Risk-based maintenance of ethylene oxide production facilities

This paper discusses a methodology for the design of an optimum inspection and maintenance program. The methodology, called risk-based maintenance (RBM) is based on integrating a reliability approach and a risk assessment strategy to obtain an optimum maintenance schedule. First, the likely equipment failure scenarios are formulated. Out of many likely failure scenarios, the ones, which are most probable, are subjected to a detailed study. Detailed consequence analysis is done for the selected scenarios. Subsequently, these failure scenarios are subjected to a fault tree analysis to determine their probabilities. Finally, risk is computed by combining the results of the consequence and the probability analyses. The calculated risk is compared against known acceptable criteria. The frequencies of the maintenance tasks are obtained by minimizing the estimated risk. A case study involving an ethylene oxide production facility is presented. Out of the five most hazardous units considered, the pipeline used for the transportation of the ethylene is found to have the highest risk. Using available failure data and a lognormal reliability distribution function human health risk factors are calculated. Both societal risk factors and individual risk factors exceeded the acceptable risk criteria. To determine an optimal maintenance interval, a reverse fault tree analysis was used. The maintenance interval was determined such that the original high risk is brought down to an acceptable level. A sensitivity analysis is also undertaken to study the impact of changing the distribution of the reliability model as well as the error in the distribution parameters on the maintenance interval.     

基于灰信息挖掘的视情维修决策方法研究

针对列装新设备缺乏状态劣化信息和维修阈值难以进行视情维修决策的问题,基于新旧设备故障率变化趋势的一致性,定义改进性系数表示基本故障率的变化不同,得到新型设备的故障率函数,结合回归支持向量机拟合新型设备风险度函数曲线,得到新型设备的维修阈值,进而建立新型设备的视情维修决策模型,最后以船用柴油机监测数据进行了实例验证。结果表明,该方法能有效挖掘旧型柴油机的历史数据信息,充分利用新旧柴油机之间的内在联系,为灰信息条件下新设备的视情维修决策提供了新途径。     

Risk-based maintenance planning of subsea pipelines through fatigue crack growth monitoring

Research and development in the field of risk-based maintenance of offshore structures has recently attracted large attention due to the significant level of accident risk and the cost associated with maintenance in such remote facilities. The uncertainties associated with the deterioration of these facilities require a sound decision making methodology for maintenance planning. This paper presents a dynamic risk-based methodology for maintenance scheduling of subsea pipelines subjected to fatigue cracks. The developed method can assist the asset managers to select the optimum approach for mitigating the consequences of failure while minimizing the maintenance costs. A Bayesian network is developed to model the probabilistic deterioration process and then it is extended to an influence diagram for estimating the expected utility of each decision alternative. Observation of damage state is included in the model to enhance decision making capacity. To demonstrate the applicability of the methodology, three cases with different fatigue crack incidents on a pipeline are considered. Based on the monitoring results, the model is able to determine whether the maintenance should be performed or not. The economic risk associated with maintenance is also minimized by suggesting the optimum maintenance technique among multiple possible methods such as welding or major repair.     

A Case Study of GAMM (Graphical Analysis for Maintenance Management) Applied to Water Pumps in a Sewage Treatment Plant,Chile

This article presents a case for practical application of the GAMM method, which has been developed and published by the authors. The GAMM method supports decision making in the overall maintenance management, through the visualization and graphical analysis of data. In addition, it allows for the identification of anomalous behavior in the equipment analyzed, whether derived from its own operations, maintenance activities, improper use of equipment or even as a result of design errors in the equipment itself. As a basis for analysis, the GAMM method uses a nonparametric estimator of the reliability function using all historical data or, alternatively, part of the history, allowing it to perform an analysis even with limited available data. In the case study presented, the GAMM method has been used to analyze two existing equipment parts in a sewage treatment plant located in Chile. The equipment analyzed were two progressive cavity pumps (BCP) which are part of the industrial process in the treatment plant. Both pumps are the same make and model and subjected to the same maintenance program, but one had a higher failure rate compared to its counterpart. The application of the GAMM method has identified deficiencies in each of the pumps being studied, thus improving decision‐making and problem‐solving process related to the maintenance of the pumps. RCM methodology was used to draft a new maintenance plan. Copyright © 2013 John Wiley & Sons, Ltd.     

Application of Bayesian statistical decision theory for a maintenance optimization problem

Reliability-centered maintenance (RCM) is a rational approach that can be used to identify the equipment of facilities that may turn out to be critical with respect to safety, to availability, or to maintenance costs. Is is dor these critical pieces of equipment alone that a corrective (one waits for a failure) or preventive (the type and frequency are specified) maintenance policy is established.But this approach has limitations:

• •when there is little operating feedback and it concerns rare events affecting a piece of equipment judged critical on a priori grounds (how is it possible, in this case, to decide whether or not it is critical, since there is conflict between the gravity of the potential failure and its frequency?);

• •when the aim is propose an optimal maintenance frequency for a critical piece of equipment - changing the maintenance frequency hitherto applied may cause a significant drift in the observed reliability of the equipment, an aspect not generally taken into account in the RCM approach.

In these two situations, expert judgments can be combined with the available operating feedback (Bayesian approach) and the combination of risk of failure and economic consequences taken into account (statistical decision theory) to achieve a true optimization of maintenance policy choices.This paper presents an application on the maintenace of diesel generator component.     

A methodology for risk-based inspection planning of oil and gas pipes based on fuzzy logic framework

In an efficient and effective pipe integrity management programme, maintenance engineers often use the risk-based inspection (RBI) and maintenance strategy. Unfortunately, the calculation of risk is a daunting task because in order to calculate the risk of failure, a maintenance engineer needs to predict the rate of growth of a defect, the effect of the defect on the integrity of the structure and the consequence of failure. Unfortunately precise calculation for either of these parts is quite difficult.Fuzzy logic is a mathematical tool suitable for handling imprecise information in the real world. The benefit of this approach lies in its ability to include personal experiences along with acceptable deterministic models in the calculation. The structure of the model also allows easy calibration of the model to suit a particular plant condition. This approach can thus help to reduce the dependence upon the precise data, allow modelling even when a phenomenon is incompletely understood, and reduce the difficulties arising due to the complex computation required by more traditional methods.This paper presents a proposed methodology, based on fuzzy logic framework, for the establishment of an RBI programme for pipes. The paper also presents in detail a section of the methodology that can be used for calculating the estimated rate of CO₂ corrosion in carbon steel pipes. In this technique the plant operating parameters (temperature, gas and liquid flow rates, total pressure, CO₂ partial pressure and pH) are taken as fuzzy variables and used to calculate the Predicted Rate of Corrosion. The inspected rate of corrosion and the efficiency of inspection are also considered as fuzzy variables and are used to calculate Trust in Inspection Results and Trust in Predicted Results. By combining all the modules an estimated rate of corrosion is calculated. This estimated rate of corrosion can then be used for developing the risk-based inspection programme.     

A corrective maintenance scheme for engineering equipment

Corrective maintenance is a maintenance task performed to identify and rectify the cause failures for a failed system. The engineering equipment gets many components and failure modes, and its failure mechanism is very complicated. Failure of system-level might occur due to failure(s) of any subsystem/component. Thus, the symptom failure of equipment may be caused by multilevel causality of latent failures.This paper proposes a complete corrective maintenance scheme for engineering equipment. Firstly, the FMECA is extended to organize the numerous failure modes. Secondly, the failure propagation model (FPM) is presented to depict the cause-effect relationship between failures. Multiple FPMs will make up the failure propagation graph (FPG). For a specific symptom failure, the FPG is built by iteratively searching the cause failures with FPM. Moreover, when some failure in the FPG is newly ascertained to occur (or not), the FPG needs to be adjusted. The FPG updating process is proposed to accomplish the adjustment of FPG under newly ascertained failure. Then, the probability of the cause failures is calculated by the fault diagnosis process. Thirdly, the conventional corrective maintenance recommends that the failure with the largest probability should be ascertained firstly. However, the proposed approach considers not only the probability but also the failure detectability and severity. The term REN is introduced to measure the risk of the failure. Then, a binary decision tree is trained based on REN reduction to determine the failure ascertainment order. Finally, a case is presented to implement the proposed approach on the ram feed subsystem of a boring machine tool. The result proves the validity and practicability of the proposed method for corrective maintenance of engineering equipment.     

Optimization of safety equipment outages improves safety

Testing and maintenance activities of safety equipment in nuclear power plants are an important potential for risk and cost reduction. An optimization method is presented based on the simulated annealing algorithm. The method determines the optimal schedule of safety equipment outages due to testing and maintenance based on minimization of selected risk measure. The mean value of the selected time dependent risk measure represents the objective function of the optimization. The time dependent function of the selected risk measure is obtained from probabilistic safety assessment, i.e. the fault tree analysis at the system level and the fault tree/event tree analysis at the plant level, both extended with inclusion of time requirements. Results of several examples showed that it is possible to reduce risk by application of the proposed method. Because of large uncertainties in the probabilistic safety assessment, the most important result of the method may not be a selection of the most suitable schedule of safety equipment outages among those, which results in similarly low risk. But, it may be a prevention of such schedules of safety equipment outages, which result in high risk. Such finding increases the importance of evaluation speed versus the requirement of getting always the global optimum no matter if it is only slightly better that certain local one.     

The EDF failure reporting system process, presentation and prospects

This paper describes the procedure Electricité de France uses to exploit the information on pressurized water reactor operation it receives back from the field (operation feedback).The first requirement in analyzing such data is a knowledge of past records. The first step, therefore, is to record the data, particularly events occurring on the plant and failures occurring on equipment, in large reliability data banks.However, the ‘raw’ information stored is rarely usable directly. The first step in the second stage—analysis—is to review and qualify the data before using it for any purpose. This difficult, but essential, review provides valuable information on the improvement of equipment reliability.The greater knowledge of plant and equipment behaviour, and the damage mechanisms involved, allows:

• safety to be kept at a high level: operation feedback is also essential for probabilistic safety studies,

• improvement of availability and preventive maintenance practices,

• correction of the initial design (design changes) and help for designing future plants.

Finally, operation feedback is a source of progress. Although it requires heavy initial investment, it is also a source of profit. It is a source of learning. The analysis results make it possible to define more suitable procedures and better preventive maintenance practices and thus improve the operation and safety of existing and future plants.     

The role of NHPP models in the practical analysis of maintenance failure data

The analysis of failure data is an important facet in the development of maintenance strategy for equipment. Only by properly understanding the mechanism of failure, through the modelling of failure data, can a proper maintenance plan be developed. This is normally done by means of probabilistic analysis of the failure data. From this, conclusions can be reached regarding the effectiveness and efficiency of preventive replacement (and overhaul) as well as that of predictive maintenance. The optimal frequency of maintenance can also be established by using well developed optimisation models. These optimise outputs, such as profit, cost and availability. The problem with this approach is that it assumes that all repairable systems are repaired to the ‘good-as-new’ condition at each repair occasion. Maintenance practice has learnt, however, that in many cases equipment slowly degrades even while being properly maintained (including part replacement and periodic overhaul). The result of this is that failure data sets often display degradation. This renders conventional probabilistic analysis useless. During the last two decades, a few researches applied themselves to the solution of this problem.This paper briefly examines the present state of the theoretical foundation of repairable systems analysis techniques and then develops two formats of the Non-Homogeneous Poisson Process model (NHPP model) for practical use by the maintenance analyst. This includes an identification framework, goodness-of-fit tests and optimisation modelling. The model is tested on two failure data sets from literature and one from industry.     

Risk assessment and optimization of a safety system using the maros simulation package

The performance of a pressure-surge relief system which acts as a safety mechanism within the export facility of an oil terminal is analysed in detail. Dynamic simulation methods are integrated with failure mode effects analysis to establish the risk of catastrophic failure of the terminal. An optimization procedure is presented which demonstrates the use of simulation to establish suitable inspection, testing and preventive maintenance schedules which maximize the effectiveness of the surge relief system over its design life. The importance of such activities is evident — left unattended the surge-relief system will not meet minimum safety standards, whereas planned maintenance and testing is shown to improve safety standards above the minimum required.     

A Dynamic Risk Assessment Methodology for Maintenance Decision Support

The failure mode and effect analysis (FMEA) is a widely applied technique for prioritizing equipment failures in the maintenance decision‐making domain. Recent improvements on the FMEA have largely focussed on addressing the shortcomings of the conventional FMEA of which the risk priority number is incorporated as a measure for prioritizing failure modes. In this regard, considerable research effort has been directed towards addressing uncertainties associated with the risk priority number metrics, that is occurrence, severity and detection. Despite these improvements, assigning these metrics remains largely subjective and mostly relies on expert elicitations, more so in instances where empirical data are sparse. Moreover, the FMEA results remain static and are seldom updated with the availability of new failure information. In this paper, a dynamic risk assessment methodology is proposed and based on the hierarchical Bayes theory. In the methodology, posterior distribution functions are derived for risk metrics associated with equipment failure of which the posterior function combines both prior functions elicited from experts and observed evidences based on empirical data. Thereafter, the posterior functions are incorporated as input to a Monte Carlo simulation model from which the expected cost of failure is generated and failure modes prioritized on this basis. A decision scheme for selecting appropriate maintenance strategy is proposed, and its applicability is demonstrated in the case study of thermal power plant equipment failures. Copyright © 2016 John Wiley & Sons, Ltd.     

A method for defence product major overhaul analysis focusing on interfaces redesign and extended life supportability

Due to high acquisition costs of new units, defence products can be subjected to life extension processes, sometimes not previously planned during their life cycle. These processes are characterised by widespread maintenance tasks and they can be denominated as major overhauls. The main objective of this paper is to propose a methodology to aid the major overhaul technical management. In general, the major overhaul defines the scope of maintenance and modernisation. Main problems about maintenance are managed by risk-based decision-making for determination of replacement rules and quantities of spares. The need for redesign of parts that interacts with the upgraded subsystems is evaluated by design structure matrix and a HAZOP-based analysis aiming at achieving product performance requirements. The proposed method is a support tool to the major overhaul manager, not replacing technical methodologies that are specific to each branch of the overhauled product. The proposed method was applied to a major overhaul project of an armoured personnel carrier. The results enabled a better characterisation of detailed design, analysing risks in the context of maintenance and modernisation aiming at controlling modernised product life cycle.     

基于器械包改进的消毒器械物流系统智能优化研究

目的 利用物联网技术改进器械包并优化消毒器械物流系统,开发出污染风险低、回收率与追溯率高的消毒器械智能化物流系统。方式 通过文献分析法确定器械污染独立影响因子,运用射频识别技术(RFID)改进器械包,利用智能设备优化原物流系统;根据动态规划与贝叶斯风险原理构建消毒器械污染风险函数,在有限预算下测算出风险最低的建设方案,对比分析优化前后器械回收率与污染率,检验优化效果。结果 算例结果得到,相较于原系统,优化后的智能物流系统器械回收率提高了79.82%,污染追溯率提高了81.18%,器械污染风险率降低了86.33%。结论 结合器械污染风险函数运算结果表明,在一定预算条件下,该模型能测算出污染风险最小的系统优化方案,帮助管理者做出决策。说明合理运用物联网技术提高消毒器械物流系统数字化水平,不仅能提高器械数据采集率,还能有效提高器械回收率与污染追溯率,降低器械污染风险。     

Risk-based inspection and maintenance in process plants and their practices in Taiwan

The content of risk-based assessment and management and risk-based inspection and maintenance (RBI&M) employed in process plants are addressed in this article. Probabilistic analyses including probabilistic risk assessment developed in the engineering field over the past few decades are reviewed first. Following that, RBI and risk-informed management employed in the nuclear industry and risk-based inspection, maintenance, and other risk-based approaches used in chemical plants as well as their developments are reviewed, respectively. While most of the above were developed and practiced in industrialized countries, the current situation in Taiwan is also mentioned. Special attention is paid to the possibility and difficulties of employing RBI&M in Taiwan’s power and process plants. The difficulties in adopting existing risk-based approaches to inspect and manage Kaohsiung’s underground pipelines are pointed out in particular. Comments are made at the end of the article.     

Establishment and Analysis of the Fault Tree for the Oil Transfer Pump System in CNPC Work Zone

The reliability of the equipment is very important for the large petrochemical industry, especially for oil pump as the core component of driving equipment. In order to reduce the loss of the enterprise brought by equipment failure, it is need to find those reasons which may lead to equipment failure and take some preventive measures as early as possible. This article analyzes the failure of the oil transfer pump system in CNPC work zone systematically, qualitatively and quantitatively, using the fault tree analysis method. Then 105 groups of minimal cut sets are found, and the probability of system failure after a certain time operation is calculated by using Weibull distribution. Combined with specific requirements of reliability, the work zone may make a scientific decision of plant maintenance cycle according to the conclusion.     

设备管理及维护信息系统的开发与应用

设备管理及维护信息系统是预防性设备维护的重要基础,对于机械制造企业提高设备管理水平和维护工作效率有重要意义。通过对企业实际情况的深入调查,分析了企业设备管理的信息需求及维护工作业务流程,开发了设备管理及其维护信息系统。该软件系统主要包括系统管理、设备维修资源管理、设备故障信息管理、维修工作管理、维修决策和维修数据分析六部分。该系统已在汽车零部件制造企业中实施。     

Earthquake hazards for cross-country pipelines

A review of the current state-of-knowledge in risk-based earthquake-resistant design of pipeline systems is presented. Damage to pipelines during past earthquakes is studied, and the necessity of evaluating the risk to such systems from earthquakes for their economic design is illustrated. The various aspects of seismic risk analysis of pipeline systems are briefly studied and a conclusion reached that given a tectonic and seismic data for the region in which pipelines lie, in addition to proper definition of various levels of pipeline unserviceability, it is possible to estimate the earthquake hazards to such pipelines with a reasonable degree of accuracy. Since this subject is of topical interest to this country, with increasing number of pipeline systems laid in earthquake-prone areas, specific areas are identified where more information is required before meaningful seismic risk analysis of pipelines can be done in India.     

Development and in vitro evaluation of pH-independent release matrix tablet of weakly acidic drug valsartan using quality by design tools

Abstract

The main objective of this study was the development of pH-independent controlled release valsartan matrix tablet in Quality by design (QbD) framework. The quality target product profile (QTPP), critical quality attributes (CQAs) and critical material attributes (CMAs) were defined by science and risk-based methodologies. Potential risk factors were identified with Fishbone diagram. Following, CMAs were further investigated with a semi-quantitative risk assessment method, which has been revised with mitigated risks after development and optimization studies. According to defined critical material attributes, which one of them was determined to be the dissolution, formulation optimization study was performed by using a statistical design of experiment. Formulation variables have been identified and fixed first with a ‘One factor at a time (OFAT)’ approach. After OFAT studies, a statistical experimental design was conducted with the most critical material attributes. Statistical design space and mathematical prediction equations have been developed for dissolution and hardness, which is important to predict drug dissolution behavior. In conclusion, a pH-independent release has been achieved for weakly acidic drug valsartan with a deeper understanding of drug product quality, with the science and risk-based approaches of QbD tools.     

Designing data warehouses for equipment management system with genetic algorithms

Effective equipment management is becoming one of the key factors in keeping a competitive advantage in the dynamic business environment since equipment is an important asset for manufacturing companies. Nowadays, maintenance administration has become one of the most important tasks in equipment management, particularly in manufacturing industries. Equipment management system (EMS) aims at reducing maintenance cost and production loss caused by machine breakdown. In addition, EMS can assist equipment engineers to make the right maintenance decisions at the right time, and at the right shop floor. Traditional computerized maintenance management systems (CMMS) have helped equipment engineers to deal with maintenance operations, but they lack decision support capability. In this paper, we design a data warehouse (DW) for EMS to help equipment engineers make maintenance decisions with various equipment related dimensions to improve effectiveness. A set of cubes can be built from EMS DW for the purpose of decision-making. In order to achieve a reasonable query response time under the memory space limit, a mechanism of partial materialization based on genetic algorithms (GAs) is adopted to design data cubes in the EMS DW. From the computational results the proposed GA-based approach for cube design can be applied to effectively select the appropriate multi-dimensional views for equipment management.