Intelligent diagnosis of petroleum equipment faults using a deep hybrid model

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.

Neft-Qaz Sənayesi̇ Üçün Rəqəmsal Əki̇zləri̇n Konseptual Modeli̇

“…Vacib tətbiq istiqamətlərindən biri də netf-qaz sənayesi avadanlıqlarında imtinaların proqnozlaşdırılması və planlaşdırılmamış dayanmaların aradan qaldırılması üçün rəqəmsal texnologiyaların işlənməsidir [35]. Normal situasiyalarda qurğunun imtinasız iş müddətinin hətta kiçik yaxşılaşdırılması da gəlirlərin artırılmasında çox mühüm faktordur.…”