Intelligent diagnosis of petroleum equipment faults using a deep hybrid model
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Intelligent diagnosis of petroleum equipment faults using a deep hybrid model Rasim Alguliyev1 · Yadigar Imamverdiyev1 · Lyudmila Sukhostat1 Received: 19 December 2019 / Accepted: 10 April 2020 / Published online: 18 April 2020 © Springer Nature Switzerland AG 2020
Abstract 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. Keywords Vibration signal · Fault diagnostics · Electrical submersible pump · Classification · Deep neural network · Convolutional neural network
1 Introduction One of the most effective ways to artificially lift oil to the surface is to use the electric submersible pump (ESP) systems. ESPs are complex subsystems that support the lift of oil and gas to the surface on the shelf. Installation and possible disposal of ESP due to maintenance are expensive operations. The system must reliably work after it is deployed. Removing faulty equipment should be avoided. Thus, a thorough assessment of the reliability is important [1]. Moreover, deep-sea work makes real-time monitoring of the system virtually impossible. This need motivates a thorough inspection of the equipment in a special test environment [2, 3]. Before installation, the ESP system is
tested in the laboratory on large datasets. An intelligent diagnostic system helps professionals detect faults in equipment. The expert should be provided with supporting information about the quality of the system. Therefore, the decision of the intelligent diagnostic system should consider the expert’s opinion. The goal of this paper is to develop a reliable method for assessing the state of ESP using a deep hybrid model. The model combines the advantages of a deep neural network (DNN) and a convolutional neural network (CNN). The frequency- and time-domain features of vibration signals are c
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