Combining classifiers with decision templates for automatic fault diagnosis of electrical submersible pumps

Oliveira-Santos, Thiago; Rodrigues, Alexandre; Rocha, Victor F.; Rauber, Thomas W.; Varejão, Flávio Miguel; Ribeiro, Marcos Pellegrini

doi:10.3233/ica-180574

Cited by 12 publications

(7 citation statements)

References 41 publications

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“…The large number of published articles on MI–BCI tasks using EEG signals highlights the importance of the applicability of EEG signals in the BCI domain (Hwang et al, ; Ortiz‐Rosario and Adeli, ). Most EEG‐based BCI systems usually use a structured approach that includes three main steps: (a) preprocessing that may contain three suboperations of noise removal (NR; Çınar, S., and Acır, ; Mutanen et al, ), channel selection (CS; Ghaemi et al, ; Rathee et al, ), and data augmentation (Kalunga et al, 2015; Krell et al, 2018); (b) feature construction, that is choosing appropriate properties of signals, consists of two suboperations of feature extraction (Hsu, ; Zhang et al, ; Aghaei et al, ; Cai et al, ), and feature selection (Zhang et al, ; Lin et al, ; Ma et al, ); and (c) classification that is performed using an appropriate classifier such as support vector machine (Khedher et al, ; Dai and Cao, ; Direito et al, ), probabilistic neural networks (Adeli and Panakkat, ; Sankari and Adeli, ), enhanced probabilistic neural network (Ahmadlou and Adeli, ; Hirschauer et al, ; Fernandes et al, ), competitive probabilistic neural network (Zeinali and Story, ), the recently developed neural dynamics classification algorithm (Rafiei and Adeli, ), or a combination or ensemble of classifiers (Oliveira‐Santos et al ; Reyes et al ). It should be noted that the necessity of each aforementioned suboperation is usually determined by a BCI expert, which is not convenient in practice.…”

Section: Introductionmentioning

confidence: 99%

“…choosing appropriate properties of signals, consists of two suboperations of feature extraction (Hsu, 2015;Zhang et al, 2015;Aghaei et al, 2016;Cai et al, 2017), and feature selection Lin et al, 2017;Ma et al, 2018); and (c) classification that is performed using an appropriate classifier such as support vector machine (Khedher et al, 2017;Dai and Cao, 2017;Direito et al, 2017), probabilistic neural networks (Adeli and Panakkat, 2009;Sankari and Adeli, 2011), enhanced probabilistic neural network (Ahmadlou and Adeli, 2010;Hirschauer et al, 2015;Fernandes et al, 2016), competitive probabilistic neural network (Zeinali and Story, 2017), the recently developed neural dynamics classification algorithm (Rafiei and Adeli, 2017), or a combination or ensemble of classifiers (Oliveira-Santos et al 2018;Reyes et al 2018). It should be noted that the necessity of each aforementioned suboperation is usually determined by a BCI expert, which is not convenient in practice.…”

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confidence: 99%

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A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals

et al. 2019

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An important subfield of brain–computer interface is the classification of motor imagery (MI) signals where a presumed action, for example, imagining the hands' motions, is mentally simulated. The brain dynamics of MI is usually measured by electroencephalography (EEG) due to its noninvasiveness. The next generation of brain–computer interface systems can benefit from the generative deep learning (GDL) models by providing end‐to‐end (e2e) machine learning and increasing their accuracy. In this study, to exploit the e2e‐property of deep learning models, a novel GDL methodology is proposed where only minimal objective‐free preprocessing steps are needed. Furthermore, to deal with the complicated multi‐class MI–EEG signals, an innovative multilevel GDL‐based classifying scheme is proposed. The effectiveness of the proposed model and its robustness against noisy MI–EEG signals is evaluated using two different GDL models, that is, deep belief network and stacked sparse autoencoder in e2e manner. Experimental results demonstrate the effectiveness of the proposed methodology with improved accuracy compared with the widely used filter bank common spatial patterns algorithm.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals

et al. 2019

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“…A comparison of the proposed approach with the methods KNN [15], KNN + [15], KNN + FS [15], KNN + EFS [15], and the method, proposed by Oliveira-Santos et al [18], was also made. Table 7 shows the promising results of using the deep hybrid model for automatic diagnosis of ESP failures.…”

Section: Resultsmentioning

confidence: 99%

“…Rauber et al [17] compared the performance of ELM with and without kernel mapping with other classifiers. Also, three types of motor pump faults (shaft misalignment, pump blade unbalance, and mechanical rubbing) and faulty accelerometer sensors were evaluated [18].…”

Section: Related Workmentioning

confidence: 99%

Intelligent diagnosis of petroleum equipment faults using a deep hybrid model

2020

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Performance assessment and timely failure detection of the electric submersible pump can reduce operation costs and maintenance in the oil and gas field. Features of equipment malfunction are changes in vibration signals. Evaluation of vibrations based on accelerometer sensors can detect failures and allows assessment of system failures. This paper proposes a reliable deep learning-based method for electric submersible pump faults detection. The frequency, time and spectral information of the vibrational signal are considered as input to the deep hybrid model. The spectral information includes the spectrogram obtained using the short-time Fourier transform and the scalogram as a result of the continuous wavelet transform and provides a detailed study of the vibration signal. The proposed approach is compared with k-nearest neighbors, support vector machines, logistic regression, and random forest. The experimental evaluation shows that the proposed deep hybrid model is superior to these machine learning methods, and can automatically and simultaneously detect failures of the electric submersible pump according to the vibration signal that is generated during system operation. The proposed approach gives good results and can help an expert in automatic diagnostics of equipment and several complex technical systems.

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“…All the others can be considered derivations, adaptations, or a mix of them. Table 1 resumes these meta-algorithms used in [11][12][13].…”

Section: Literature Reviewmentioning

confidence: 99%

Empowering Predictive Maintenance: A Hybrid Method to Diagnose Abnormal Situations

et al. 2020

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Aerospace systems are composed of hundreds or thousands of components and complex subsystems which need an appropriate health monitoring capability to enable safe operation in various conditions. In terms of monitoring systems, it is possible to find a considerable number of state-of-the-art works in the literature related to ad-hoc solutions. Still, it is challenging to reuse them even with subtle differences in analogous subsystems or components. This paper proposes the Generic Anomaly Detection Hybridization Algorithm (GADHA) aiming to build a more reusable algorithm to support anomaly detection. The solution consists of analyzing different supervised machine learning classification algorithms combined in ensemble techniques, with a physical model when available, and two levels of a decision to estimate the current state of the monitored system. Finally, the proposed algorithm assures at least equal, or, more typically, better, overall accuracy in fault detection and isolation than the application of such algorithms alone, through few adaptations.

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Combining classifiers with decision templates for automatic fault diagnosis of electrical submersible pumps

Cited by 12 publications

References 41 publications

A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals

A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals

Intelligent diagnosis of petroleum equipment faults using a deep hybrid model

Empowering Predictive Maintenance: A Hybrid Method to Diagnose Abnormal Situations

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