Safety Instrumented Systems Reliability Research Articles

Towards standardized reporting and failure classification of safety equipment: Semi-automated classification of failure data for safety equipment in the operating phase

Safety instrumented systems (SISs) are installed on process plants to protect against undesired events like e.g., gas leakage and overpressure. A SIS has reliability requirements that are determined during design, and conformance to these requirements should be verified during operation. It is therefore important that all SIS failures are recorded and classified according to their impact on the SIS reliability. Failures of SIS equipment classified as dangerous undetected are of particular interest because they are dormant (undetected) and will prevent the execution of the safety function (dangerous). Analysis of the failure mode and detection method is essential when deciding if a failure is dangerous and undetected. Such information is often provided as unstructured text in notifications registered into the maintenance management system. Therefore, the work of classifying failures requires considerable manual effort in reading and analyzing the texts. Approaches within natural language processing, like technical language processing, have the potential to be deployed more actively for this purpose. However, successful adoption relies on groundwork where classification rules are derived from international standards and commonly agreed industry practice. This paper presents a semi-automated process that incorporates classification rules and gives examples that indicate some of the capabilities of technical language processing for failure classification. The paper also elaborates on how the work relates to Industry 4.0 in creating digital representations to monitor the performance of safety instrumented systems. This work has been carried out as part of the APOS project (Automated process for follow-up of safety instrumented systems). The APOS project has developed knowledge and specifications that simplify and automate the design and operation of safety equipment and investigated how the failure classification process can be made more efficient.

Fuzzy Reliability Assessment of Safety Instrumented Systems Accounting for Common Cause Failure

Reliability of safety instrumented systems (SISs) is a critical measure to ensure production safety of many industries. This paper focus on low-demand SISs. The reliability of these SISs is quantified by evaluating their probability of failure on demand (PFD). However, due to lack of knowledge, and/or vague judgments from experts, epistemic uncertainty associated with the parameters of components' degradation models is inevitable. Meanwhile, common cause failure (CCF) of two or more components caused by shared environments, often exists in SISs, reliability assessment for a SIS, therefore, becomes a challenging task. In this paper, fuzzy reliability assessment for SISs is conducted by taking account of the CCF among components of a SIS. The fuzzy Markov model is utilized to characterize the degradation process of components under fuzzy environment. The fuzzy state probability distribution of components is, then, calculated by formulating a set of constrained optimization models. Based on the fault tree of a SIS, the fuzzy PFD of the entire SIS with CCFs is formulated by using the $\beta $ -factor to quantify the CCFs. The fuzzy PFD at any $\alpha $ -cut level is, therefore, computed by a constrained optimization model. According to the optimization of parameters of SIS, the lower bound and upper bound of fuzzy PFD of SIS can be determined. Finally, we can quantify the effectiveness of CCF event for assessing the fuzzy PFD of SIS.

Performance analysis of redundant safety-instrumented systems subject to degradation and external demands

Safety-instrumented systems (SISs) play a vital role in preventing hazardous events in the offshore facilities. Many of existing performance analysis of SISs are based on the constant failure rate assumption, which is however doubtful when it is applied to actuator sub-systems or mechanical final elements of a SIS. These mechanical SIS components can become vulnerable with time and with upcoming demands given the past exposures to shocks/demands. In this paper, we analyze SIS reliability and unavailability by considering that a failure occurs when total degradation of a SIS component, including continuous degradation and increments caused by random demands, exceeds to a predefined critical threshold. The dependency of two components in a redundant structure of mechanical actuators caused by random demands is also taken into account in the analysis. Approximation formulas for reliability and unavailability of the redundant SIS sub-system under a degradation process are developed. Finally, a numerical example is conducted to illustrate effects of degradation parameters on SIS performance.

Modeling patterns for reliability assessment of safety instrumented systems

Safety Instrumented Systems (SIS) act as crucial safety barriers for preventing hazardous accidents in the industrial systems. It is therefore of primary importance to study their reliability, i.e. eventually to design probabilistic reliability assessment models. SIS have common behaviors such as the periodic test policies to reveal the dangerous undetected failures. These common behaviors can be captured in models via modeling patterns. By reusing modeling patterns, the modeling process can be simplified and made more efficient.In this paper, we propose a versatile set of modeling patterns implemented in AltaRica 3.0 language. We apply them to assess the reliability of SIS described in ISO technical report ISO/TR 12489. Comparisons are performed between the results obtained from AltaRica models and those reported in ISO/TR 12489. We show that the set of proposed modeling patterns can serve as an effective tool to model SIS in a modular way.

Reliability assessment for final elements of SISs with time dependent failures

Reliability assessment for safety-instrumented systems (SISs) plays a vital role in improving the design of SISs. Traditional methods for SIS reliability assessment that assume constant failure rates are, however, not realistic for many final elements of SISs, e.g. electro-mechanical and hydraulic/mechanical actuators that are subject to degradation. This paper presents an approach for reliability assessment of SIS final elements with time dependent failure rates. Different operational issues, such as partial and full testing, are investigated for their effects on reliability of SISs. Approximation formulas for evaluation of average probability of failure on demand (PFDavg) involving degradation are developed within different subsequent proof testing intervals, and Weibull distributions are adopted to model the degradation processes of the final elements. The corresponding numerical results of PFDavg from the set of the derived formulations are validated by Petri nets models that are developed for different scenarios. Shutdown valves installed as part of a high integrity pressure protection system are analyzed as the case, to illustrate the feasibility of the proposed approach, and also demonstrate that the approximation can provide possibilities for testing strategies design and optimization.

Optimization of Maintenance Schedule for Safety Instrumented Systems

Preventive maintenance plays an important role in the reliability of safety instrumented systems (SIS). In this paper, an approach is proposed for the optimization of maintenance schedule of SIS. The basic idea is to regard a SIS with preventive maintenance tests as a switched linear system with state jumps. Then the problem of finding the optimal time instants for maintenance tests is formulated as an optimal control problem of Bolza form. The average probability of failure on demand (PFD) is taken as the objective function. The proposed approach is able to take into account different types of maintenance tests and redundancy architecture. Examples are given to illustrate the proposed approach.

How to classify failures when collecting data for safety-instrumented systems in the oil and gas industry

The oil and gas industry places a high value on achieving high reliability and availability on safety critical equipment. To achieve this, assessments of the reliability performances of such equipment are required, both before and during the production phase. The fact that the reliability data available to support the assessments is often sparse or insufficiently detailed presents a challenge. These assessments also typically require insights into the system in which the equipment is used and information about failure detection. However, this ensemble information is often difficult to achieve in the way the data are collected today. As a response to this challenge, one suggested option is to collect reliability data using one acknowledged failure mode classification specifically designed to assess the reliability of safety-instrumented systems. This is a classification adopted from the International Electrotechnical Commission standard 61508. In this article, we discuss the pros and cons of adopting this failure mode classification in generic reliability data collection in the oil and gas industry. One argument discussed is that the data may lack relevant information about the associated safety system and thus be valid for a specific system only, not for generic equipment and systems in general. Hence, should the classification be implemented, the collected data should be used with care.

Safety instrumented systems in lieu of pressure relief valves

There are two aspects to risk assessments: safety issues and regulatory issues. Usually the two are aligned, but not always. This article explains ASME B&PVC Section VIII (Sec VIII) [American Society of Mechanical Engineers, 2013 ASME Boiler & Pressure Vessel Code: Section VIII, Division I. s.l.] requirements along with other industry guidance for using Safety Instrumented Systems (SIS) in lieu of pressure relief devices (e.g., pressure relief valves or rupture disks). For most pressure vessels in petrochemical installations in the United States, the Sec VIII requirements are regulatory requirements. A designer that is implementing facility changes from a corporate risk assessment (e.g., Layer of Protection Analysis or Hazard and Operability Study (HAZOP)) would do well to also meet the minimum regulatory requirements for overpressure in Sec VIII. Typical risk assessments allow the designer to consider both the likelihood and consequences of an overpressure scenario in determining if additional mitigation is required. Whereas, Sec VIII does not allow any credible overpressure scenarios (solely based on likelihood) unless there is adequate capacity provided by a relief device. This article discusses the detailed typical risk analysis compared to the requirements of Sec VIII overpressure protection by system design. The SIS reliability rating or determination of said rating (e.g., SIL‐3 Systems) is outside the scope of this article. © 2014 American Institute of Chemical Engineers Process Saf Prog 33: 345–349, 2014

Reliability of safety-instrumented systems subject to partial testing and common-cause failures

Partial testing is sometimes used as a supplement to proof testing to improve the reliability of safety-instrumented systems (SISs) in low-demand mode of operation. This paper studies the effect of partial testing on SIS reliability. Simplified formulas are developed to include both partial and proof testing in the calculation of the average probability of failure on demand (PFDavg). The proposed formulas can handle situations where partial testing is performed periodically and non-periodically. Common-cause failures (CCFs) are treated by using the beta-factor model, and different β-factors can be included for different failure modes. The proposed formulas are compared with existing results for partial verification. A case study is presented to demonstrate the applicability. The proposed formulas can serve as a valuable tool for selecting a cost-effective strategy for partial testing.

Monitoring human and organizational factors influencing common-cause failures of safety-instrumented system during the operational phase

Safety-instrumented systems (SISs) are important safety barriers in many technical systems in the process industry. Reliability requirements for SISs are specified as a safety integrity level (SIL) with reference to the standard IEC 61508. The SIS reliability is often threatened by common-cause failures (CCFs), and the beta-factor model is the most commonly used model for incorporating the effects of CCFs. In the design phase, the beta-factor, β, is determined by answering a set of questions that is given in part 6 of IEC 61508. During the operational phase, there are several factors that influence β, such that the actual β differs from what was predicted in the design phase, and therefore the required reliability may not be maintained. Among the factors influencing β in the operational phase are human and organizational factors (HOFs). A number of studies within industries that require highly reliable products have shown that HOFs have significant influence on CCFs and therefore on β in the operational phase, but this has been neglected in the process industry. HOFs are difficult to predict, and susceptible to be changed during the operational phase. Without proper management, changing HOFs may cause the SIS reliability to drift out of its required value. The aim of this article is to highlight the importance of HOFs in estimation of β for SISs, and also to propose a framework to follow the HOFs effects and to manage them such that the reliability requirement can be maintained.

Reliability performance of safety instrumented systems: A common approach for both low- and high-demand mode of operation

Safety instrumented systems (SISs) are usually divided into two modes of operation, low-demand and high-demand. Unfortunately, this classification is not easy to justify and the available formulas that are used to quantify the reliability performance in these two modes of operation are unable to capture combined effects of functional testing, spurious activations, and successful responses to demands. This article discusses some important modeling issues for SIS reliability performance quantification, and demonstrates their implementation in a Markov model. The accuracy of the Markov model for a simple case study of a pressure transmitter is verified through comparison with a scenario-based formula, and it is shown that the Markov approach gives a sufficiently accurate result for all demand rates, covering both low- and high-demand modes of operation.

Reliability of safety instrumented systems: Where to direct future research?

AbstractReliability analysis is an important basis for decision making related to design and follow‐up of safety instrumented systems (SISs) in the process industry. A significant research activity has been devoted to this area and has formed the basis for the current analytical methods.Considering the challenges of operating process facilities, it is pertinent to raise the following questions: Have we reached the required level of knowledge? If not, where should we direct future research? This article draws the attention to these questions and outlines the position taken by the authors. The authors hope that this article will initiate further discussions among analysts and researchers having a special interest in SISs. © 2010 American Institute of Chemical Engineers Process Saf Prog 2010

Reliability assessment of safety instrumented systems subject to different demand modes

Safety instrumented systems (SISs) are commonly used in the process industry, to respond to hazardous events. In line with the important standard IEC 61508, SISs are generally classified into two types: low-demand systems and high-demand systems. This article explores this classification by studying the SIS reliability for varying demand rates, demand durations, and test intervals. The approach is based on Markov models and is exemplified by two simple system configurations. The SIS reliability is quantified by the probability of failure on demand (PFD) and the frequency of entering a hazardous state that will lead to an accident if the situation is not controlled by additional barriers. The article concludes that very low-demand systems are similar and may be treated as a group. The same applies to very high-demand system. Between these group, there is a rather long interval where the demand rate is neither high-demand nor low-demand. These medium-demand systems need a specific treatment. The article shows that the frequency of entering into a hazardous state increases with the demand rate for low-demand systems, while it is nearly independent of both the demand rate and the demand duration for high-demand systems. The PFD is an adequate measure for the SIS reliability for low-demand systems, but may be confusing and difficult to interpret for high-demand systems.