracy

The diagnosis of psychogenic nonepileptic seizures (PNES) by means of electroencephalography (EEG) is not a trivial task during clinical practice for neurologists. No clear PNES electrophysiological biomarker has yet been found, and the only tool available for diagnosis is video EEG monitoring with recording of a typical episode and clinical history of the subject. In this paper, a data-driven machine learning (ML) pipeline for classifying EEG segments (i.e., epochs) of PNES and healthy controls (CNT) is introduced. This software pipeline consists of a semiautomatic signal processing technique and a supervised ML classifier to aid clinical discriminative diagnosis of PNES by means of an EEG time series. In our ML pipeline, statistical features like the mean, standard deviation, kurtosis, and skewness are extracted in a power spectral density (PSD) map split up in five conventional EEG rhythms (delta, theta, alpha, beta, and the whole band, i.e., 1–32 Hz). Then, the feature vector is fed into three different supervised ML algorithms, namely, the support vector machine (SVM), linear discriminant analysis (LDA), and Bayesian network (BN), to perform EEG segment classification tasks for CNT vs. PNES. The performance of the pipeline algorithm was evaluated on a dataset of 20 EEG signals (10 PNES and 10 CNT) that was recorded in eyes-closed resting condition at the Regional Epilepsy Centre, Great Metropolitan Hospital of Reggio Calabria, University of Catanzaro, Italy. The experimental results showed that PNES vs. CNT discrimination tasks performed via the ML algorithm and validated with random split (RS) achieved an average accuracy of 0.97 ± 0.013 (RS-SVM), 0.99 ± 0.02 (RS-LDA), and 0.82 ± 0.109 (RS-BN). Meanwhile, with leave-one-out (LOO) validation, an average accuracy of 0.98 ± 0.0233 (LOO-SVM), 0.98 ± 0.124 (LOO-LDA), and 0.81 ± 0.109 (LOO-BN) was achieved. Our findings showed that BN was outperformed by SVM and LDA. The promising results of the proposed software pipeline suggest that it may be a valuable tool to support existing clinical diagnosis.

Optimization of support vector machine based multi-fault classification with evolutionary algorithms from time domain vibration data of gears

2013 ◽  
Vol 227 (11) ◽  
pp. 2428-2439 ◽  
Author(s):  
DJ Bordoloi ◽  
Rajiv Tiwari
Keyword(s):  
Support Vector Machine ◽  
Time Domain ◽  
Angular Speed ◽  
Fault Classification ◽  
Support Vector ◽  
Vibration Data ◽  
Bee Colony ◽  
Time Domain Data ◽  
Grid Search Method

In the present work, a multi-fault classification of gears has been attempted by the support vector machine learning technique using the vibration data in time domain. A proper utilization of the support vector machine is based on the selection of support vector machine parameters. The main focus of this article is to examine the performance of the multiclass ability of support vector machine techniques by optimizing its parameters using the grid-search method, genetic algorithm and artificial bee colony algorithm. Four fault conditions were considered. A group of statistical features were extracted from time domain data. The prediction of fault classification is attempted at the same angular speed as the measured data as well as innovatively at the intermediate and extrapolated angular speed conditions. This is due to the fact that it is not feasible to have measurement of vibration data at all continuous speeds of interest. The classification ability is noted and it shows an excellent prediction performance.

scholarly journals Fault Diagnosis System of Induction Motors Based on Multiscale Entropy and Support Vector Machine with Mutual Information Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Shuang Pan ◽  
Tian Han ◽  
Andy C. C. Tan ◽  
Tian Ran Lin
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Mutual Information ◽  
Induction Motors ◽  
Sample Entropy ◽  
Multiscale Entropy ◽  
Support Vector ◽  
Vibration Signals ◽  
Vibration Data ◽  
Motor Vibration

An effective fault diagnosis method for induction motors is proposed in this paper to improve the reliability of motors using a combination of entropy feature extraction, mutual information, and support vector machine. Sample entropy and multiscale entropy are used to extract the desired entropy features from motor vibration signals. Sample entropy is used to estimate the complexity of the original time series while multiscale entropy is employed to measure the complexity of time series in different scales. The entropy features are directly extracted from the nonlinear, nonstationary induction motor vibration signals which are then sorted by using mutual information so that the elements in the feature vector are ranked according to their importance and relevant to the faults. The first five most important features are selected from the feature vectors and classified using support vector machine. The proposed method is then employed to analyze the vibration data acquired from a motor fault simulator test rig. The classification results confirm that the proposed method can effectively diagnose various motor faults with reasonable good accuracy. It is also shown that the proposed method can provide an effective and accurate fault diagnosis for various induction motor faults using only vibration data.

Diagnostics of Fatigue Crack in the Shaft Using Spectral Kurtosis

2019 ◽  
Author(s):  
Sagi Rathna Prasad ◽  
A. S. Sekhar
Keyword(s):  
Fatigue Crack ◽  
Crack Detection ◽  
Fatigue Cracks ◽  
Processing Technique ◽  
Rotating Machinery ◽  
Operating Conditions ◽  
Laser Vibrometer ◽  
Signal Processing Technique ◽  
Spectral Kurtosis ◽  
Vibration Data

Abstract Rotating machinery components like shafts subjected to continuous fluctuating loads are prone to fatigue cracks. Fatigue cracks are severe threat to the integrity of rotating machinery. Therefore it is indispensable for early diagnostics of fatigue cracks in shaft to avoid catastrophic failures. From the literature, it is evident that the spectral kurtosis (SK) and fast kurtogram were used to detect the faults in bearings and gears. The present study illustrates the use of SK and fast kurtogram for early fatigue crack detection in the shaft using vibration data. To perform this study, experiments are conducted on a rotor test rig which is designed and developed according to the function specification proposed by ASTM E468-11 standard. Fatigue crack is developed, on three shaft specimens, each seeded with the same circumferential V-Notch configuration, by continuous application of stochastic loads on the shaft using electrodynamic shaker in addition to the unbalance forces that arise in normal operating conditions. Vibration data is acquired from various locations of the rotor, using different sensors like miniature accelerometers, laser vibrometer and wireless telemetry strain gauge, till the shaft specimen develops fatigue crack. The analysis results show that the combination of SK and fast kurtogram is an effective signal processing technique for detecting the fatigue crack in the shaft.

Ensemble empirical mode decomposition-entropy and feature selection for pantograph fault diagnosis

2020 ◽  
Vol 26 (23-24) ◽  
pp. 2230-2242
Author(s):  
Ying Shi ◽  
Cai Yi ◽  
Jianhui Lin ◽  
Zhe Zhuang ◽  
Senhua Lai
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Approximate Entropy ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Effective Measure ◽  
Vibration Data ◽  
Ranking Criteria ◽  
Mode Decomposition

In this article, a fault diagnosis approach for a pantograph is developed with collected vibration data from a test rig. Ensemble empirical mode decomposition is used to decompose the signals to get intrinsic mode function, and four kinds of entropies (permu1tation entropy, approximate entropy, sample entropy, and fuzzy entropy) reflecting the working state are extracted as the inputs of the support vector machine based on particle swarm optimization algorithm support vector machine. The effect of data length, embedded dimension, and other parameters on calculation of the entropy value has also been studied. Multiple feature ranking criteria are used to select the useful features and improve the fault diagnosis accuracy of certain measurement points. Experimental results on pantograph vibration analysis have then confirmed that the proposed method provides an effective measure for pantograph diagnosis.

scholarly journals Twin Support Vector Machine Method for Identification of Wiener Models

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Mujahed Al-Dhaifallah
Keyword(s):  
Support Vector Machine ◽  
Control Valve ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Twin Support Vector Regression ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Wiener System ◽  
In Series ◽  
Ph Neutralization

Twin support vector regression is applied to identify nonlinear Wiener system, consisting of a linear dynamic block in series with static nonlinearity. The linear block is expanded in terms of basis functions, such as Laguerre or Kautz filters, and the static nonlinear block is determined using twin support vector machine regression. Simulation of a control valve model and pH neutralization process have been presented to show the features of the proposed algorithm over support vector machine based algorithm.

Vibration Gear Fault Diagnostics Technique Using Wavelet Support Vector Machine

2014 ◽  
Vol 564 ◽  
pp. 182-188
Author(s):  
Achmad Widodo ◽  
D.P. Dewi Widowati ◽  
D. Satrijo ◽  
I. Haryanto
Keyword(s):  
Support Vector Machine ◽  
Input Data ◽  
Feature Space ◽  
Testing Procedure ◽  
Haar Wavelet ◽  
Support Vector ◽  
Fault Diagnostics ◽  
Test Rig ◽  
Vibration Data ◽  
Gear Fault

Intelligent diagnostics tool for detecting damaged bevel gears was developed based on wavelet support vector machine (WSVM). In this technique, the existing method of SVM was modified by introducing Haar wavelet function as kernel for mapping input data into feature space. The developed method was experimentally evaluated by vibration data measured from test rig machinery fault simulator (MFS). There were four conditions of gears namely normal, worn, teeth defect and one missing-teeth which has been experimented. Statistical features were then calculated from vibration signals and they were employed as input data for training WSVM. Fault diagnostics of bevel gear was performed by executing classification task in trained WSVM. The accuracy of fault diagnostics were evaluated by testing procedure through vibration data acquired from test rig. The results show that the proposed system gives plausible performance in fault diagnostics based on experimental work.

Condition prediction based on wavelet packet transform and least squares support vector machine methods

2009 ◽  
Vol 223 (2) ◽  
pp. 71-79 ◽  
Author(s):  
F Zhao ◽  
J Chen ◽  
W Xu
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Support Vector ◽  
Vibration Data ◽  
Simulation And Experiment

Owing to the importance of condition maintenance, it is urgently required to predict condition in order to avoid unexpected failure. This article presents a new comprehensive prognostic approach for condition prediction based on wavelet packet transform and least squares support vector machine (LS-SVM). Comparision with traditional LS-SVM is also done to show its advantages. Simulation and experiment have been conducted to test the method. In the experiment, vibration data that were collected from the equipment is used to predict condition.

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