A Domain-Specific Language for Fault Diagnosis in Electrical Submersible Pumps

Monjardim, Gustavo Epichin; Rodrigues, Alexandre; Varejão, Flávio Miguel; Souza, Vítor E. Silva; Ribeiro, Marcos Pellegrini

doi:10.1109/indin.2018.8471932

Cited by 3 publications

(1 citation statement)

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“…According to the 1st and 2nd harmonics of the shaft rotation frequency [43], the pump blade unbalances, and pump shaft misalignment can be detected. The presence of low-frequency noise in the vibration signal characterizes the presence of rubbing [44].…”

Section: Dataset Descriptionmentioning

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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Section: Dataset Descriptionmentioning

confidence: 99%

Intelligent diagnosis of petroleum equipment faults using a deep hybrid model

2020

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Research on Business-oriented Smart Grid Asset Information Modeling Technology

Zhao

She

et al. 2019

2019 IEEE 3rd International Electrical and Energy Conference (CIEEC)

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CRUDyLeaf: A DSL for Generating Spring Boot REST APIs from Entity CRUD Operations

Gómez

Rosero

Cortes-Verdin

2020

Cybernetics and Information Technologies

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Domain-Specific Languages (DSLs) are programming languages designed specifically to express solutions to problems in a particular domain. It is said they foster productivity and quality. In this work we describe CRUDyLeaf, a DSL focused on the generation of Spring Boot REST APIs from entity CRUD operations. Spring Boot is an open source Java-based framework used to implement the REST architecture style. It has gained popularity among developers mainly because it allows to build stand-alone and production ready software applications (avoiding the use of an application server). Through seven proposed stages (domain immersion, golden application implementation, syntax definition, meta model generation, code generator implementation, deployment, and refinement) we describe the development of this DSL. We also exemplify and evaluate the proposed DSL. Our findings suggest a yield automation rate of 32.1 LOC (Lines Of Code) for each LOC written in this DSL, among other observed benefits.

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A Domain-Specific Language for Fault Diagnosis in Electrical Submersible Pumps

Cited by 3 publications

References 4 publications

Intelligent diagnosis of petroleum equipment faults using a deep hybrid model

Intelligent diagnosis of petroleum equipment faults using a deep hybrid model

Research on Business-oriented Smart Grid Asset Information Modeling Technology

CRUDyLeaf: A DSL for Generating Spring Boot REST APIs from Entity CRUD Operations

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