Sterilizer Reliability Analysis Using Reliability Block Diagram Based on Failure Identification Through Fault Tree Analysis

A hybrid method combining various Reliability (Fault Tree Analysis -FTA-, Reliability Block Diagrams -RBD), Multi Criteria Decision Making (Analytic Hierarchy Process -AHP), High Reliability Organisations (HROs) and Learning from Failures (LFF) framework tools, has been applied to a long-established pattern of small to medium chronic failures in a large Oil & Gas organisation. Through the Reliability techniques FTA/RBD, the root causes of the chronic failure pattern were identified, categorised and their interconnections established. The obtained root causes were then used as criteria and with the five principles of mindful organising as alternatives in constructing the AHP model. Finally, an attempt to extract useful generic lessons from the failure causes identified by applying the learning from failure (LFF) principles was made, in order to obtain guidelines for updating existent company routines. Despite a few shortcomings, some inherent to the tools themselves and some related to the method intent, reasonable results were obtained which appear to demonstrate the applicability of the approach. Abbreviations: AHP: analytical hierarchy process; CAPEX: capital expenditure; CBM: condition based maintenance; CTA: crisis tree analysis; CMMS: computerised maintenance management system; DMG: decision making grid; E&P: exploration and production; FTA: fault tree analysis; HRO: high reliability organisation; LFF: learning from failure; MCDM: multi criteria decision making; OPEX: operational expenditure; PSF: performance shaping factors; RACI: responsible accountable consulted informed; RBD: reliability block diagram; RBM: risk based maintenance.

This paper deals with the reliability assessment of electrical power supply systems under redundancy. For doing so, three probabilistic safety analysis (PSA) methods are proposed and developed namely fault tree analysis (FTA), reliability block diagram (RBD), and Monte Carlo Simulation (MC) method. The FTA method is performed using RiskSpectrumPSA® software and the RBD and MC methods are coded using Matlab software. The electrical power supply system reliability is analyzed and enhanced considering various cases of redundancy. The FTA method is based on qualitative and quantitative reliability assessment. The quantitative assessment calculates the failure probability of the top event, and the qualitative approach determines the minimal cut sets (MCS). However, the RBD and MC methods are proposed to compute the system failure probability. The considered methods are applied on three case studies and the obtained results are compared to each other. The obtained results show that the three methods provide almost the same results. Furthermore, the reliability of the electrical power supply is clearly enhanced with the number of redundant components.

PurposeThis paper aims to conduct a comprehensive fault tree analysis (FTA) on the critical components of industrial robots. This analysis is integrated with the reliability block diagram (RBD) approach to investigate the robot system reliability.Design/methodology/approachFor practical implementation, a particular autonomous guided vehicle (AGV) system was first modeled. Then, FTA was adopted to model the causes of failures, enabling the probability of success to be determined. In addition, RBD was used to simplify the complex system of the AGV for reliability evaluation purpose.FindingsHazard decision tree (HDT) was configured to compute the hazards of each component and the whole AGV robot system. Through this research, a promising technical approach was established, allowing decision-makers to identify the critical components of AGVs along with their crucial hazard phases at the design stage.Originality/valueAs complex systems have become global and essential in today’s society, their reliable design and determination of their availability have turned into very important tasks for managers and engineers. Industrial robots are examples of these complex systems that are being increasingly used for intelligent transportation, production and distribution of materials in warehouses and automated production lines.

This paper presents reliability, availability, and maintainability (RAM) analysis framework for evaluating the performance of a circulation system of water (WCS) used in a coal-fired power plant (CFPP). The performance of WCS is evaluated using a reliability block diagram (RBD), fault tree analysis (FTA), and Markov birth–death probabilistic approach. In this work, the system under study consists of five subsystems connected in series and parallel configuration namely condensate extraction pump (CEP), low-pressure feed water heater (LPH), deaerator (DR), boiler feed pump (BFP), high-pressure feed water heater (HPH). The reliability block diagram (RBD) and fault tree approach (FTA) have been employed for the performance evaluation of WCS. The Markov probabilistic approach based simulation model is developed. The transition diagram of the proposed model represented several states with full working capacity, reduced capacity, and failed state. The ranking of critical equipment is decided on the basis of criticality level of equipment. The study results revealed that the boiler feed pump affects the system availability at most, while the failure of deaerator affects it least. The availability of the system is optimized using the particle swarm optimization method. The optimized availability parameter (TBF, TTR) based modified maintenance strategy is recommended to enhance the availability of the plant system. The optimized failure rate and repair rate parameters of the subsystem are used to suggest a suitable maintenance strategy for the water circulation system of the thermal power plant. The proposed RAM framework helps the decision-makers to plan the maintenance activity as per the criticality level of subsystems and allocate the resources accordingly.

Component failures can result in catastrophic behaviors in safety critical embedded systems, sometimes resulting in loss of life. Component failures can be treated as off nominal behaviors (ONBs) with respect to the components and sub systems involved in an embedded system. A lot of research is being carried out to tackle the problem of ONBs. These approaches are mainly focused on the states (i.e., desired and undesired states of a system at a given point of time to detect ONBs). In this paper, an approach is discussed to detect component failures and critical components of an embedded system. The approach is based on fault tree analysis (FTA), applied to the requirements specification of embedded systems at design time to find out the relationship between individual component failures and overall system failure. FTA helps in determining both qualitative and quantitative relationship between component failures and system failure. Analyzing the system at design time helps in detecting component failures and critical components and helps in devising strategies to mitigate component failures at design time and improve overall safety and reliability of a system.

Current reliability approaches were designed to assess and quantify the reliability associated with complex systems such as nuclear power plants (NPPs). These approaches are generally based on classical Boolean logic structures such as event trees (ETs) and fault trees (FTs) [Rausand, 2020]. The outcome obtained by combining FTs and ETs is the set of minimal cuts sets (MCSs), with each MCS representing a unique combination of BEs that leads to an undesired outcome (e.g., core damage). Probabilistic evaluation of a MCS is performed by evaluating the product of the probability values associated with each BE. A relevant factor here is that the probability values associated with BEs used in the plant models are updated at least every 4 years based on past operational experience and through the use of a Bayesian statistical process [Siu, 1998]. Hence, the probability value of a BE associated with a physical asset (e.g., a centrifugal pump or motor-operated valve) in no way reflects that asset’s actual condition and performance.
 This fact plays a major role in the application of plant reliability models to support risk-informed decisions. With the particular goal of reducing operation and maintenance costs, existing NPPs are moving from corrective and periodic maintenance toward new types of predictive maintenance strategies [Agarwal, 2021]. This transition is designed such that maintenance is conducted only when the asset requires it (i.e., prior to undergoing imminent failure). And though these benefits cannot be achieved through actual reliability modelling methods and currently employed reliability data, they can be achieved by employing asset-monitoring sensors, automated data acquisition systems, data analysis methods, and improved decision-making processes. Combined, these resources can provide precise information on the health of an asset, track its degradation trends, and estimate its expected failure time. Based on such information, maintenance operations can be scheduled and performed for each asset on an as-needed basis. This dynamic context of predictive maintenance operations requires new methods of data analysis, the propagation of asset health information from the asset level to the system level, and the optimization of plant resources.
 This paper provides an alternative reliability approach designed for a predictive maintenance context in which a direct link is created between monitoring data and decision-making. Rather than thinking of reliability in terms of system/asset probability of failure, we propose a reliability mindset based on the concept of margin [Mandelli, 2023]. An asset’s health is quantified by determining its margin, based on the asset’s current and historical monitoring data. The margin values of the monitored asset are then propagated through system reliability models (e.g., FTs or reliability block diagrams) to identify the assets that are more critical to guarantee system operation. We show how a margin-based approach can be used assess asset health, based solely on current and historic monitoring data (e.g., condition-based, anomaly detection, diagnostic, and prognostic data) [Xingang, 2021]. A margin-based approach directly addresses the limitations of classical reliability modelling approaches and provides a snapshot of system health—given the availability of monitoring data. These two different approaches are designed to address different types of decisions: classical reliability models support static decisions (e.g., a set frequency of periodic maintenance or surveillance operations) based on past operational experience, whereas a margin-based approach directly supports dynamic decisions involving maintenance operations that should only be performed when necessary, based on monitoring data (i.e., a predictive maintenance context).

One of the most common questions in the early stages of designing a new facility is whether the normal utility supply to a fire pump is reliable enough to “tap ahead of the main” or whether the fire pump supply is so unreliable that it must have an emergency power source; typically an on-site generator. Apart from the obligation to meet life safety objectives, it is not uncommon that capital on the order of $100,000 to $1 million is at stake for a fire pump backup source. Until now, that decision has only been answered with intuition- using a combination of utility outage history and anecdotes about what has worked before. There are processes for making the decision about whether a facility needs a second source of power using quantitative analysis. Fault Tree Analysis (FTA) and Reliability Block Diagram (RBD) are two quantitative methods used in reliability engineering for assessing risk. This paper will use a simple one line for the power to a fire pump to show how each of these techniques can be used to calculate the reliability of electric power to a fire pump. The paper will also discuss the strengths and weakness of the two methods. The hope is that these methods will begin tracking in the NFPA documents that deal with fire pump power sources and can be used as another tool to inform design engineers and authorities having jurisdiction about public safety and property protection. These methods will enlighten decisions about the relative cost of risk control with quantitative information about the incremental cost of additional 9's of operational availability.

Reliability engineering approach was selected for optimizing the Gas Dehydration unit performance in BS-140/150 facilities to meet Asset Management process supported by Reliability Analysis for Gas dehydration units. Event log data collected for the system from March 2008 to March 2011 was analyzed using weibull event log portfolio. Reliability Block diagram was developed for the gas dehydration and glycol regeneration system. The failure/repair distribution parameters were derived from the event log analysis. Reliability, availability and Maintainability values were derived from the simulation of the block diagram in “Block Sim” software. Exponential model was used for the analysis; since the temperature increases exponentially when the glycol cooler performance degrades. This leads to major impact on moisture increases in the gas export. Fault Tree Analysis was carried out for entire gas dehydration functional failure. Objective of this FTA was to identify the basic equipment responsible for the functional failure.

Reliability and availability analysis of dependent–dynamic systems with DRBDs

A general framework for dependability modelling coupling discrete-event and time-driven simulation

There is a need to quantify system properties methodically. Dependability and security models have evolved nearly independently. Therefore, it is crucial to develop a classification of dependability and security models which can meet the requirement of professionals in both fault-tolerant computing and security community. In this paper, we present a new classification of dependability and security models. First we present the classification of threats and mitigations in systems and networks. And then we present several individual model types such as availability, confidentiality, integrity, performance, reliability, survivability, safety and maintainability. Finally we show that each model type can be combined and represented by one of the model representation techniques: combinatorial (such as reliability block diagrams (RBD), reliability graphs, fault trees, attack trees), state-space (continuous time Markov chains, stochastic Petri nets, fluid stochastic Petri nets, etc) and hierarchical (e.g., fault trees in the upper level and Markov chains in the lower level). We show case studies for each individual model types as well as composite model types.

Modeling a complete Internet of Things (IoT) infrastructure is crucial to assess its availability and security characteristics. However, modern IoT infrastructures often consist of a complex and heterogeneous architecture and thus taking into account both architecture and operative details of the IoT infrastructure in a monolithic model is a challenge for system practitioners and developers. In that regard, we propose a hierarchical modeling framework for the availability and security quantification of IoT infrastructures in this paper. The modeling methodology is based on a hierarchical model of three levels including (i) reliability block diagram (RBD) at the top level to capture the overall architecture of the IoT infrastructure, (ii) fault tree (FT) at the middle level to elaborate system architectures of the member systems in the IoT infrastructure, and (iii) continuous time Markov chain (CTMC) at the bottom level to capture detailed operative states and transitions of the bottom subsystems in the IoT infrastructure. We consider a specific case-study of IoT smart factory infrastructure to demonstrate the feasibility of the modeling framework. The IoT smart factory infrastructure is composed of integrated cloud, fog, and edge computing paradigms. A complete hierarchical model of RBD, FT, and CTMC is developed. A variety of availability and security measures are computed and analyzed. The investigation of the case-study’s analysis results shows that more frequent failures in cloud cause more severe decreases of overall availability, while faster recovery of edge enhances the availability of the IoT smart factory infrastructure. On the other hand, the analysis results of the case-study also reveal that cloud servers’ virtual machine monitor (VMM) and virtual machine (VM), and fog server’s operating system (OS) are the most vulnerable components to cyber-security attack intensity. The proposed modeling and analysis framework coupled with further investigation on the analysis results in this study help develop and operate the IoT infrastructure in order to gain the highest values of availability and security measures and to provide development guidelines in decision-making processes in practice.

Fault Tree Analysis and Reliability Block Diagram

This paper reviews three main methods in reliability analysis for power systems and applies them to a micro-grid with significant clean energy penetration. The approaches used are Reliability Block Diagrams (RBD), Fault Tree Analysis (FTA) and Markov Reliability Modeling (MRM). Each method is introduced and applied to a simple system to illustrate its application procedure. Reliability parameters such as the system's mean time to failure (MTTF), component structure importance, and reliability function are evaluated using simulation models. All three methods are then used to find the reliability measures of a real micro-grid which is characterized by significant clean energy penetration using solar photovoltaic panels and a fuel cell.

<div class="section abstract"><div class="htmlview paragraph">The rapid integration of intermittent renewable energy sources (RES) poses significant operational challenges for modern power systems. Lithium-ion battery (LIB)–based battery energy storage systems (BESS) have become vital for grid stability and energy management. However, large-scale deployment of BESS has led to increasing incidents such as fires and explosions, raising serious concerns regarding their safety and reliability. To overcome the limitations of traditional reliability assessment methods—such as reliability block diagrams (RBD), fault tree analysis (FTA), and Markov models—this study proposes an integrated fault detection and reliability analysis framework that combines FTA, failure mode and effects analysis (FMEA), and a Bayesian Fault Propagation Network (BFPN). The framework systematically models fault propagation across component, subsystem, and system levels, dynamically updating the prior probabilities of basic failure events using a Gaussian Mixture Model (GMM) and Expectation–Maximization (EM) algorithm. Conditional Probability Tables (CPTs) are recalculated through Maximum Likelihood Estimation (MLE) with logical relationships to achieve accurate and adaptive fault probability estimation. A multi-feature fusion indicator, the State Severity Indicator (SSI), is further introduced to evaluate system health in real time. A qualitative comparison with representative fault modeling and detection approaches—including Bayesian Network, FTA-DBN, and various machine learning methods—shows that the proposed BFPN offers a well-balanced trade-off between interpretability and real-time performance. Simulation experiments under both single- and multiple-fault scenarios demonstrate that the proposed framework accurately detects typical fault events and provides early warnings before fault escalation. Under complex coupled fault conditions, it effectively captures fault interactions and predicts cascading failures across subsystems and the overall BESS, showing strong robustness and diagnostic capability for real-time reliability assessment in modern energy storage systems.</div></div>