Functional Failure Propagation Research Articles

A graph theory approach to predicting functional failure propagation during conceptual systems design

AbstractAn open area of research for complex, cyber‐physical systems is how to adequately support decision making using reliability and failure data early in the systems engineering process. Having meaningful reliability and failure data available early offers information to decision makers at a point in the design process where decisions have a high impact to cost ratio. When applied to conceptual system design, widely used methods such as probabilistic risk analysis (PRA) and failure modes effects and criticality analysis (FMECA) are limited by the availability of data and often rely on detailed representations of the system. Further, existing methods for system reliability and failure methods have not addressed failure propagation in conceptual system design prior to selecting candidate architectures. Consideration given to failure propagation primarily focuses on the basic representation where failures propagate forward. In order to address the shortcomings of existing reliability and failure methods, this paper presents the function failure propagation potential methodology (FFPPM) to formalize the types of failure propagation and quantify failure propagation potential for complex, cyber‐physical systems during the conceptual stage of system design. Graph theory is leveraged to model and quantify the connectedness of the functional block diagram (FBD) to develop the metrics used in FFPPM. The FFPPM metrics include (i) the summation of the reachability matrix, (ii) the summation of the number of paths between nodes (i.e., functions) i and j for all i and j, and (iii) the degree and degree distribution. In plain English, these metrics quantify the reachability between functions in the graph, the number of paths between functions, and the connectedness of each node. The FFPPM metrics can then be used to make candidate architecture selection decisions and be used as early indicators for risk. The unique contribution of this research is to quantify failure propagation potential during conceptual system design of complex, cyber‐physical systems prior to selecting candidate architectures. FFPPM has been demonstrated using the example of an emergency core cooling system (ECCS) system in a pressurized water reactor (PWR).

An expert-based method for the risk analysis of functional failures in the fracturing system of unconventional natural gas

This paper proposes an expert-based method to analyze functional failure risk in the fracturing system of unconventional natural gas. The newly proposed Fuzzy-FFIP method integrates functional failure identification and propagation (FFIP) with fuzzy logic. Fuzzy-FFIP is sufficient for dealing with the complexity and correlation of functional failures and the lack of failure data, as well as capturing the fuzziness of functional states of a critical component and entire system. From a configuration-behavior-function aspect, FFIP is a common framework used to represent system-wide logical relationships and study functional failure propagation paths. This framework is an expert system where knowledge base and inference engine are based on behavioral rules (BRs) and functional failure logic (FFL), respectively. Using FFL as a support, fuzzy logic is specifically applied to analyze the fuzzification and defuzzification of functional states – operating, degraded, and lost. The proposed method can clearly provide functional failure modes, effectively reveal failure propagation paths, and quantitatively assess functional failure risk levels in the fracturing system. To illustrate its validity, an on-site pump lubricant subsystem of fracturing unit is selected as a test case. Results show that Fuzzy-FFIP is more detailed and accurate, and contributes to system safety during the whole fracturing period.

Conceptual Scheme Decision Model for Mechatronic Products Driven by Risk of Function Failure Propagation

Reliability is a major performance index in the electromechanical product conceptual design decision process. As the function is the purpose of product design, the risk of scheme design is easy to be caused when there is a failure (i.e., function failure). However, existing reliability analysis models focus on the failure analysis of functions but ignore the quantitative risk assessment of conceptual schemes when function failures occur. In addition, design information with subjectivity and fuzziness is difficult to introduce the risk index into the early design stage for comprehensive decisions. To fill this gap, this paper proposes a conceptual scheme decision model for mechatronic products driven by the risk of function failure propagation. Firstly, the function structure model is used to construct the function fault propagation model, so as to obtain the influence degree of the subfunction failure. Secondly, the principle solution weight is calculated when the function failure is propagated, and the influence degree of the failure mode is integrated to obtain the severity of the failure mode on the product system. Thirdly, the risk value of failure mode is calculated by multiplying the severity and failure probability of failure mode, and the risk value of the scheme is obtained based on the influence relationship between failure modes. Finally, the VIKOR (Višekriterijumska Optimizacija i kompromisno Rešenje) method is used to make the optimal decision for the conceptual scheme, and then take the cutting speed regulating device scheme of shearer as an example to verify the effectiveness and feasibility of the proposed decision model.

Simulation and Risk Assessment of Sand Mixing Equipment Based on Function Failure Propagation

Sand mixing equipment is one of the key equipment in the process of shale gas exploitation. There is a complex interaction between various components of the sand mixing equipment. The failure of a single component could trigger a cascade of system failures. In view of this problem, we proposed a function failure identification and propagation model based on fuzzy theory. Matlab Simulink software was used for modeling and simulation. The as low as reasonably practicable principle is used to evaluate the risk levels of different failure conditions. By comparing the analysis results of traditional FFIP method and fuzzy FFIP method, the practicability and effectiveness of fuzzy FFIP method were proved.

A Graph Theory Approach to Functional Failure Propagation in Early Complex Cyber‐Physical Systems (CCPSs)

AbstractThis paper presents a framework to quantify failure propagation potential for complex, cyber‐physical systems (CCPSs) during the conceptual stages of design. This method is referred to as the Function Failure Propagation Potential Methodology (FFPPM). This research is motivated by recent trends in engineering design. As systems become increasingly connected, an open area of research for CCPSs is to move reliability and failure assessments earlier in the engineering design process. This allows practitioners to make decisions at a point in the design process where the decision has a high impact and a low cost. Standard methods are limited by the availability of data and often rely on detailed representations of the system. As such, they have not addressed failure propagation in the functional design prior to selecting candidate architectures. To develop the metrics, graph theory is used to model and quantify the connectedness of the functional block diagram (FBD). These metrics quantify (1) the summation of the reachability matrix and (2) the summation of the number of paths between nodes (functions within system models) i and j for all i and j. From a practical standpoint, these metrics quantify the reachability between functions in the graph and the number of paths between functions defines the failure propagation potential of that failure. The unique contribution of this research is to quantify failure propagation potential during conceptual design prior to selecting candidate architectures. The goal of these metrics is to produce derived system requirements, based on an analysis, that focus on minimizing the impact of failures.

Early integration of safety to the mechatronic system design process by the functional failure identification and propagation framework

The research goal of this paper is to introduce a risk analysis methodology that can be applied at the early concept design phase, whose purpose is to identify fault propagation paths that cross disciplinary boundaries, and determine the combined impact of several faults in software-based automation subsystems, electric subsystems and mechanical subsystems. Specifically, the Functional Failure Identification and Propagation (FFIP) analysis framework is proposed to perform a simulation-based analysis of functional failure propagation. The focus is on risk assessment, the earliest activities of the safety process, in which hazards are identified and safety requirements are derived. It is argued that current risk assessment methods are not sufficient for concurrent integration of the safety process to the design process of a complex mechatronic system. In order to facilitate the integration of risk assessment to such systems at the earliest design stages, the design is expressed with syntax and semantics that is able to describe the propagation of failures throughout the system and especially across the boundaries of the mechatronic domains. A boiling water nuclear reactor (limited to the reactor core and steam outlets) is used as a case study. The results demonstrate the capability to handle several fault propagation paths in one scenario for hazard identification at the early, functional, design stage. Specifically, it is shown that FFIP is able to identify fault propagation paths that cross disciplinary boundaries, and which in turn is able to determine the combined impact of several faults in software-based automation subsystems, electric subsystems and mechanical subsystems. The impact is expressed in degradation or loss of safety related functions.