Failure analysis and mechanical performance of an oil pipeline

This paper presents a work scheme for failure analysis of buried oil pipelines. The scheme provides answers for two main questions at least: what were the causes that made the system fail and why the system failed in the way it did. The scheme is based on five stages and the results of these stages are related to each other and consistent with the type of failure, thereby providing objective conclusions. The proposed scheme was successfully applied to a case of a buried oil pipeline whose failure was a result of external corrosion. We established the occurrence of accelerated corrosion characterized by two corrosion mechanisms: one involving iron oxides and the other, iron sulfides. Furthermore, the ductile type failure has proved to be due to leakage, which is consistent with the reported evidence.     

A fuzzy knowledge based method for maintenance planning in a power system

The inspection planning in electric power industry is used to assess the safety and reliability of system components and to increase the ability of failure situation identification before it actually occurs. It reflects the implications of the available information on the operational and maintenance history of the system. The output is a ranked list of components, with the most critical ones at the top, which indicates the selection of the components to be inspected.In this paper, we demonstrate the use of a fuzzy relational database model for manipulating the data required for the criticality component ranking in thermal power systems inspection planning, incorporating criteria concerning aspects of safety and reliability, economy, variable operational conditions and environmental impacts. Often, qualitative thresholds and linguistic terms are used for the component criticality analysis. Fuzzy linguistic terms for criteria definitions along with fuzzy inference mechanisms allow the exploitation of the operators' expertise.The proposed database model ensures the representation and handling of the aforementioned fuzzy information and additionally offers to the user the functionality for specifying the precision degree by which the conditions involved in a query are satisfied.In order to illustrate the behavior of the model, a case study is given using real inspection data.     

基于Haar小波和模糊逻辑的纸币图像特征提取方法

针对如何提取纸币图像特征的问题,提出了一种基于离散Haar小波变换和模糊逻辑相结合的纸币特征提取方法。该方法首先使用Haar小波对纸币图像进行分解操作,提取出图像的低频小波系数、高频小波系数。在此基础上引入模糊逻辑方法,把提取的小波系数分别作为语言变量,并构造出相应的隶属度函数,在模糊特征空间中求出每个模糊区域对应的激活强度值,将这些激活强度值进行归一化处理后构成纸币特征向量,使用神经网络分类器对纸币进行识别。此方法在资源约束的嵌入式系统(TI TMS320C6713 DSP)上实现,实验结果表明,离散Haar小波变换和模糊逻辑相结合的特征提取方法可以取得较高的识别率。     

Reliability analysis of pipelines carrying H2S for risk based inspection of heavy water plants

Reliability analysis of pipelines containing corrosion defects due to H₂S is presented. The pipeline carrying H₂S is more susceptible to the internal corrosion thereby reducing the pipeline's load carrying capacity. The objective of this study is to obtain the pipelines' failure probabilities that are required in establishing a Risk Based Inspection (RBI) programme for heavy water plants. The reliability assessment of pipelines involves the estimation of failure pressure and evaluating the limit state function. Several failure pressure models were studied for this purpose and it was found that the modified B31G failure pressure model is most suitable for the pipeline failure pressure modeling. Due to the presence of non-linearity in the limit state function and non-normal variables, the first order reliability method has been employed for carrying out the reliability analysis. The uncertainty of the random variables on which the limit state function depends is modeled using normal and non-normal probabilistic distributions. The failure probabilities and the categories of the pipelines connected to the first pair of first stage of exchange towers are presented. In addition, a sensitivity analysis was carried out on random variables involved in the problem. The results of sensitivity analysis are also presented.     

Experimental analysis of differences in mechanical behaviour of cracked and notched specimens in a ferritic–pearlitic steel: Considerations about the notch effect on structural integrity

This paper analyses the experimental differences found between the mechanical behaviour of cracked and notched specimens. These differences are defined in terms of local strains, global strains, stresses and CTOD values during testing, and also through the correlations established between these variables. The experimental programme has been performed at a wide range of temperatures (from −100 °C to −20 °C), providing an outline of the notch effect throughout the transition region. The corresponding effects on structural integrity assessments (through failure assessment diagrams) are also analysed. From the results obtained, a clear notch effect has been defined in all the mentioned parameters and in the assessment itself for temperatures below the transition temperature (T < T₀) of the material. The effect is less pronounced or even negligible at higher temperatures (T > T₀).     

油气管道缺陷漏磁图像的小波压缩技术研究

针对漏磁信号的特点,提出了基于小波分析的缺陷漏磁图像压缩方案。该方案通过调整正交函数零极点位置来设计小波基函数,利用该函数对图像作小波变换,对获得的各级小波系数用更新的JPEG图像压缩表阈值量化,再用算术编码方案处理量化结果,从而得到压缩的图像。试验结果表明,当压缩比小于30%时,图像压缩引起的失真不会对缺陷分析产生影响。     

A fuzzy modeling application of CREAM methodology for human reliability analysis

This paper is using a fuzzy classification system for human reliability analysis in order to calculate the probability of erroneous actions according to CREAM in specific contexts e.g. maintenance tasks, in-field actions or control room operations in the running of a chemical plant. The complexities of such a system as well as the appropriate actions that have to be taken into consideration by the developers are analysed in detail. CREAM methodology has been selected among the most known and used methods for Human Reliability Analysis not only for being well-structured and precise, but also because it fits better in the general structure of the fuzzy logic. The first results from the application of the model are very promising and in accordance with CREAM. The numerical values produced by the model can be further used in the ‘classical’ risk assessment methods, such as event trees and fault trees, in order to calculate the overall occurrence frequency of a particular hazardous event. This is a pilot application that demonstrates the successful ‘translation’ of CREAM into a fuzzy logic model. However, the same model could be extended to cover also other industrial fields, like aviation technology and maritime transports.     

Inspection and monitoring planning for RC structures based on minimization of expected damage detection delay

The deterioration mechanism of reinforced concrete (RC) structures under corrosion is highly dependent on environment and material properties. Uncertainties in structural damage occurrence and propagation due to corrosion should be considered in a rational way using a probabilistic approach. In this study, such an approach is proposed to establish a life-cycle optimum inspection plan under uncertainty. This plan leads to cost-effective maintenance interventions, considering uncertainties associated with damage occurrence/propagation and inspection methods. Uncertainties associated with prediction of damage occurrence time are considered by using the Monte Carlo simulation. A damage detectability function is used to assess the quality of inspection method according to damage intensity. The inspection planning is formulated as an optimization problem with the objective of minimizing the expected damage detection delay. This formulation is further used for optimum monitoring planning. Effects of number of inspections and/or monitoring actions, quality of inspection, monitoring duration, and uncertainties associated with damage occurrence/propagation are investigated. The proposed approach is applied to an existing highway bridge. This approach can be used to develop cost-effective management strategies by considering effects of damage detection delay on life-cycle cost and performance of deteriorating structures.     

Synthesis of quantitative and qualitative evidence for accident analysis in risk-based highway planning

Accident analysis involves the use of both quantitative and qualitative data in decision-making. The aim of this paper is to demonstrate the synthesis of relevant quantitative and qualitative evidence for accident analysis and for planning a large and diverse portfolio of highway investment projects. The proposed analysis and visualization techniques along with traditional mathematical modeling serve as an aid to planners, engineers, and the public in comparing the benefits of current and proposed improvement projects. The analysis uses data on crash rates, average daily traffic, cost estimates from highway agency databases, and project portfolios for regions and localities. It also utilizes up to two motivations out of seven that are outlined in the Transportation Equity Act for the 21st Century (TEA-21). Three case studies demonstrate the risk-based approach to accident analysis for short- and long-range transportation plans. The approach is adaptable to other topics in accident analysis and prevention that involve the use of quantitative and qualitative evidence, risk analysis, and multi-criteria decision-making for project portfolio selection.     

Risk analysis of corrosion failures of equipment in refining and petrochemical plants based on fuzzy set theory

Corrosion failures of process equipment have been one of the main sources of risk to refining and petrochemical plants. For reducing failure risk levels and optimizing inspection plans, risk analysis of equipment failures resulting from corrosion need to be implemented. However, due to the complexity and uncertainty of the refining and petrochemical units and risks, effective analyses are hard to accomplish by using conventional risk techniques. With respect to this, a new model for risk analysis of corrosion failures of equipment is developed base on fuzzy set theory. In this model, two essential parts of failure risk (i.e., failure likelihood and severity of failure consequence) are first estimated by using fuzzy synthetic evaluation, and then integrated into a risk index by fuzzy risk graph which is established based on fuzzy logic system. In order to demonstrate the feasibility of this model, an application example in an overhead system of a crude-oil distillation unit was presented. The results show that this model is effective and feasible.     

IFSJSP: A novel methodology for the Job-Shop Scheduling Problem based on intuitionistic fuzzy sets

The Job-Shop Scheduling Problem (JSP) is an important concern in advanced manufacturing systems. In real applications, uncertainties exist practically everywhere in the JSP, ranging from engineering design to product manufacturing, product operating conditions and maintenance. A variety of approaches have been proposed to handle the uncertain information. Among them, the Intuitionistic Fuzzy Sets (IFS) is a novel tool with the ability to handle vague information and is widely used in many fields. This paper develops a method to address the JSP under an uncertain environment based on IFSs. Another contribution of this paper is to put forward a generalised (or extended) IFS to process the additive operation and to compare the operation between two IFSs. The methodology is illustrated using a three-step procedure. First, a transformation is constructed to convert the uncertain information in the JSP into the corresponding IFS. Secondly, a novel addition operation between two IFSs is proposed that is suitable for the JSP. Then a novel comparison operation on two IFSs is presented. Finally, a procedure is constructed using the chromosome of an operation-based representation and a genetic algorithm. Two examples are used to demonstrate the efficiency of the proposed method. In addition, a comparison between the results of the proposed IFSJSP and other existing approaches demonstrates that IFSJSP significantly outperforms other existing methods.     

A classification tool for N-way array based on SIMCA methodology

In the literature there are only few papers concerned with classification methods for multi-way arrays. The most common procedure, by far, is to unfold the multi-way data array into an ordinary matrix and then to apply the traditional multivariate tools for classification. As opposed to unfolding the data several possibilities exist for building classification models more directly based on the multi-way structure of the data. As an example, multi-way partial least squares discriminant analysis has been used as a supervised classification method, another alternative that has been investigated is to perform classification using Fisher's LDA or SIMCA on the score matrix from e.g. a PARAFAC or a Tucker model. Despite a few attempts of applying such multi-way classification approaches, no-one has looked into how such models are best built and implemented.In this work, the SIMCA method is extended to three-way arrays. Included in this work is also actual code that will work on general multi-way arrays rather than just three-way arrays. In analogy with two-way SIMCA, a decomposition model is separately built for the multi-way data for each class, using multi-way decomposition method such as PARAFAC or Tucker3. In the choice of the best class dimensionality, i.e. number of latent factors, both the results of cross-validation but mainly the sensitivity/specificity values are evaluated. In order to estimate the class limits for each class model, orthogonal and score distances are considered, and different statistics are implemented and tested to set confidence limits for these two parameters. Classification performance using different definitions of class boundaries and classification rules, including the use of cross-validated residuals and scores is compared.The proposed N-SIMCA methodology and code, besides simulated data sets of varying dimensionality, has been tested on two case studies, concerning food authentication tasks for typical food products.     

A conceptual framework and practical guide for assessing fitness-to-operate in the offshore oil and gas industry

The paper outlines a systemic approach to understanding and assessing safety capability in the offshore oil and gas industry. We present a conceptual framework and assessment guide for understanding fitness-to-operate (FTO) that builds a more comprehensive picture of safety capability for regulators and operators of offshore facilities. The FTO framework defines three enabling capitals that create safety capability: organizational capital, social capital, and human capital. For each type of capital we identify more specific dimensions based on current theories of safety, management, and organizational processes. The assessment guide matches specific characteristics to each element of the framework to support assessment of safety capability. The content and scope of the FTO framework enable a more comprehensive coverage of factors that influence short-term and long-term safety outcomes.     

A multi-mode failure boundary exploration and exploitation framework using adaptive kriging model for system reliability assessment

Various adaptive reliability analysis methods based on surrogate models have recently been developed. A multi-mode failure boundary exploration and exploitation framework (MFBEEF) was proposed for system reliability assessment using the adaptive kriging model based on sample space partitioning to reduce computational cost and use the characteristics of the failure boundary in multiple failure mode systems. The efficiency of the adaptive construction of kriging model can be improved by using the characteristics of the center sample of the small space to represent the characteristics of all samples in the small space. This method proposes a failure boundary exploration and exploitation strategy and a convergence criterion based on the maximum failure probability error for a system with multiple failure modes to adaptively approximate the failure boundary of a system with multiple failure modes. A multiple-failure-mode learning function was used to identify the optimal training sample to gradually update the kriging model during the failure boundary exploration and exploitation stages. In addition, a complex failure boundary-oriented adaptive hybrid importance sampling method was developed to improve the applicability of the MFBEEF method to small failure probability assessments. Finally, the MFBEEF method was proven to be effective using five system reliability analysis examples: a series system, a parallel system, a series–parallel hybrid system, a multi-dimensional series system with multiple failure modes, and an engineering problem with multiple implicit performance functions.     

Failure analysis on abnormal wall thinning of heat-transfer titanium tubes of condensers in nuclear power plant Part I: Corrosion and wear

Titanium tubes used in condensers in a nuclear power plant in China encountered abnormal wall thinning, and was thus forced to temporarily stop operation or it could bring about catastrophic safety problems. Most of the wall thinning happened at quite regular positions on the tubes and these failure tubes were located similarly in the condensers, indicating some common problems. To find out the root cause and mechanism of the thinning failure, we conducted surface deposit analysis, appearance inspection, microstructure analysis and composition analysis of the samples by means of X-ray diffraction (XRD), stereo microscope, scanning electron microscope (SEM) and Energy Dispersive Spectrometer (EDS). The results revealed that the wall thinning was primarily caused by eccentric contact wear and three-body contact wear rooted in processing defect of internal borings, corrosion products deposit and sagging, and foreign particles. Finally, countermeasures were proposed for repair and prevention.     

A decision framework for risk management, with application to the offshore oil and gas industry

In this paper we present and discuss a decision framework for risk management. The framework comprises the basic elements: problem definition (challenges, goals and alternatives), stakeholders, concerns that affect the consequence analyses and the value judgments related to these consequences and analyses (frame conditions and constraints), identification of which consequence analyses to execute and the execution of these, managerial review and judgement, and the decision. The framework has novel aspects on the way of classifying the decision situations and characterising risks. The classification is based on the two dimensions, expected consequences, and uncertainties. Our starting point is the offshore oil and gas industry, but our framework and discussion is to a large extent general and could also be applied in other areas. An example is outlined to illustrate the use of the framework.     

基于半透膜的钻井液油气分离检测技术

在钻井过程中,录井对钻井液油气的检测是发现和评价油气层重要手段之一,定量获取油气组分信息是有效评价油气层和油气性质的基础.目前录井气测技术所检测的烃组分通常由电动脱气器获得,受钻井液温度、液面高度和黏度的影响较大,所获得的油气定量信息较少,影响了油气解释与评价的准确性.半透膜对油气具有选择性分离的特性,能直接从钻井液中获取烃类组分,能够为油气解释与评价提供定量的基础信息.通过对半透膜和气体分离技术的研究,制备适合现场油气分离要求的半透膜,并在此基础上研制了半透膜油气分离装置,通过稳定性和灵敏度、耐温和抗冲击性能试验,以及钻井现场的应用,其结果证明了该半透膜钻井液油气分离装置适用于钻井现场的油气检测,扩展了油气检测内容,丰富了油气评价信息.     

New methods for specification and determination of component reliability characteristics

In the last two decades it has become clear that component lifetime distributions are seldom exponential. Early failures due to flaws have been of major concern. Various models to cater for this observation have been suggested. The models have been developed from a component rather from a system point of view. Furthermore, no substitute for the heavily criticized MIL-HDBK-217-type prediction handbooks has been offered. This paper describes a recently developed methodology to arrive at realistic component reliability characteristics, which take into account the non-exponential component lifetime distributions. These characteristics are developed from a system's point of view, which makes it easier for the system manufacturer to assess the reliability of their systems in the field. The methodology is based on six years of research in field performance of electronic systems. The research has been carried out in close co-operation with industrial companies.     

A phenomenologically based damage model for 2D and 3D-textile composites with non-crimp reinforcement

The application of textile-reinforced composites for safety-relevant structural components requires reliable predictions about their damage and failure behaviour. The potential of these materials for engineering applications has not been fully exploited so far since practical design rules disallow the occurence of any damage in the material even if the damage is not critical. In this context, the paper presents a novel damage model for textile composites with quasi-unidirectional reinforcement. A failure criterion based on the failure mode concept is adopted to describe the quasi-brittle fracture behaviour. To take into account the subsequent non-linear stiffness degradation, this approach is combined with a continuum damage mechanics model. The capability of the damage model is shown for biaxially reinforced weft-knitted glass fibre–epoxy composites.     

A global view on ARAMIS, a risk assessment methodology for industries in the framework of the SEVESO II directive

The ARAMIS methodology was developed in an European project co-funded in the fifth Framework Programme of the European Commission with the objective to answer the specific requirements of the SEVESO II directive. It offers an alternative to purely deterministic and probabilistic approaches to risk assessment of process plants. It also answers the needs of the various stakeholders interested by the results of the risk assessment for land use or emergency planning, enforcement or, more generally, public decision-making. The methodology is divided into the following major steps: identification of major accident hazards (MIMAH), identification of the safety barriers and assessment of their performances, evaluation of safety management efficiency to barrier reliability, identification of reference accident scenarios (MIRAS), assessment and mapping of the risk severity of reference scenarios and of the vulnerability of the plant surroundings. The methodology was tested during five case studies, which provided useful information about the applicability of the method and, by identifying the most sensitive parts of it opened way to new research activity for an improved industrial safety.