Condition monitoring of multiphase pumps

Rohlfing, Gerhard

doi:10.1016/s0262-1762(10)70127-9

Cited by 2 publications

(2 citation statements)

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“…In the O&G industry CBM policies haven been applied to enforce safe and reliable production. [29][30][31][32][33][34][35][36][37] This is because the processes that lead to the final product (i.e. exploration, extraction of raw materials, refining, and transportation) use a large number of physical assets that require high level of reliability and whose health states can be monitored and analyzed through variables such as vibration, temperature, pressure, acoustic emissions, among others.…”

Section: Introductionmentioning

confidence: 99%

Deep learning health state prognostics of physical assets in the Oil and Gas industry

Barraza

Brauning

Pérez

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part O:

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Due to its capital-intensive nature, the Oil and Gas industry requires high operational standards to meet safety and environmental requirements, while maintaining economical returns. In this context, maintenance policies play a crucial role in the avoidance of unplanned downtimes and enhancement of productivity. In particular, Condition-Based Maintenance is an approach in which maintenance actions are performed depending on the assets’ health state that is evaluated through different kinds of sensors. In this paper, Deep Learning methods are explored and different models are proposed for health state prognostics of physical assets in two real-life cases from the Oil and Gas industry: a Natural Gas treatment plant in an offshore production platform where elevated levels of CO2 must be predicted, and a sea water injection pump for oil extraction stimulation, in which several degradation levels must be predicted. A general methodology for preprocessing the available multi-sensor data and developing proper models is proposed and apply in both case studies. In the first one, a LSTM autoencoder is developed, achieving precision values over 83.5% when predicting anomalous states up to 8 h ahead. In the second case study, a CNN-LSTM model is proposed for the pump’s health state prognostics 48 h ahead, achieving precision values above 99% for all possible pump health states.

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Section: Introductionmentioning

confidence: 99%

Deep learning health state prognostics of physical assets in the Oil and Gas industry

Barraza

Brauning

Pérez

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part O:

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“…The condition monitoring of pumps and their associated systems is an established application of CBM and is an existing area of research [2]. Rohlfing [3] provides three examples in the oil and gas industry where pump's CBM has been effectively implemented. Azadeh et al in [4] have developed a diagnostic mechanism for pump failures in which pump operating problems fall into two categories: (1) hydraulic problems that suggest the pump may fail to deliver liquid, deliver insufficient capacity, develop insufficient pressure, or lose its prime at starting and (2) mechanical problems that are characterised by the consumption of excessive power or development of mechanical difficulties at the seal chambers or bearings; in either case vibration, noise, or breakage may occur.…”

Section: Introductionmentioning

confidence: 99%

Petroleum Pumps’ Current and Vibration Signatures Analysis Using Wavelet Coherence Technique

Shnibha

Albarbar

2013

Advances in Acoustics and Vibration

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Vibration analysis is widely used for rotating machinery diagnostics; however measuring vibration of operational oil well pumps is not possible. The pump’s driver’s current signatures may provide condition-related information without the need for an access to the pump itself. This paper investigates the degree of relationship between the pump’s driver’s current signatures and its induced vibration. This relationship between the driver’s current signatures (DCS) and its vibration signatures (DVS) is studied by calculating magnitude-squared coherence and phase coherence parameters at a certain frequency band using continuous wavelet transform (CWT). The CWT coherence-based technique allows better analysis of temporal evolution of the frequency content of dynamic signals and areas in the time-frequency plane where the two signals exhibit common power or consistent phase behaviour indicating a relationship between the signals. This novel approach is validated by experimental data acquired from 3 kW petroleum pump’s driver. Both vibration and current signatures were acquired under different speed and load conditions. The outcomes of this research suggest the use of DCS analysis as reliable and inexpensive condition monitoring tool, which could be implemented for oil pumps, real-time monitoring associated with condition-based maintenance (CBM) program.

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Condition monitoring of multiphase pumps

Cited by 2 publications

References 0 publications

Deep learning health state prognostics of physical assets in the Oil and Gas industry

Deep learning health state prognostics of physical assets in the Oil and Gas industry

Petroleum Pumps’ Current and Vibration Signatures Analysis Using Wavelet Coherence Technique

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