Neighbors Class Solidarity Feature Selection for Fault Diagnosis of Brushless Generator Using Thermal Imaging

Mohammad‐Alikhani, Arta; Rahnama, Mehdi; Vahedi, Abolfazl

doi:10.1109/tim.2020.2972081

Cited by 22 publications

(7 citation statements)

References 18 publications

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“…Second, due to the coupled vibration of multiple components and complicated transmission paths, the collected vibration signals are usually corrupted by noise disturbances [18]. Infrared thermal images are increasingly used as sensory data for condition monitoring of electromechanical equipment [19][20][21]. Unlike vibration-based monitoring, the use of infrared thermal images has the advantages of being contactless, convenient, high precision, and wide coverage [18].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of a Rotor-Bearing System Under Variable Rotating Speeds Using Two-Stage Parameter Transfer and Infrared Thermal Images

Shao

Xia³

et al. 2021

IEEE Trans. Instrum. Meas.

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Current fault diagnosis methods for rotor-bearing system are mostly based on analyzing the vibration signals collected at steady rotating speeds. In those methods, the data collected under one operating condition cannot be accurately used for diagnosis under a different condition. Moreover, in vibration monitoring, installing the necessary sensors will affect the equipment structure and hence the vibration response itself. The present paper proposes a new method based on two-stage parameter transfer and infrared thermal images for fault diagnosis of rotor-bearing system under variable rotating speeds. The method of parameter transfer enables the use of data (or parameters) acquired under one operating condition (called the source domain) to be extended for use in a different operating condition (called the target domain). First, scaled exponential linear unit (SELU) and modified stochastic gradient descent (MSGD) are used to construct an enhanced convolutional neural network (ECNN). Second, a stacked convolutional auto-encoder (CAE) trained based on unlabeled source-domain thermal images is employed to initialize a source-domain ECNN. Third, model parameters from the pre-trained source-domain ECNN are transferred to the target-domain ECNN to adapt to the characteristics of the target domain. The collected thermal images for a rotor-bearing system under variable speeds are used to test the transfer diagnosis performance of the proposed method. The experimental results demonstrate the performance improvement and the advantages of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of a Rotor-Bearing System Under Variable Rotating Speeds Using Two-Stage Parameter Transfer and Infrared Thermal Images

Shao

Xia³

et al. 2021

IEEE Trans. Instrum. Meas.

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“…Extraction and selection of the measured data play a significant role in the performance of fault detection. Various methods are used to select and extract the most appropriate features from the measured signal for fault detection [5,[16][17][18][19][20]. Overall, the feature selection method can be categorized into wrapper methods, filter methods, and hybrid methods [17].…”

Section: Introductionmentioning

confidence: 99%

“…Various methods are used to select and extract the most appropriate features from the measured signal for fault detection [5,[16][17][18][19][20]. Overall, the feature selection method can be categorized into wrapper methods, filter methods, and hybrid methods [17]. In wrapper methods, features are selected according to the performance of the model.…”

Section: Introductionmentioning

confidence: 99%

Induction Motor Fault Detection in ESP Systems Based on Vibration Measurements

Mohammad‐Alikhani

Vahedi

Khlyupin

2021

Electromechanics and Robotics

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Condition monitoring of electrical submersible pumps (ESPs) is vital since they work in a harsh and confined environment. One of the most important parts of ESP systems is the electric motor which is conventionally the induction motor. Therefore, fast and accurate diagnosis of the motor in the ESP system can cut overhaul expenses and prevent further damages and costs. In ESPs, the vibration of the system is usually measured using the downhole sensors. Accordingly, this paper aims to propose a method for bearing fault detection in the induction motor based on vibration measurements. In this method, first, the best features of the vibration signals are selected using a wrapper approach based on the genetic algorithm. Then, the model is trained using the selected features based on k nearest neighbor classifier. The proposed method is evaluated using data of an experimental set from Case Western Reserve University (CWRU) Bearing Data Center.

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“…In addition, in some articles, to select the most appropriate features for fault detection, feature selection methods are developed. In Mohammad-Alikhani et al (2020), a novel feature selection is proposed called neighbor class solidarity as a filter feature selection method to select the best wavelet parameters of thermal images. In Biet (2013), Fisher criterion, and the sequential backward selection algorithm is used to choose the most relevant features among the complete set of features.…”

Section: Introductionmentioning

confidence: 99%

“…In Yang et al (2015), multiple class feature selection is proposed to select the best features for diagnosis of an induction motor. In Rahnama et al (2019), Mohammad-Alikhani et al (2020), wrapper-based feature selection methods are applied to the features for fault detection of the brushless synchronous generator. In this paper as well, a novel filter-based feature selection method is proposed based on Relief algorithm to select the best features among all features.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of brushless synchronous generator using numerical modeling

Rahnama

Vahedi

Mohammad‐Alikhani

et al. 2020

COMPEL

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Purpose On-time fault diagnosis in electrical machines is a critical issue, as it can prevent the development of fault and also reduce the repairing time and cost. In brushless synchronous generators, the significance of the fault diagnosis is even more because they are widely used to generate electrical power all around the world. Therefore, this study aims to propose a fault detection approach for the brushless synchronous generator. In this approach, a novel extension of Relief feature selection method is developed. Design/methodology/approach In this paper, by taking the advantages of the finite element method (FEM), a brushless synchronous machine is modeled to evaluate the machine performance under two conditions. These conditions include the normal condition of the machine and one diode open-circuit of the rotating rectifier. Therefore, the harmonic behavior of the terminal voltage of the machine is obtained under these situations. Then, the harmonic components are ranked by using the extension of Relief to extract the most appropriate components for fault detection. Therefore, a fault detection approach is proposed based on the ranked harmonic components and support vector machine classifier. Findings The proposed diagnosis approach is verified by using an experimental test. Results show that by this approach open-circuit fault on the diode rectifier can effectively be detected by the accuracy of 98.5% and by using five harmonic components of the terminal voltage [1]. Originality/value In this paper, a novel feature selection method is proposed to select the most effective FFT components based on an extension of Relief method, and besides, FEM modeling of a brushless synchronous generator for normal and one diode open-circuit fault.

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Neighbors Class Solidarity Feature Selection for Fault Diagnosis of Brushless Generator Using Thermal Imaging

Cited by 22 publications

References 18 publications

Fault Diagnosis of a Rotor-Bearing System Under Variable Rotating Speeds Using Two-Stage Parameter Transfer and Infrared Thermal Images

Fault Diagnosis of a Rotor-Bearing System Under Variable Rotating Speeds Using Two-Stage Parameter Transfer and Infrared Thermal Images

Induction Motor Fault Detection in ESP Systems Based on Vibration Measurements

Diagnosis of brushless synchronous generator using numerical modeling

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