Performance Of Safety Instrumented Systems 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.

Performance analysis of safety instrumented systems against cascading failures during prolonged demands

Cascading failures may occur in many technical systems where the failure of one component triggers successive events. Safety barriers like safety instrumented systems are installed in many industries to prevent failures and failure propagations. However, little attention has been paid to the impacts of safety instrumented systems employed to prevent cascading failures in the literature. This paper proposes a novel method for analyzing how the performance of safety instrumented systems influences the protection against and mitigation of cascading failures. It considers SIS reliability and SIS durability in the mitigation of cascading failures. The method uses recursive aggregations based on the reliability block diagram and is verified with Monte Carlo simulations. The application is illustrated with a practical case study, where the proposed method is found beneficial to identify the criticality of safety instrumented systems in consideration of their locations and performance.

Dynamic Risk Analysis from the Perspective of Life Cycle Approach in Iec 61508 and Iec 61511

Dynamic risk analysis (DRA) aims to provide updated risk levels during operations of a hazardous facility. One of the main objectives of performing a DRA is to support day-to-day operational decisions, primarily for preventing major accidents. For this reason, many DRA methods have been developed to include information about the status of the safety barriers whose failures can increase the likelihood of a major accident. However, DRA is not widely used in industry, and there is no standard that describes DRA approaches and their applications. It may therefore be of interest to consider similar concepts and methods addressed in the existing standards. This paper focuses on a specific type of safety barriers, safety instrumented systems (SISs), and recognized functional standards IEC 61508 and IEC 61511 that give performance requirements to a SIS. In particular, SIS performance monitoring in the operational phase according to IEC 61508/61511 can provide valuable inputs to DRA applications.

Slaying the dragons that sabotage protection measures to feed on the ash of your plant

AbstractDragons are legendary winged monsters that breathe fire and feed on ash. Or so we think. This is a tale of three dragons that have evolved to hide in plain sight and quietly feed on the underpinnings of protection layers, eroding the effectiveness of process safety mitigation measures. These dragons are mischievous and feed off process upsets, near misses, and accidents. The Invisible Dragon roams around the plant breaking devices and causing people to make mistakes that set off alarms and causes safety instrumented systems (SIS) trips to initiate. Its special joy is creating initiating events that were not considered in the design. Tiny and Giant Dragons specialize in defeating the SIS protection layers, so that all three dragons can feed on the ash of an accident. The Tiny Dragon baits SIS designers with devices having extremely low failure rate data causing the humans to believe safety instrumented functions (SIF) are more reliable than they actually are. The Giant Dragon works alongside the Tiny Dragon to further undermine the SIS with its song of near perfect proof testing. This paper will illustrate the impacts of underestimating SIF demand rate, using overly optimistic failure rate data, and overestimating proof test coverage. We will discuss techniques to identify and correct these issues and suggest metrics to monitor SIS performance thus slaying the dragons.

Generalized markovian consideration of common cause failures in the performance assessment of safety instrumented systems

Aiming to provide a generalized method for assessing the performance of safety instrumented systems with a flexible and accurate consideration of the common cause failures’ contribution. This paper is devoted to the development of a direct way to generate the transition rate matrix associated with the continuous-time Markov model of any typical KooN architecture using any parametric model. Such a choice is considered after a detailed comparison of the ability of several dependability methods (e.g., fault trees, reliability block diagrams, Markov models, Bayesian networks, etc) to provide simple representations and genuine results in this context. To validate the developed method, the unavailability and the unconditional failure intensity of a wide range of configurations are quantified using the Binomial Failure Rate model and compared to those of the complete fault tree implementation.

Effects of proof tests on the safety performance of safety-instrumented systems

Effects of proof tests on the safety performance of safety-instrumented systems

Effects of proof tests on the safety performance of safety-instrumented systems

The imperfection in the values of the average probability of failure on demand (PFDavg) creates uncertainty about the effectiveness of safety-instrumented system (SIS). To overcome this problem, many parameters such as dangerous failures, common cause failures, diagnostic coverage rate and proof tests are taken into consideration. In order to emphasise the importance of the proof tests and show their effects on the safety performance of the SIS, a new analytical formula is developed in this study. The impact of these tests on the SIS's allocation of the safe integrity level (SIL) is shown through the results obtained in the present research.

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.

How to monitor your safety instrumented system performance

With the impending release of Edition 2 of the International Electrotechnical Commission (IEC) Standard 61511 in Australia, it is timely to revisit the topic of safety instrumented system (SIS) performance monitoring. Operators need to monitor the performance of their SISs to ensure that design assumptions are correct. Any incorrect assumption or error introduced during operation can result in an unsafe plant situation. Questions to consider include: how do operators know their plants are adequately protected; are protection systems performing as required, and do operators need to re-assess the safety of their plant’s process vis-à-vis the implemented safety instrumented systems; what are end-user obligations; how and is it possible to meet these obligations; and what additional requirements are there, and how much more prescriptive is Edition 2? If any of the above questions are of concern, or if plant personnel are aware of their obligation to maintain and monitor the SIS during operations but are still not 100% sure if they are on the right track or need to do more, then this paper will prove useful. In addition to knowing all the requirements to comply, actual implementation and maintenance of a program to monitor the performance of SIS can prove overwhelming. Not only do you need to be adept in maintaining a database, but you also need to understand how to consolidate various critical information sources in order to make sense of all the data collected. Fortunately, there are solutions in the market to help end-users overcome these hurdles to effectively monitor SIS performance. This paper looks at some key areas that a complete solution should cover and what some of the features of such a solution are.

Dealing with uncertainty in effect analysis of test strategies on safety instrumented system performance

In this paper, we have attempted to show the effect of test strategies on the performance of safety instrumented system (SIS) in a fuzzy parameter environment. Two types of test strategies are considered: simultaneous and staggered tests. As a relevant parameters of SIS, common cause failure factor and diagnostic coverage ( $$DC$$ ) rate are modeled by triangular fuzzy numbers and are injected into the fuzzy Markov model to calculate the fuzzy PFD of SIS. Sensitivity analysis is carried out to show the effect of fuzzy beta and $$DC$$ variation on average fuzzy PFD of the SIS. An application case focused on a high integrity pressure protection system is carried out.

Generalized analytical expressions for safety instrumented systems' performance measures: PFDavg and PFH

Safety Instrumented Systems (SIS) constitute an indispensable element in the process of risk reduction for almost all of nowadays' industrial facilities. The main purpose of this paper is to develop a set of generalized and simplified analytical expressions for two commonly employed metrics to assess the performance of SIS in terms of safety integrity, namely: the Average Probability of Failure on Demand (PFDavg) and the Probability of Dangerous Failure per Hour (PFH). In addition to the capability to treat any K-out-of-N architecture, the proposed formulas can smoothly take into account the contributions of Partial Stroke Testing (PST) and Common Cause Failures (CCF). The validity of the suggested analytical expressions is ensured through various comparisons that are carried out at different stages of their construction.

Safety and operational integrity evaluation and design optimization of safety instrumented systems

The control of risks generated by modern industrial facilities could not be guaranteed without the use of safety instrumented systems (SIS). The failure of SIS to achieve their assigned functions could result in huge consequences with respect to both (i) the safety of the monitored system (relating to the SIS safety integrity) as well as (ii) its production availability due to false trips (relating to the SIS operational integrity). Furthermore, these two aspects are usually antagonistic. Therefore, the assurance of this double performance comes first by a thoughtful design of SIS. In that case, the aim of this paper is twofold. First, it focuses on the establishment of generic analytical formulations allowing the assessment of the SIS performance regarding safety integrity and operational integrity. Second, it deals with SIS architecture design optimization. The optimization problem is firstly addressed by a preliminary search for a balance between the above two quantities relying on the analysis of the structure of KooN architectures. Then, a more general and suitable approach based on genetic algorithms is proposed, where several performance indicators and the costs of purchase and maintenance are expected to be considered simultaneously. This general approach is illustrated through an application example.

Switching Markov chains for a holistic modeling of SIS unavailability

This paper proposes a holistic approach to model the Safety Instrumented Systems (SIS). The model is based on Switching Markov Chain and integrates several parameters like Common Cause Failure, Imperfect Proof testing, partial proof testing, etc. The basic concepts of Switching Markov Chain applied to reliability analysis are introduced and a model to compute the unavailability for a case study is presented. The proposed Switching Markov Chain allows us to assess the effect of each parameter on the SIS performance. The proposed method ensures the relevance of the results.

Fuzzy multiphase Markov chains to handle uncertainties in safety systems performance assessment

In this article, we address the problem of imprecision in assessing the performance of safety instrumented systems (SIS) using fuzzy multiphase Markov chains. The elementary probabilities usually considered in Markov chains are replaced by fuzzy numbers. It allows experts to express their uncertainty concerning the basic parameters of systems and to evaluate the impact of this uncertainty on the SIS performance. We show how the imprecision induces significant changes on the Safety Integrity Level of the SIS. The proposed method ensures the relevance of the results. This is validated by a comparison with the results of an enhanced Markov Analysis.

Uncertainty analysis of common cause failure in safety instrumented systems

This paper analyses the problem of epistemic uncertainty in assessing the performance of safety instrumented systems (SIS) using fault trees. The imperfect knowledge concerns the common cause failure (CCF) involved in the SIS in low demand mode. The point-valued CCF factors are replaced by fuzzy numbers, allowing experts to express their uncertainty about the CCF values. This paper shows how these uncertainties propagate through the fault tree and how this induces an uncertainty to the values of the SIS failure probability on demand and to the safety integrity level of the SIS. For the sake of verification and comparison, and to show the exactness of the approach, a Monte Carlo sampling approach is proposed, where by a uniform or triangular second-order probability distribution of CCF factors is considered.

Uncertainty evaluation of Safety Instrumented Systems by using Markov chains

AbstractIn this article, the problem of imprecision in assessing the performance of Safety Instrumented Systems (SIS) using fuzzy Markov chains is adressed. The scalar elementary probabilities usually considered in Markov chains are replaced by fuzzy numbers. It allows experts to express their uncertainty concerning the basic parameters of systems (common cause factors, failure rate …) and, to evaluate the impact of the uncertainty. We show how the imprecision induces significant changes on the Safety Integrity Level (SIL) of a SIS. The proposed method ensures the relevance of the results. It is affirmed by the comparison with results obtained by a Monte Carlo sampling approach.

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.

Human and organisational factors in the operational phase of safety instrumented systems: A new approach

The international standards IEC 61508 and IEC 61511, which provide a general framework for the design and implementation of safety instrumented systems, require quantification of the achieved risk reduction, expressed as a safety integrity level (SIL). Human and organisational factors affect the performance of safety instrumented systems during operation and may threaten the achieved SIL, but this is usually not explicitly accounted for. This article presents a new approach to address human and organisational factors in the operational phase of safety instrumented systems. This approach gives a prediction of the operational SIL and can also be used to improve safety. It shows which human and organisational factors are most in need of improvement and it provides guidance for preventive or corrective action. Finally, the approach can be used as part of a SIL monitoring strategy in order to maintain the achieved SIL at the required level during the operational phase.

Fault management analysis

AbstractIn the chemical process industries, intrinsically safe operations must be the process engineer's goal, but, in reality, it is often not feasible, either technically or economically. In these cases, safety instrumented systems (SIS) are often used to detect and respond to process risk. SISs have many components that must work as designed in order to mitigate potential hazardous incidents. Over the years, many of these components have evolved from simple electro‐mechanical devices to complex mechanical, electrical, and programmable devices. What makes these devices fail must be examined in order to understand how each component failure can propagate into fail‐safe and fail‐dangerous conditions.This paper proposes the use of fault management analysis (FMA) for the assessment of proposed SIS designs. An FMA identifies not only the failure modes of each component, but also determines how to appropriately manage the identified failure. This paper demonstrates how proposed design, diagnostics, inspection, maintenance, and testing programs should be modified to improve the SIS performance.