Design of Intelligent Fault Diagnostic System (FDS)

This research work presents useful framework and mechanism for integrated fault diagnostic system, or FDS. The proposed system is composed of three major subsystems: fault detection, root cause and consequence analyzer, and maintenance analyzer. Learning mechanisms are proposed to extract knowledge about deviations/failure modes from real time process and equipment monitoring data. Fault semantic network is proposed to represent failure modes and fault propagation models as integrated with process and equipment models. Qualitative rules are defined and associated with fault semantic networks for practical Actual maintenance findings are used to tune training data for more accurate fault detection and root cause and consequence analysis. Case study is used to illustrate the proposed idea.     

Pipeline leakage detection and isolation: An integrated approach of statistical and wavelet feature extraction with multi-layer perceptron neural network (MLPNN)

Leakage diagnosis of hydrocarbon pipelines can prevent environmental and financial losses. This work proposes a novel method that not only detects the occurrence of a leakage fault, but also suggests its location and severity. The OLGA software is employed to provide the pipeline inlet pressure and outlet flow rates as the training data for the Fault Detection and Isolation (FDI) system. The FDI system is comprised of a Multi-Layer Perceptron Neural Network (MLPNN) classifier with various feature extraction methods including the statistical techniques, wavelet transform, and a fusion of both methods. Once different leakage scenarios are considered and the preprocessing methods are done, the proposed FDI system is applied to a 20-km pipeline in southern Iran (Goldkari-Binak pipeline) and a promising severity and location detectability (a correct classification rate of 92%) and a low False Alarm Rate (FAR) were achieved.     

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.     

基于概率神经网络的液氨汽车罐车复合故障诊断

故障诊断在保证危险化学品汽车罐车运输安全方面具有重要意义。从国内交通运输安全的实际要求出发,依据液氨汽车罐车的结构特点及国家法律法规的要求,比较全面、系统地分析了液氨汽车罐车故障特征的相关参数,并将其作为概率神经网络的输入结点。根据实际可能发生的故障分类模式,考虑到故障诊断的容错能力和自适应能力,提出了基于概率神经网络的复合故障诊断模型。利用指标参数作为网络训练样本,对未知故障模式进行诊断,并以广西地区压力容器检验所液氨检测数据为例进行说明。理论分析和实例计算表明,该模型物理概念清晰,计算结果合理,精度较高,在危险化学品汽车罐车故障诊断中有很好的适用性。该项工作可为我国危险化学品汽车罐车故障智能诊断的深入开展提供参考依据。     

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.     

A new multiple-risk map approach to solve process plant layout considering safety and economic aspects

Mathematical models used to optimize the process plant layout (PPL) with risk reduction have four primary objectives, which are related to the minimization of land, pumping (pipe system), protection system devices, and risk costs. Moreover, these models are of two types: continuous plane models (CPM) and grid-based models (GBM); however, the nonconvexity of the CPM models makes difficult to achieve the global optimum, because it is formulated as Mixed-Integer Nonlinear Programming (MINLP). Thus, the risk map approach has been implemented with the grid-based models to solve problems of process plant layout focused on finding the best possible solution. However, these risk map formulations present important limitations, mainly related with the use of protection devices and the occupied area. Therefore, a new GBM-MILP formulation is proposed to optimize the selection of protection devices and minimize the occupied area. The risk is reduced through the investment on safety devices instead of considering the increase of separation distances. The proposed model was used to solve the layout problem of an ethylene oxide process, and the results was compared with a process layout reported in the literature. The results show that the model can provide the best possible solution; however, the time spent in the calculation is considerably greater than that reported for continuous plane models. Finally, the model can be used by decision-makers to evaluate different layout options for several explosion scenarios, during the early stages of the plant design.     

A design method of a plant alarm system for first alarm alternative signals using a modularized CE model

Management of a plant alarm system has been identified as one of the key safety issues because of disasters caused by alarm floods. When a chemical plant is at abnormal state, an alarm system must provide useful information to operators as the third layer of an independent protection layer (IPL). Therefore, a method of designing a plant alarm system is important for plant safety. Because the plant is maintained in the plant lifecycle, the alarm system for the plant should be properly managed through the plant lifecycle. To manage changes, the design rationales of the alarm system should be explained explicitly. This paper investigates a logical and systematic alarm system design method that explicitly explains the design rationales from know-why information for proper management of changes through the plant lifecycle. In the method, the module structure proposed by Hamaguchi et al. (2011) to assign a fault origin to be distinguished is extended. Using modules to investigate the sets of alarm sensors and the alarm limits setting for first alarm alternative signals to distinguish the fault origin, an alarm system design method is proposed. Also, the completeness of fault propagation for a branch of the cause–effect model as the plant model is explained. Using the modules and the set of fault origins to be distinguished by the alarm system, we try to explicitly explain the design rationales of the alarm system.     

Methodology for Computer-Aided Fault Tree Analysis

Fault tree analysis is a systematic, deductive and probabilistic risk assessment tool which elucidates the causal relations leading to a given undesired event. Quantitative fault tree (failure) analysis requires a fault tree and failure data of basic events. Development of a fault tree and subsequent analysis require a great deal of expertise, which may not be available all the time. Computer-aided fault tree analysis is an easy-to-use approach, which not only provides reliable results but also facilitates the validation and repeatability of the analysis. This enhances the overall results of the fault tree analysis and quantitative risk analysis.This paper presents a revised methodology for computer-aided fault tree analysis. The methodology includes fault tree development, minimal cutsets determination, cutsets optimization and probability analysis. The methodology uses advanced concepts of fault tree development and static and dynamic modularizing for complex and large fault trees. Furthermore, it enables sensitivity analysis of the system for design modification and risk-based decision making. Application of the proposed methodology to a process system is also discussed in the paper.     

An extension to Fuzzy Fault Tree Analysis (FFTA) application in petrochemical process industry

Fault Tree Analysis (FTA) is an established technique in risk management associated with identified hazards specific to focused fields. It is a comprehensive, structured and logical analysis method aimed at identifying and assessing hazards of complex systems. To conduct a quantitative FTA, it is essential to have sufficient data. By considering the fact that sufficient data is not always available, the FTA method can be adopted into the problems under fuzzy environment, so called as Fuzzy Fault Tree Analysis (FFTA). This research extends FFTA methodology to petrochemical process industry in which fire, explosion and toxic gas releases are recognized as potential hazards. Specifically, the case study focuses on Deethanizer failure in petrochemical plant operations to demonstrate the proposed methodology. Consequently, the study has provided theoretical and practical values to challenge with operational data shortage in risk assessment.     

Epistemic uncertainty in fault tree analysis approached by the evidence theory

Process plants may be subjected to dangerous events. Different methodologies are nowadays employed to identify failure events, that can lead to severe accidents, and to assess the relative probability of occurrence. As for rare events reliability data are generally poor, leading to a partial or incomplete knowledge of the process, the classical probabilistic approach can not be successfully used. Such an uncertainty, called epistemic uncertainty, can be treated by means of different methodologies, alternative to the probabilistic one. In this work, the Evidence Theory or Dempster–Shafer theory (DST) is proposed to deal with this kind of uncertainty. In particular, the classical Fault Tree Analysis (FTA) is considered when input data are supplied by experts in an interval form. The practical problem of information acquisition from experts is discussed and two realistic scenarios are proposed. A methodology to propagate such an uncertainty through the fault tree up to the Top Event (TE) and to determine the belief measures is supplied. The analysis is illustrated by means of two simple series/parallel systems. An application to a real industrial safety system is finally performed and discussed.     

Evaluation of the Risk OMT model for maintenance work on major offshore process equipment

Operational safety is receiving more and more attention in the Norwegian offshore industry. Almost two thirds of all leaks on offshore installations in the period 2001–2005, according to the Risk Level Project by the Petroleum Safety Authority in Norway, resulted from manual operations and interventions, as well as shut-down and start-up. The intention with the Risk OMT (risk modelling – integration of organisational, human and technical factors) program has been to develop more representative models for calculation of leak frequencies as a function of the volume of manual operations and interventions. In the Risk OMT project a generic risk model has been developed and is adapted to use for specific failure scenarios. The model considers the operational barriers in event trees and fault trees, as well as risk influencing factors that determine the basic event probabilities in the fault trees. The full model, which applies Bayesian belief networks, is presented more thoroughly in a separate paper. This paper presents the evaluation of the model. The model has been evaluated through some case studies, and one important aspect is the evaluation of the importance of each risk influencing factor. In addition some risk-reducing measures have been proposed, and the paper presents how the effect of these measures has been evaluated by using the model. Finally, possible applications and recommendations for further work are discussed.     

Bi-directional connectivity diagram for accident propagation analysis considering the interactions between multiple-process units

An incident may propagate to an accident with different severity dependent on its propagation scenarios. Since the accident propagation is a two-way process, the current research is focusing on the one-way analysis. This paper aims to analyze the combined effect of multi-units sources and their interactions during the accident propagation. The bi-directional connectivity diagram (BDCD) is applied to visualize the interactions between multiple process units as hazardous sources. The deployed safety barriers interrupt the connection between the hazardous sources and thus minimize the influence of one BDCD node on another. Through which, the accident propagation is reduced. The proposed method can be suitable to the general accidents, and it is applied to a case study of the LNG terminal station to assess the potential consequences of explosion caused by the leakage, in which the cost of the safety barrier is also considered. The BDCD approach is found more effective than traditional single-hazardous source methods for analyzing the accident propagation of multi-units sources in the chemical plant and achieving intrinsic safety.     

Traffic accident reconstruction and an approach for prediction of fault rates using artificial neural networks: A case study in Turkey

Objective: Currently, in Turkey, fault rates in traffic accidents are determined according to the initiative of accident experts (no speed analyses of vehicles just considering accident type) and there are no specific quantitative instructions on fault rates related to procession of accidents which just represents the type of collision (side impact, head to head, rear end, etc.) in No. 2918 Turkish Highway Traffic Act (THTA 1983). The aim of this study is to introduce a scientific and systematic approach for determination of fault rates in most frequent property damage–only (PDO) traffic accidents in Turkey.

Methods: In this study, data (police reports, skid marks, deformation, crush depth, etc.) collected from the most frequent and controversial accident types (4 sample vehicle–vehicle scenarios) that consist of PDO were inserted into a reconstruction software called vCrash. Sample real-world scenarios were simulated on the software to generate different vehicle deformations that also correspond to energy-equivalent speed data just before the crash. These values were used to train a multilayer feedforward artificial neural network (MFANN), function fitting neural network (FITNET, a specialized version of MFANN), and generalized regression neural network (GRNN) models within 10-fold cross-validation to predict fault rates without using software. The performance of the artificial neural network (ANN) prediction models was evaluated using mean square error (MSE) and multiple correlation coefficient (R).

Results: It was shown that the MFANN model performed better for predicting fault rates (i.e., lower MSE and higher R) than FITNET and GRNN models for accident scenarios 1, 2, and 3, whereas FITNET performed the best for scenario 4. The FITNET model showed the second best results for prediction for the first 3 scenarios. Because there is no training phase in GRNN, the GRNN model produced results much faster than MFANN and FITNET models. However, the GRNN model had the worst prediction results. The R values for prediction of fault rates were close to 1 for all folds and scenarios.

Conclusions: This study focuses on exhibiting new aspects and scientific approaches for determining fault rates of involvement in most frequent PDO accidents occurring in Turkey by discussing some deficiencies in THTA and without regard to initiative and/or experience of experts. This study yields judicious decisions to be made especially on forensic investigations and events involving insurance companies. Referring to this approach, injury/fatal and/or pedestrian-related accidents may be analyzed as future work by developing new scientific models.     

基于IRML网络模型的LNG卸料系统故障传播分析

为研究陆地LNG卸料系统的物理设备、信息网络及人员操作的依赖关系和信息层、人员层对设备层故障传播的影响,基于面向基础设施弹性建模语言(Infrastructure Resilience-Oriented Modelling Language,IRML),从单层网络静态风险分析和多层依赖网络的动态传播2个方面,提出LNG...     

基于情景的建筑火灾风险分析方法及CFAST软件应用

提出基于情景的建筑火灾风险分析方法,它通过3个步骤即起火分析、确定未来情景集和确定风险分析结果来计算建筑物火灾风险大小。从一个建筑单体出发,通过过程模拟的手段得到其未来可能会出现的各种"情景",计算各"情景"中的指标,通过综合分析得到风险分析的结果。并通过算例说明了基于情景的火灾风险分析方法的实现过程。最后通过分析北京市近几年的火灾历史数据、设定影响火灾发展的各种因素和CFAST软件模拟的手段,得到建筑火灾年期望损失率作为风险的度量值。     

Safety assessment of natural gas storage tank using similarity aggregation method based fuzzy fault tree analysis (SAM-FFTA) approach

Fault tree analysis (FTA) is an important method to analyze the failure causes of engineering systems and evaluate their safety and reliability. In practical application, the probabilities of bottom events in FTA are usually estimated according to the opinions of experts or engineers because it is difficult to obtain sufficient probability data of bottom events in fault tree. However, in many cases, there are many experts with different opinions or different forms of opinions. How to reasonably aggregate expert opinions is a challenge for the engineering application of fault tree method. In this study, a fuzzy fault tree analysis approach based on similarity aggregation method (SAM-FFTA) has been proposed. This method combines SAM with fuzzy set theory and can handled comprehensively diverse forms of opinions of different experts to obtain the probabilities of bottom events in fault tree. Finally, for verifying the applicability and flexibility of the proposed method, a natural gas spherical storage tank with a volume of 10,000 m³ was analyzed, and the importance of each bottom event was determined. The results show that flame, lightning spark, electrostatic spark, impact spark, mechanical breakdown and deformation/breakage have the most significant influence on the explosion of the natural gas spherical storage tank.     

广东科龙集团无氟生产线预混站燃烧爆炸风险分析

广东科龙集团无氟生产线中的预混站是使用环戊烷危险物品配制发泡剂的重大危险源,燃烧爆炸事故风险较大。笔者针对该生产场所重大危险源,应用事故树分析方法,编制了预混站的燃烧爆炸事故树,计算出事故能量及伤害后果。通过对事故树的定性和定量分析,得出事故树的最小割集和最小径集,判定事故发生的可能途径,选定预防事故发生的最佳方案。针对导致事故发生的可能原因组合,制定有效的安全技术措施和管理制度,为国内同类企业预防该类事故提供依据。     

Model selection and fault detection approach based on Bayes decision theory: Application to changes detection problem in a distillation column

The fault detection of industrial processes is very important for increasing the safety, reliability and availability of the different components involved in the production scheme. In this paper, a fault detection (FD) method is developed for nonlinear systems. The main contribution consists in the design of this FD scheme through a combination of the Bayes theorem and a neural adaptive black-box identification for such systems. The performance of the proposed fault detection system has been tested on a real plant as a distillation column. The simplicity of the developed neural model of normal condition operation, under all regimes (i.e. steady-state and unsteady state), used in this case is realised by means of a NARX (Nonlinear Auto-Regressive with eXogenous input) model and by an experimental design. To show the effectiveness of proposed fault detection method, it was tested on a realistic fault of a distillation plant of laboratory scale.     

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.