Study and application of Reliability-centered Maintenance considering Radical Maintenance

Radical Maintenance (RM) takes the root causes of failures as executive objects to make maintenance decisions. A Root Cause Analysis (RCA) is carried out on the function failure of equipment to perform RM and to create a maintenance plan using the combined methods of Failure Mode, Effects and Criticality Analysis (FMECA), and Fault Tree Analysis (FTA). Evaluation criteria and matrixes of criticality are used to evaluate the criticality level of failure modes. Moreover, Minimum Cut Sets (MCS) and importance calculation are applied to analyze the fault tree quantitatively. Based on our research, the concept and the analysis process of Reliability-centered Maintenance (RCM) considering RM are proposed and then applied to an actual engineering project in the petrochemical industry. The results of the application are presented through a maintenance strategy based on the project analysis by establishing the evaluation criteria and matrixes of criticality for petrochemical rotating machines and a fault tree of compressor vibrations. The direct causes of the induction of vibration faults in rotating machinery are classified according to the fault mechanism and frequency domain features in this paper. The research shows that using RM in the traditional RCM can help assign maintenance resources rationally and improve the quality of maintenance strategies.     

基于Bayesian网络的常减压装置设备故障概率分析

为分析影响常减压蒸馏装置平稳运行的设备失效模式及故障部件,基于1 151条设备故障数据,采用Bayesian网络分析方法,分别对离心泵、压缩机、电动机构建基于Bayesian网络的设备故障概率分析模型,分析故障部件、失效模式、故障后果之间的定量概率关系。研究结果表明:离心泵、压缩机、电动机停运的关键致因部件分别为轴承箱密封故障、活塞环故障、轴承故障,同时得到导致设备停运的故障部件敏感度排序。研究结果有助于提高设备故障风险防范及检维修工作效率,同时可为备件优化方案提供思路。     

Fault detection and diagnostics of a three-phase separator

A high demand of oil products on daily basis requires oil processing plants to work with maximum efficiency. Oil, water and gas separation in a three-phase separator is one of the first operations that are performed after crude oil is extracted from an oil well. Failure of the components of the separator introduces the potential hazard of flammable materials being released into the environment. This can escalate to a fire or explosion. Such failures can also cause downtime for the oil processing plant since the separation process is essential to oil production. Fault detection and diagnostics techniques used in the oil and gas industry are typically threshold based alarm techniques. Observing the sensor readings solely allows only a late detection of faults on the separator which is a big deficiency of such a technique, since it causes the oil and gas processing plants to shut down.A fault detection and diagnostics methodology for three-phase separators based on Bayesian Belief Networks (BBN) is presented in this paper. The BBN models the propagation of oil, water and gas through the different sections of the separator and the interactions between component failure modes and process variables, such as level or flow monitored by sensors installed on the separator. The paper will report on the results of the study, when the BBNs are used to detect single and multiple failures, using sensor readings from a simulation model. The results indicated that the fault detection and diagnostics model was able to detect inconsistencies in sensor readings and link them to corresponding failure modes when single or multiple failures were present in the separator.     

成品油站场工艺管道风险辨识与评价

为保障成品油站场工艺管道的安全平稳运行,在充分辨识风险因素的前提下,提出了一种基于KENT法和RBI的风险评价方法。首先,以风险机理分析为基础,采用SHEL模型从导致风险上升的直接原因（风险内因）和间接原因（风险外因）2个角度辨识了风险因素,并细化各因素指标项;然后借鉴KENT法,对各指标项进行评分量化;依据RBI法,采用风险外因体系修正风险内因体系的方式确定失效可能性;综合环境后果、人员后果与商业后果,明确失效后果的评价体系与计算方法;最后,结合失效可能性与失效后果进行风险评价,从风险等级和风险排序2方面为检维修决策提供依据。应用表明:该评价方法便于工艺管道风险评价的基层实施,可为基于风险的检维修决策提供有效技术支撑。     

Improved qualitative fault propagation analysis

Fault propagation analysis is the cornerstone to assure safe operation, optimized maintenance, as well as for the management of abnormal situations in chemical and petrochemical plants. Due to plant complexity and dynamic changes in plant conditions, current approaches have major limitations in identifying all possible fault propagation scenarios. This is due to the lack of realistic equipment and fault models. In this paper, practical framework is proposed to synthesize and assess all possible fault propagation scenarios based on robust modeling methodology. Fault models are constructed where deviations are identified and associated with symptoms, faults, causes, and consequences. Fault models are tuned using real time process data, simulation data, and human experience. The proposed system is developed and applied on case study experimental plant.     

Safety study of an LNG regasification plant using an FMECA and HAZOP integrated methodology

A safety analysis was performed to determine possible accidental events in the storage system used in the liquefied natural gas regasification plant using the integrated application of failure modes, effects and criticality analysis (FMECA) and hazard and operability analysis (HAZOP) methodologies. The goal of the FMECA technique is the estimation of component failure modes and their major effects, whereas HAZOP is a structured and systematic technique that provides an identification of the hazards and the operability problems using logical sequences of cause-deviation-consequence of process parameters. The proposed FMECA and HAZOP integrated analysis (FHIA) has been designed as a tool for the development of specific criteria for reliability and risk data organisation and to gain more recommendations than those typically provided by the application of a single methodology. This approach has been applied to the risk analysis of the LNG storage systems under construction in Porto Empedocle, Italy. The results showed that FHIA is a useful technique to better and more consistently identify the potential sources of human errors, causal factors in faults, multiple or common cause failures and correlation of cause-consequence of hazards during the various steps of the process.     

Development of a risk-based maintenance strategy using FMEA for a continuous catalytic reforming plant

Petrochemical facilities and plants require essential ongoing maintenance to ensure high levels of reliability and safety. A risk-based maintenance (RBM) strategy is a useful tool to design a cost-effective maintenance schedule; its objective is to reduce overall risk in the operating facility. In risk assessment of a failure scenario, consequences often have three key features: personnel safety effect, environmental threat and economic loss. In this paper, to quantify the severity of personnel injury and environmental pollution, a failure modes and effects analysis (FMEA) method is developed using subjective information derived from domain experts. On the basis of failure probability and consequence analysis, the risk is calculated and compared against the known acceptable risk criteria. To facilitate the comparison, a risk index is introduced, and weight factors are determined by an analytic hierarchy process. Finally, the appropriate maintenance tasks are scheduled under the risk constraints. A case study of a continuous catalytic reforming plant is used to illustrate the proposed approach. The results indicate that FMEA is helpful to identify critical facilities; the RBM strategy can increase the reliability of high-risk facilities, and corrective maintenance is the preferred approach for low-risk facilities to reduce maintenance expenditure.     

考虑冰风载荷影响的输电线路运行风险评估方法*

为预防在极端冰灾环境下大规模停电事故的发生，提出1种考虑冰风载荷的输电线路运行风险评估方法。在极端冰风环境下，计算输电线路与杆塔所承受的总载荷，利用强度与应力干涉模型确定输电线路与杆塔的故障率，同时结合串联可靠性原理确定线路故障综合概率；利用非贯序蒙特卡洛法与线路故障综合概率设置预想故障集，结合直流潮流优化模型计算每条线路的失负荷量；综合考虑负荷等级与抢修恢复时间，确定线路失负荷的经济价值并定义为故障后果；将故障后果与线路故障综合概率的乘积定义为输电线路风险评估指标。以实际拓扑结构修正的IEEE30节点开展算例分析。结果表明:所提方法可对输电线路运行风险进行有效评估。     

安全系统的故障分析

以可靠性研究为基础,分析独立和冗余配置的安全系统的故障状态,给出独立配置和冗余配置时安全系统的显性故障率、隐性故障率以及系统可用度的定量模型。以该模型对以紧急停车系统(ESD)和生产监控系统(DCS)构成的组合安全系统进行了比较和分析。笔者认为:ESD单独设置时,应注意降低隐性故障率、提高其修复率;ESD与DCS冗余配置时,无论是并联冗余还是串联冗余,安全保护功能都是增加的,但同时显性故障率有所增加,隐形故障率大大减少。因此,用ESD补充DCS的安全保护功能而不是简单的重复其功能,对提高系统的整体安全水平将是十分明显的。     

A method for assisting the accident consequence prediction and cause investigation in petrochemical industries based on natural language processing technology

Risk analysis for production processes in the petrochemical industry is an important procedure for consequence prediction and investigation of accidents. The analyzer must grasp the correlations between the possible causes and consequences. From the potential cause and effect found in risk analysis reports, complete clarification should be obtained. Therefore, this study presents a method for assisting accident consequence prediction and investigation in the petrochemical industry based on risk analysis reports using natural language processing technology. First, a hazard and operability (HAZOP) historical data table is established by filling over 7200 HAZOP analysis data points. Both the causes and consequences in the table are classified into 20 categories each using the Latent Dirichlet Allocation (LDA) models. The LDA clustering results are assigned classification for the cause and consequence topics to the cause and consequences of the HAZOP analysis data. Based on part-of-speech (POS) tagging, all the words in each cause and consequence record are divided into subject and action words. Next, the word combinations of subject and action words with a higher occurrence are considered the key phrases for describing and representing the corresponding cause and consequence topic classifications. The Apriori algorithm is used to determine the frequent item sets, acquire the association rules, and calculate the association degree to obtain the sort order; it can highlight general trends in relational cause and consequence topics. According to the results, the most likely cause of the consequence and the most likely consequence that the cause may lead to are identified. Finally, a visual interface is developed to present the data for the consequence prediction and cause investigation of accidents. The results reveal that the quantity and quality of historic data are important factors that may influence the results. This method can contribute to predicting the accident evolution trend of an abnormal situation, taking preventive measures in advance, improving the accuracy of early warning, and supporting emergency response measures.     

可靠性数据在民机故障诊断中的应用研究

可靠性数据是故障统计历史信息的记录,数据内隐含着大量对维修人员排故具有指导意义的信息,为达到提高工作效率,减少排故时间,以保证航班正点运营的目的,提出将可靠性数据应用于民机故障诊断的计算模型。利用从可靠性报表中收集到的数据对某航空公司主力机型A320-200飞机的空调系统常发故障进行诊断,首先用粗糙集(RS)对8个可能导致温度控制组件超温故障的部件进行分析,初步总结出3个故障源,然后计算综合故障概率,最后用Weibull分布验证以上诊断结论。结果表明,该法的引入能实现故障源的精确定位,并在实际中成功排故,大大缩短维修时间。     

输油气站场综合风险评价技术研究

为完善国内输油气站场综合风险评价技术,提出一种站场综合风险评价模型。该模型基于模糊集理论,重点对传统的RCM与RBI技术进行改进与完善。将故障(失效)后果划分为伤亡后果、经济损失、环境影响和无形损失;采用模糊推理系统代替原有的算法进行计算设备风险,将动设备与静设备的风险转换为统一标度,得到站场综合风险值;最后采用雷达图显示站内设备的风险分布状况。实例分析表明,所建立的综合风险评价模型能够有效地解决定性风险与定量风险的融合问题,可为输油气站场的风险管理提供决策依据。     

Integrated dynamic risk assessment of buried gas pipeline leakages in urban areas

In urban areas, buried gas pipeline leakages could potentially cause numerous casualties and massive damage. Traditional static analysis and dynamic probability-based quantitative risk assessment (QRA) methods have been widely used in various industries. However, dynamic QRA methods combined with probability and consequence are rarely used to evaluate gas pipelines buried in urban areas. Therefore, an integrated dynamic risk assessment approach was proposed. First, a failure rate calculation of buried gas pipelines was performed, where the corrosion failure rate dependent on time was calculated by integrating the subset simulation method. The relationship between failure probability and failure rate was considered, and a mechanical analysis model considering the corrosion growth model and multiple loads was used. The time-independent failure rates were calculated by the modification factor methods. Next, the overall evolution process from pipeline failures to accidents was proposed, with the accident rates subsequently updated. Then, the consequences of buried gas pipeline accidents corresponding to the accident types in the evolution process were modeled and analyzed. Finally, based on the above research, dynamic calculation and assessment methods for evaluating individual and social risks were established, and an overall application example was provided to demonstrate the capacity of the proposed approach. A reliable and practical theoretical basis and supporting information are provided for the integrity and emergency management of buried gas pipelines in urban areas, considering actual operational conditions.     

An extended HAZOP analysis approach with dynamic fault tree

An extended hazard and operability (HAZOP) analysis approach with dynamic fault tree is proposed to identify potential hazards in chemical plants. First, the conventional HAZOP analysis is used to identify the possible fault causes and consequences of abnormal conditions, which are called deviations. Based on HAZOP analysis results, hazard scenario models are built to explicitly represent the propagation pathway of faults. With the quantitative analysis requirements of HAZOP analysis and the time-dependent behavior of real failure events considered, the dynamic fault tree (DFT) analysis approach is then introduced to extend HAZOP analysis. To simplify the quantitative calculation, the DFT model is solved with modularization approach in which a binary decision diagram (BDD) and Markov chain approach are applied to solve static and dynamic subtrees, respectively. Subsequently, the occurrence probability of the top event and the probability importance of each basic event with respect to the top event are determined. Finally, a case study is performed to verify the effectiveness of the approach. Results indicate that compared with the conventional HAZOP approach, the proposed approach does not only identify effectively possible fault root causes but also quantitatively determines occurrence probability of the top event and the most likely fault causes. The approach can provide a reliable basis to improve process safety.     

高速铁路信号系统的安全管理评价研究

高速铁路信号系统要通过安全管理来保障其开发与运行中的安全相关活动符合系统安全计划的要求。为了评估安全管理活动的可信性,提出基于系统安全分析技术的安全管理评价方法。通过建立安全管理流程与系统安全功能相关联的概念模型,使用安全文化危险与可操作性研究(SCHAZOP)辨识出安全管理流程中的行为偏差,基于失效传导转换符号(FPTN)建立管理角色的安全文化失效模型,最终将管理行为失效模式转换成组件故障树作为安全管理评价证据。研究结果表明,安全管理行为偏差体现了信号系统开发与运营过程中的安全文化特征;辨识与分析安全文化失效,为信号系统安全管理活动的可信性提供了评价依据。     

SVD在串联故障电弧检测及选相中的应用*

煤矿井下发生串联故障电弧易引发火灾等安全事故,为了预防电气火灾、指导线路维修,利用三相电动机及变频器负载开展不同线路、不同电流条件下的串联故障电弧实验,研究三相串联故障电弧的检测及选相方法。首先,对单相电流进行一阶差分处理后,建立改进的吸引子轨迹矩阵作为故障特征矩阵;其次,对故障特征矩阵进行奇异值分解,采用特征矩阵的奇异值构建串联故障电弧检测及选相的特征向量;最后,利用极限学习机建立故障电弧检测及选相模型,并测试检测及选相准确率。研究结果表明:提出的SVD方法可以利用单相电流实现三相电动机及变频器负载回路中的串联故障电弧检测及选相。     

Developing an expert prognosis system of the reciprocating compressor based on associations among monitoring parameters and maintenance records

The reciprocating compressor is, in general, a critical equipment in a process plant. For certain ultra-high-pressure process, if the reciprocating compressor fails, often it will cause serious impact to not just the compressor itself, but also the process surrounds it. To prevent compressors from failures, an expert diagnosis system is needed. However, the traditional rule-based expert system is quite inefficient and difficult to create.For an expert prognosis system that is customized to meet needs of a specific process, one needs to refer to plant maintenance history, which is hard to come by due to the fact that most maintenance was poorly documented. This research attempt to demonstrate the feasibility of developing an expert prognosis system through implementation of association rules. Rather than mining from maintenance history, records of failure cases were collected from technical journal articles by extracting information containing failure symptoms and causes on failed components, that mimicking repair history. In total, 115 failure information out from 41 journal articles were gathered. Applications of this approach to practical use in a process plant is easy by replacing the failure information table with that from datamining the repair history. The failure information was first tabulated and then put through association analysis for support, confidence, and lift between two parameters. The demonstration program has been successful with 1-to-1, many-to-1, and many-to-many analysis among failed components, failure modes, and operation parameters.     

A new fault detection method for non-Gaussian process based on robust independent component analysis

Conventional fault detection method based on fast independent component analysis (FastICA) is sensitive to outliers in the modeling data and thus may perform poorly under the adverse effects of outliers. To solve such problem, a new fault detection method for non-Gaussian process based on robust independent component analysis (RobustICA) is proposed in this paper. A RobustICA algorithm which can effectively reduce the effects of outliers is firstly developed to estimate the mixing matrix and extract non-Gaussian feature called independent components (ICs) by robust whitening and robust determination of the maximum non-Gaussian directions. Furthermore, a monitoring statistic for each extracted IC is constructed to detect process faults. Simulations on a simple example of the mixing matrix estimation and a fault detection example in the continuous stirred tank reactor system demonstrate that the RobustICA achieves much higher estimation accuracy for the mixing matrix and the ICs than the commonly used FastICA algorithm, and the RobustICA-based fault detection method outperforms the conventional FastICA-based fault detection method in terms of the fault detection time and fault detection rate.     

Simulation-based fault propagation analysis—Application on hydrogen production plant

Recently production of hydrogen from water through the Cu–Cl thermochemical cycle is developed as a new technology. The main advantages of this technology over existing ones are higher efficiency, lower costs, lower environmental impact and reduced greenhouse gas emissions. Considering these advantages, the usage of this technology in new industries such as nuclear and oil is increasingly developed. Due to hazards involved in hydrogen production, design and implementation of hydrogen plants require provisions for safety, reliability and risk assessment. However, very little research is done from safety point of view. This paper introduces fault semantic network (FSN) as a novel method for fault diagnosis and fault propagation analysis by using evolutionary techniques like genetic programming (GP) and neural networks (NN), to uncover process variables’ interactions. The effectiveness, feasibility and robustness of the proposed method are demonstrated on simulated data obtained from the simulation of hydrogen production process in Aspen HYSYS^®. The proposed method has successfully achieved reasonable detection and prediction of non-linear interaction patterns among process variables.     

Integrating switchgear breakers and contactors into a safety instrumented function

In the process industries, electrical circuit breakers are tripped for a variety of reasons including hazard prevention and mitigation. This paper will discuss the application of tripping electrical breakers for process safety reasons. It often occurs that a risk assessment study has determined the need to include the shutdown of a large electric motor as part of a safety instrumented function (SIF). In the process industries, it is common practice to control and protect large horsepower motors (typically 2000 HP and above), with medium-voltage switchgear breakers. Safety instrumented functions that include electric motors utilizing medium-voltage switchgear for tripping have unique final element subsystems, which require specialized knowledge in order to implement correctly. This paper will discuss the design considerations for integrating medium-voltage switchgear used for shutdown of an electric motor into a safety instrumented function. First, an overview of typical low and medium-voltage electrical equipment used for controlling and protecting electric motors will be discussed. Next, a review of generic sourced failure data for medium-voltage switchgear breakers will be presented. Focus will be on the taxonomy used to classify different breaker types and the relevant failure modes and effects used for quantifying performance. Next, specific SIF design details for tripping medium-voltage switchgear breakers will be analyzed in the context of achieving a certain integrity level. Parameters to be considered include hardware fault tolerance requirements, voting, available diagnostics, energize vs. de-energize to trip shutdown circuits, certified equipment vs. proven-in-use, and alternate means of shutdown (including manual intervention and protective relaying). This paper will also analyze the common electrical equipment that is shared among potential protection layers used to trip the same breaker, and determine how much credit can be taken considering common cause failure. Finally, inspection, test, and preventive maintenance (ITPM) of medium-voltage switchgear will be discussed as a means for maintaining the electrical equipment in the “as good as new” condition.