Cracked modeling and vibration analysis of pipe with a part-through crack

Pipeline is an important transportation facility in the oil and gas industries. But cracks inevitably appear in the pipe body due to various factors. Mechanical analysis of cracked pipe structures based on local flexibility has received increased attention in the last three decades. However, few reports exist on the local flexibility of pipes with an arbitrary angled crack. In this paper, the general solution of the local flexibility equations of a pipe with a part through-crack subjected to axial force, shearing force, and bending moment is deduced with respect to an arbitrary angled crack. The proposed equations consider the influence of the crack orientation on the local flexibility coefficient. An adaptive Simpson method is used to calculate the local flexibility coefficients of a cracked pipe. The results of testing by Naniwadekar’s and Authors’ are used to validate the proposed method. The results demonstrate that the proposed method is accurate for calculating local flexibility and can be applied for vibration analysis in a pipe-like structure

Nonlinear dynamic response analysis of a cantilever beam with a breathing crack

The nonlinear dynamic characteristics of plane cracked beam subjected to a harmonic load at the tip are researched. A crack opens and closes during vibration that is simulated as a frictionless plane contact problem, and a finite element contact model for a cantilever cracked beam is established. The quarter-point element is used to describe the crack tip singularity. Based on the proposed finite element contact model, the influence of excitation frequency, crack depth and crack position on nonlinear dynamic characteristics are discussed in detail. Relative amplitude of frequency spectrum with respect to different factors is analyzed to realize sub-harmonics or super-harmonics of cracked beam. The research results demonstrate that there is obvious nonlinear dynamic behavior for plane beam with a fatigue crack, and the different feature of frequency spectrum can be used to identify the beam damage in structure. Meanwhile, the strategy of experimental validation to the FEM results is discussed

Performance analysis for subsea blind shear ram preventers subject to testing strategies

In a subsea blowout preventer system, a subsea blind shear ram preventer (BSRP) plays as a crucial safety barrier by cutting off the drill pipe and sealing the wellhead to prevent serious accidents. Testing and repairs of BSRPs are the main issues in operation and maintenance activities. It is important to assess BSRPs unavailability during proof and partial testing phases in order to ensure their safety functions. This paper presents a newly state-based approach for unavailability analysis by incorporating testing activities of BSRPs into multiphase Markov process. In the approach, states waiting for repair are taken into consideration. Analytical formulas for evaluation of time-dependent unavailability and average unavailability for BSRPs are developed. In addition, the non-periodic characteristics and effects of degradation are also taken into account in proof testing. The effects of testing errors and postponed repairs on the tendency of unavailability in partial testing phases are checked in the proposed models. Performance analyses for BSRPs configurations, scenarios and cases considered in the paper are carried out to demonstrate the application of the proposed models. Monte Carlo models for both proof and partial testing are developed and simulated. Different comparisons are performed for validation of the set of the derived formulations.acceptedVersion© 2017. This is the authors’ accepted and refereed manuscript to the article. LOCKED until 1.9.2019 due to copyright restrictions. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0

A Study on Pipeline Leakage Detection Using Bayesian Deep Learning

Pipelines act a key role in assuring the safe and economical transportation of oil and gas. However, pipelines are typically operated under harsh working conditions and are hence vulnerable to leakage owing to a variety of causes, such as corrosion, fatigue, and third-party interference. Moreover, the consequences of pipeline leakage may lead to serious production loss, environmental pollution, and even injuries. Therefore, an effective pipeline leakage detection method is of great importance to maintain the integrity of the oil and gas transportation systems. The objective of this paper is threefold. First, we will provide a comprehensive review of pipeline leakage detection methods involving various types of sensors and machine learning techniques in the oil and gas industry. Second, we will develop a Bayesian deep learning-based method to effectively identify the pipeline leakage and meanwhile characterize the predictive uncertainty. This can present distinct advantages against the Frequentist deep learning-based pipeline leakage detection methods, which would present over- confident predictions and lead to errors in the decision-making process. Third, we will demonstrate the proposed method using real-field pipeline monitoring data, and a comparative study between Bayesian deep learning and its Frequentist counter

Reliability modeling of subsea SISs partial testing subject to delayed restoration

Subsea oil and gas production has always involved the challenging task of determining the overall reliability of safeguarding systems, such as safety instrumented systems (SISs). Partial testing and delayed restoration of SISs are the main issues in operation and maintenance activities. This paper proposes a novel reliability-modeling methodology for subsea SISs subject to partial testing and delayed restoration. The proposed methodology incorporates an increasing failure rate in conjunction with dangerous undetected failures for the final elements. Approximation formulas for evaluating the average probability of failure on demand are derived for SISs in the low-demand operating mode. In addition, the effects of degradation are modeled by following Weibull rules in different subsequent partial testing intervals. In contrast to previous works, the present research accounts for delayed restoration after detecting failures and also considers the repair time for different scenarios. The proposed formulas are compared with the existing ones for partial verification. A case study on the shutdown valves of a subsea high-integrity pressure protection system is presented to illustrate the feasibility of the proposed methodology. It is also proven that the proposed approximation offers a robust opportunity for testing strategy optimization and performance improvement of SISs.acceptedVersion© 2019. This is the authors’ accepted and refereed manuscript to the article. Locked until 12.6.2021 due to copyright restrictions. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0

Virtual sensing for gearbox condition monitoring based on extreme learning machine

Gearbox, as a critical component to convert speed and torque to maintain machinery normal operation in the industrial processes, has been received and still needs considerable attentions to ensure its reliable operation. Direct sensing and indirect sensing techniques are widely used for gearbox condition monitoring and fault diagnosis, but both have Pros and Cons. To bridge their gaps and enhance the performance of early fault diagnosis, this paper presents a new virtual sensing technique based on extreme learning machine (ELM) for gearbox degradation status estimation. By fusing the features extracted from indirect sensing measurements (e.g. in-process vibration measurement), ELM based virtual sensing model could infer the gearbox condition which was usually directly indicated by the direct sensing measurements (e.g. offline oil debris mass (ODM)). Different state-of-the-art dimension reduction techniques have been investigated for feature selection and fusion including principal component analysis (PCA) and its kernel version, locality preserving projection (LPP) method. The effectiveness of the presented virtual sensing technique is experimentally validated by the sensing measurements from a spiral bevel gear test rig. The experimental results show that the estimated gearbox condition by the virtual sensing model based on ELM and kernel PCA well follows the trend of truth data and presents the better performance over the support vector regression based virtual sensing scheme

Damage modeling and simulation of vibrating pipe with part-through circumferential crack

A new finite element model is developed to perform vibration analysis of a cracked pipe. To formulate the method, the local flexibility coefficients of a part-through circumferential crack in a pipe that is subjected to axial force, shear force and bending moment are analytically derived using linear fracture mechanics. In particular, an adaptive Simpson method is utilized to carry out the numerical integration for calculating the flexibility coefficients. With the flexibility coefficients, a finite element model is established to study the vibration characteristics of the cracked pipe, with particular emphasis on the crack effect represented by change in natural frequency. As an illustrative application, the finite element model is utilized to identify a crack in a pipe by contour plots of frequency ratio as function of crack location and crack depth, with the crack location and depth identified accurately. The proposed method is effective in characterizing the vibration behavior of a pipe with a crack