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

Venkata, Santhosh Krishnan; Rao, Swetha

doi:10.3390/electronics8101062

Cited by 27 publications

(25 citation statements)

References 29 publications

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“…Some interesting works are discussed as follows. In [35], the windowed power spectral density of vibration signal is used with support vector machine (SVM) to detect the normal and faulty condition of rotatory control valve. In [36], a deep statistical feature set composed of time, frequency and time-frequency features is classified using Gaussian-Bernoulli deep Boltzmann machine.…”

Section: Literature Reviewmentioning

confidence: 99%

An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis

Kafeel¹,

Aziz

Awais

et al. 2021

Sensors

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Accurate and early detection of machine faults is an important step in the preventive maintenance of industrial enterprises. It is essential to avoid unexpected downtime as well as to ensure the reliability of equipment and safety of humans. In the case of rotating machines, significant information about machine’s health and condition is present in the spectrum of its vibration signal. This work proposes a fault detection system of rotating machines using vibration signal analysis. First, a dataset of 3-dimensional vibration signals is acquired from large induction motors representing healthy and faulty states. The signal conditioning is performed using empirical mode decomposition technique. Next, multi-domain feature extraction is done to obtain various combinations of most discriminant temporal and spectral features from the denoised signals. Finally, the classification step is performed with various kernel settings of multiple classifiers including support vector machines, K-nearest neighbors, decision tree and linear discriminant analysis. The classification results demonstrate that a hybrid combination of time and spectral features, classified using support vector machines with Gaussian kernel achieves the best performance with 98.2% accuracy, 96.6% sensitivity, 100% specificity and 1.8% error rate.

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Section: Literature Reviewmentioning

confidence: 99%

An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis

Kafeel¹,

Aziz

Awais

et al. 2021

Sensors

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“…Zeng and Hussein [12,13] conducted extensive research on the vibration mechanism of control valves and performed various simulation experiments. Venkata and Rao [14] extracted the power spectral density of the vibration signals to achieve accurate fault detection of control valves. Huang et al [15] applied complete ensemble empirical mode decomposition (EMD) with adaptive noise algorithm combined with fast Fourier transform to achieve control valve fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Improved DBO-VMD and optimized DBN-ELM based fault diagnosis for control valve

Zhang,

Han

et al. 2024

Meas. Sci. Technol.

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Control valves play a vital role in process production. In practical applications, control valves are prone to blockage and leakage faults. At the small control valve openings, the vibration signals exhibit the drawbacks of significant interference and weak fault characteristics, which causes subpar fault diagnosis performance. To address the issue, a diagnostic model based on optimized Variational Mode Decomposition (VMD) and improved Deep Belief Network-Extreme Learning Machine (DBN-ELM) is proposed. Firstly, good point set population initialization, nonlinear convergence factor, and adaptive Gaussian-Cauchy mutation strategies are applied in the dung beetle optimization algorithm (DBO) to escape local optima. Then, the improved DBO (IDBO) is used to optimize VMD parameters to obtain a series of modal components. Next, the generalized dispersion entropy (GDE) is formed by the combination of Generalized Gaussian Distribution and refined composite multiscale fluctuation-based dispersion entropy. The maximum correlation coefficient modal components are applied to extract generalized dispersion entropy. Finally, the IDBO is applied to optimize the parameters of the DBN-ELM network to improve the classification performance of control valve faults. The comparative experiment results demonstrate that the proposed model can extract effective features and the diagnostic accuracy reaches 99.87%.

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“…For structural health monitoring, scholars have proposed various diagnostic techniques and methods, including manual inspection method, vibration analysis method, 1 pressure drop detection method, ultrasonic monitoring method, 2,3 infrared thermal imaging technique, 4 and acoustic emission (AE) technique. 5 AE has become a research hotspot due to its sensitivity to leaked information.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive diagnosis method of valve leakage faults based on bi-sensor information fusion

Liu

Hao

et al. 2023

Structural Health Monitoring

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Acoustic emission (AE) signals caused by valve leakage exhibit obvious nonlinearity and nonstationarity characteristics. Due to the limitations of traditional valve leakage diagnosis methods, it is difficult to distinguish between internal and external valve leakage failures effectively. Recognizing this challenge, a comprehensive valve leakage diagnosis method based on a multichannel fusion convolutional neural network (MCFCNN) is proposed. First, AE signals are converted from one-dimensional time-domain signals to two-dimensional time–frequency images by the time–frequency analysis method. Then, the time–frequency images are used as model inputs, and MCFCNN fuses the features of time–frequency images from two different position. Hence, a new comprehensive diagnosis method for the bi-sensor fusion contains the time–frequency information, modal information, and position information of valve leakage is proposed. Subsequently, the effectiveness of the proposed method was verified through valve leakage simulation experiments. Furthermore, in order to study the impact of modal information on identifying internal and external valve leakage faults, the fault prediction performance of MCFCNN was compared and analyzed using short-time Fourier transform (STFT) and time-reassigned synchrosqueezing transform (TSST). Finally, according to the needs of engineering practice, the impact of sampling length on different methods is studied. The results show that compared to STFT–MCFCNN, TSST–MCFCNN required a shorter sampling length with the same diagnostic accuracy, which means that the method proposed in this study can achieve faster response time for ball valve leakage under conventional leakage flow rates.

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

Cited by 27 publications

References 29 publications

An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis

An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis

Improved DBO-VMD and optimized DBN-ELM based fault diagnosis for control valve

A comprehensive diagnosis method of valve leakage faults based on bi-sensor information fusion

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