Failure Analysis Of Systems Research Articles

Parametrized fragility, failure analysis and cost-effectively strengthening of electrical power distribution system under ice and concurrent wind hazards

In the past decades, the increasing frequency and severity of winter storms due to climate change have revealed the vulnerability of power systems against such events. This study aims to investigate the impact of ice and concurrent wind hazards on power systems by proposing a set of parametrized fragility functions for utility poles and conductors, and then applying them to system failure analysis and cost-effectively strengthening. The fragility model is generated by logistic regression to estimate the failure probability of poles and conductors as a function of hazard and structural characteristics. Applying proposed functions, system failure analysis is performed using IEEE123 testbed in four cities with various hazard characteristics. The results show that wind speed significantly affects pole rupture, while extreme ice and wind combination influences conductor breakage. Meanwhile, even minor structural failure leads to severe outages. Moreover, the risk-based Expected Outage Reduction (EOR) is calculated with developed fragility models as pole replacement ranking index. By comparing different replacement strategies, the 70-year system performance can be cost-effectively optimized by risk-based pole ranking and replacement pace and timing. Overall, the models and applications presented in this paper serve as a critical part of power system resilience analysis against winter-related hazards.

Failure Analysis: Insights from Model‐Based Systems Engineering

ABSTRACTProcesses for system failure analysis (for example, FMEA) are structured, well‐documented, and supported by tools. Nevertheless, we hear complaints that FMEA work feels (1) too labor intensive to encourage engagement, (2) somewhat arbitrary in identifying issues, (3) overly sensitive to the skills and background of the performing team, and (4) not building enough confidence of fully identifying the risks of system failure. In fairness to experts in the process, perhaps such complaints come from those less experienced — but even so, we should care how to describe this process to encourage better technical and experience outcomes. This paper shows how model‐based systems engineering (MBSE) answers these challenges by deeper and novel integration with requirements and design. Just as MBSE powered the requirements discovery process past its earlier, more subjective performance, so also can MBSE accelerate understanding and performance of failure risk analysis — as a discipline deeply connected within the systems engineering process.

Review of Wind Field Characteristics of Downbursts and Wind Effects on Structures under Their Action

Downbursts belong to sudden, local, and strong convection weather, which present significant destruction for structures. At any given time, there are approximately 2000 thunderstorms occurring on the Earth. Many studies have investigated the effects of downbursts on different structures. However, the extensive range of varying wind field parameters and the diverse representations of wind speeds render the study of structural wind effects complex and challenging under downbursts. This study firstly reviews the research of wind field properties of downbursts according to four common approaches, and the major findings, advantages, and disadvantages of which are concluded. Then, failure analysis of transmission line systems under stationary and moving downbursts is explored. The article also reviews the wind pressure on the roof of different kinds of low-rise buildings, and some dominant parameters, namely roof slope, distance of building from downburst center, wind direction angle, and so on, are discussed. Moreover, the wind effects caused by downbursts on high-rise buildings and some specialized structures are also considered because more and more wind hazards are related to downbursts. Finally, the limitations of the current study are pointed out, and recommendations for further research are given for the accurate assessment of the effects of wind on buildings, with a view to providing safer and more economical wind-resistant design solutions for structures.

Root Cause Analysis Implementation to Improve Tissue Mill Automation System Availability in a Mill of the Biggest Indonesia Pulp Paper Industry: A Systematic Literature Review Approach

Increasing rivalry enforces Indonesia pulp paper industry to focus on competitive products in the market and the choice is to develop a Tissue Mill which is equipped with an automation system that has less manpower but require high availability that ensures production runs continuously. The minimum target for the availability of automation systems is 99.95%, which since commissioning in 2019 has not been reached on average per year until April 2024. Analysis of system failures that causing downtime was conducted. Analysis by comparing similar cases in various literatures and references as well for corrective actions and summarize suggested improvements to reduce downtime possibility in order to obtain 99.95% availability or even higher.

NLP-Based Fault Detection Method for Multifunction Logging-While-Drilling Services

This paper presents a Natural Language Processing (NLP) method aimed at detecting faults within field failure reports of drilling tools. It builds on the definition of entities specifically matched to our unique requirements. These entities have been annotated within the dataset under the guidance of a Subject Matter Expert (SME), laying a foundation for our NLP method. By utilizing a model based on bidirectional encoder representations from transformers, the method achieves an F1-score of 88\% in identifying entities and consequently detecting faults within field failure reports. This work is part of a long-term project aiming to construct a failure analysis and resolution system for drilling tools.

Research on cascading effect analysis of civil aircraft electrical power system failures

Abstract Cascade effect analysis is a qualitative analytical method recommended by SAE ARP 4761A [1] for evaluating complex integrated civil aircraft systems. The Electrical Power System (EPS) is one of the critical onboard systems. It can provide electrical power to the avionics system, flight control system, and other critical systems on the aircraft, and it has a wide range of failure effects. The consequences of EPS failure effects are difficult to determine. This paper applies the cascading effect analysis method to carry out the qualitative safety assessment of the EPS on the aircraft and finally confirms that the architectural design of the EPS met the safety design requirements. It also gives the engineering method (such as test, MBSA) suggestion to further check and verify the correctness and completeness of the theoretical analysis of CEA.

Research and verification on parameter solution of mixed shock model for common cause failure based on particle swarm algorithm

AbstractMixed shock model is an explicit construction method of failure probability model based on component independent failure, system nonfatal shock, and fatal shock failure, which considers common cause failure (CCF) in redundant system. For aerospace systems, a modified mixed shock model is proposed, which considers several components may fail independently and simultaneously in operation. In order to solve the issue that the parameters of the mixed shock model cannot be solved directly based on the failure probability data, a parameter solving method based on particle swarm optimization (PSO) algorithm is proposed. Additionally, the relationship between the failure probability and the gradient of the parameter change is deduced, and the reduced‐order (RO) solution based on the gradient of the parameter change is proposed to improve the efficiency of the solution. A fitness function construction method based on the relative error of the solution probability and the true probability is proposed to improve the probability solution accuracy of multicomponent failure. The nonlinear inertia factor optimization method combined with fitness change is studied to improve the particle swarm dynamics. The accuracy of the results of different parameters solving sequence and different PSO methods are compared, and the effectiveness of the RO solution is verified. The results of the mixed shock model before and after modification are compared with the different CCF data, which verifies the effectiveness and wide applicability of the modified mixed shock model. The results show that the modified mixed shock model for CCF and its parameter solution method can significantly improve the probability solution accuracy of all components failure, and also provide a new theoretical basis and solution method for the quantitative analysis of multiredundant system failure.

Competing risks analysis of engineering system failure based on nonparametric prediction

Purpose: The aim of this work is to analyze competing risks of engineering system failure using nonparametric prediction.Methodology: Two statistical methods are used to tackle the problem of the pipeline integrity. Nonparametric prediction is applied in the first one. The main attention is paid to the possible failure of the pipeline section due to a specific threat in terms of competing risks. The second method contains the analysis of the entire pipeline system. The focus is on rupture incidents, that provides the real data on the lifecycle of the pipeline section.Research findings: Nonparametric prediction is used to analyze competing risks of the pipeline failure. The lower and upper probability boundaries and survivability functions and competing risks of the pipeline section failure are introduced for a future ground section of the pipeline, which can be failed as a result of rupture.

EffCause: Discover Dynamic Causal Relationships Efficiently from Time-Series

Since the proposal of Granger causality, many researchers have followed the idea and developed extensions to the original algorithm. The classic Granger causality test aims to detect the existence of the static causal relationship. Notably, a fundamental assumption underlying most previous studies is the stationarity of causality, which requires the causality between variables to keep stable. However, this study argues that it is easy to break in real-world scenarios. Fortunately, our paper presents an essential observation: if we consider a sufficiently short window when discovering the rapidly changing causalities, they will keep approximately static and thus can be detected using the static way correctly. In light of this, we develop EffCause, bringing dynamics into classic Granger causality. Specifically, to efficiently examine the causalities on different sliding window lengths, we design two optimization schemes in EffCause and demonstrate the advantage of EffCause through extensive experiments on both simulated and real-world datasets. The results validate that EffCause achieves state-of-the-art accuracy in continuous causal discovery tasks while achieving faster computation. Case studies from cloud system failure analysis and traffic flow monitoring show that EffCause effectively helps us understand real-world time-series data and solve practical problems.

Dynamic Information System for Failure Analysis with It’s Application on Ship Main Engine

Dynamic Information System for Failure Analysis with It’s Application on Ship Main Engine

A hybrid Dynamic Bayesian network method for failure prediction of a lock mechanism

This paper aims to construct a failure prediction method for a lock mechanism system to increase prediction accuracy. The two major failure modes in lock mechanisms are kinematic accuracy failure and clamping stagnation. One failure mode is affected by another failure mode as a result of multiple influencing factors that are dependent on one another. Besides, the characteristic values of failure modes are challenging to acquire in some particular situations, such as when sensors are not placed. To address these issues, this study aims to propose a hybrid Dynamic Gaussian Bayesian network (DGBN) model for the failure prediction of a lock mechanism, in which correlations between each influencing factor and correlations between each failure mode are taken into account simultaneously. The improved DGBN model is integrated with measurement data and system failure analysis. The presented model relies on the information about influence factors that are more easily measured in practice and can account for multiple hidden variables for which measurement data is missing. Furthermore, a failure prediction framework is developed based on the proposed model. Finally, the proposed prediction method is tested by the application of a lock mechanism. A comparison is made between the improved method and the data-driven method. The results show that the proposed method can predict failures relatively accurately, even when partial measurement data are missing. The prediction error narrowed from 10% to less than 4%.

Online failure analysis and autonomous risk control scheme for electric buses

This paper proposes a Cyber–Physical System (CPS) scheme to address the challenge of developing self-improvement and online risk control for Electric Buses (E-Bus) in the absence or uncertainty of subsystems’ historical operational failure data. The scheme effectively deals with the unavailability or uncertainty of operational failure data by conducting an online analysis of the system’s failure through the development of a Fault Tree Analysis (FTA). Additionally, a comprehensive online risk control approach based on Reliability Centered Maintenance (RCM) is devised to define autonomous actions aimed at enhancing system uptime. The paper presents a case study on the Electric Bus drive system, establishing a link between the on-road physical driving conditions of the E-Bus, the system failure analysis, and autonomous uptime improvement actions. The implementation of the proposed approach in the case study resulted in a significant increase in the maximum allowable E-Bus mileage distance by 8603 miles while controlling the average driving cycle.

A Method for Backward Failure Propagation in Conceptual System Design

The increased complexity of modern system designs and demands for quicker time to market have made safety-related verification and validation of such systems more challenging. Incorporating safety and risk considerations at the early stages of design is one way to acquire a more robust initial design for novel systems. Inductive fault analysis has its significance at final stages of design, e.g., verification and validation. However, to preclude certain system failure states—especially for the systems with high failure consequences, a designer would innately prefer to trace back and remedy the causes of failure, as compared to a more cumbersome activity of identifying the faults individually and sifting the combinations that lead to the failure of interest. The work presented in this paper is aimed at the development of a backward failure propagation methodology for analyzing the origins of functional failures in a conceptual design of systems including but not limited to nuclear, mechanical, aerospace, process, electrical/electronics, telecommunication, automotive, etc. This method allows the designer to achieve a robust early design based on the analyses of the system’s functional dependencies before proceeding to the detailed design and testing stages. The insights provided by the analysis at the conceptual design stage also reduce redesign efforts, testing costs, and project delays. The proposed method is a functional analysis approach that extends the Integrated System Failure Analysis for backward failure propagation. When provided with an abstract system configuration, a system’s functional model, and a system’s behavioral model, it utilizes a known functional state (typically a failure) to acquire system component modes and the states of other functions. The method includes inversion of the functional failure logic and component behavioral rules using propositional logic and deductive analysis to assess valid states of a system that satisfy the given initial conditions. To test the method’s scalability, we applied the proposed method to a simplified representation of the secondary loop of a typical pressurized water reactor.

Design and manufacturing of aspherical GaAs-SILs for a versatile optical semiconductor failure analysis system

In this study, we have developed an aspherical solid immersion lens (SIL) made of gallium arsenide (GaAs) for semiconductor failure analysis. A SIL is an optical component to dramatically increase the numerical aperture and the spatial resolution of a microscopy system by attaching it to the backside of the device-under-test. To further enhance the spatial resolution of the optical microscopy system, a shorter wavelength light source has also been introduced. In the past, we have developed a spherical SIL made of silicon (Si), which is the same material as the device under test. However, Si is not transparent for wavelengths shorter than around 1100 nm. To enable the use of a SIL at shorter wavelengths, we chose GaAs as SIL material, which is transparent at shorter wavelengths than Si and has a refractive index very close to that of Si. However, a spherical GaAs SIL features a more pronounced spherical aberration due to the difference in refractive index between the device-under-test (Si) and that of the SIL (GaAs) deteriorating the optical resolution of the microscopy system. We have therefore designed an aspherical GaAs-SIL which makes it possible to correct spherical aberration. We applied diamond tooling as a technique for manufacturing the SIL shape with a required surface irregularity below 100 nm peak-to-valley, resulting in a diffraction limited performance of the optical system for semiconductor failure analysis with a performance at around 0.18 μm cutoff.

Root-Cause Analysis of Water System Failures in Jackson, Mississippi, and Flint, Michigan, Using the Institutional Analysis and Development Framework

Failures of drinking water systems in Flint, Michigan, and Jackson, Mississippi, were similar, other than their contextual situations. Each episode spurred media accounts and policy actions, but these may not address long-term systemic issues. Application of the Institutional Analysis and Development (IAD) framework can provide a repeatable approach to study such problems, and depending on its scope of application, can provide insights to patterns of interaction in critical action arenas where solutions must begin. The IAD framework evolved from work by policy scientists on a range of complex problems, and its use to analyze issues in Flint and Jackson explored its potential for addition to the tools of Integrated Water Resources Management. IAD can incorporate information from other methods and sources to enrich its knowledge base. For example, media attention highlights problems, and the case studies that follow can identify issues, action arenas, and players. Tools like systems analysis can be used to study the patterns of interaction in the action arenas and the feedback loops between them. Flint and Jackson require multiple solution approaches like these to address systemic issues. They should involve better utility management supported by political leadership and effective governance, as well as attention to equity issues involving human rights and responsibilities to pay for services. In shedding light on root causes of failures, IAD can help point the way to improving services despite formidable obstacles.

Dynamic Failure Analysis of Ship Energy Systems Using an Adaptive Machine Learning Formalism

The criticality of shipping operations in global trade requires a comprehensive understanding of its sustainability. This depends on the integrity/performance of the ship structure and vital systems, such as the ship propulsion engine. The current research paper presents the application of an adaptive machine learning formalism, the Bayesian network, for failure assessment of a ship propulsion engine considering nonlinear and nonsequential failure interactions. The model captures critical failure influencing factors and their complex interactions to predict the failure probability of the ship energy system. Sensitivity and uncertainty analysis was carried out to establish the degree of influence of vital failure influencing factors as they affect the ship propulsion engine’s reliability and the associated uncertainty in the prior data processing. The model is tested on the propulsion engine of an ocean going vessel to forecast the likelihood of failure based on the logical dependencies among failure causative factors. Two scenarios were analyzed based on canonical probabilistic algorithms, and the results show that upon evidence on the three critical failure modes, the ship propulsion engine failure likelihood increased by 11.8%, 8.2%, and 9.4%, respectively. The model shows an adaptive/dynamic capability to capture new failure information and update the system’s failure probability. The proposed approach provides a condition monitoring tool and early warning guide for integrity management of critical ship energy systems.

Cascading failure analysis of multistate loading dependent systems with application in an overloading piping network

Cascading failure analysis of multistate loading dependent systems with application in an overloading piping network

Logic Differential Calculus for Reliability Analysis Based on Survival Signature

The structure function is an often-used mathematical representation of the investigated system in reliability analysis. It is a binary function that models a system state according to the states of its components. The size of the structure function depends on the number of components and can be enormous for systems with many components. Therefore, the system reliability analysis based on the structure function needs special methods to decrease this dimension and to measure the system reliability. The concept of survival signature provides a useful transformation of the structure function to simplify reliability assessment for systems with many components of specified types. The survival signature is a complete probabilistic description of the system. The new methods and algorithms of system reliability analysis based on this mathematical representation should be developed. The Direct Partial Logic Derivative is one of the approaches that are effective in system reliability evaluation based on the structure function. This approach is used to determine different aspects of system failure depending on system components breakdowns. The development of this derivative for survival signature permits to obtain the new method for the reliability analysis of system failure caused by system component breakdown depending on components types.

Analysis of coupling system failures on freight trains

This paper presents an analysis of train coupling failure that led to trains break apart on the Serbian Railways over 10 years period. Train coupling failure of freight trains with single locomotives was considered. The analysis was done based on accident data combine with FMECA risk assessment. As a result distribution of failure along the train, driving regime and velocity were obtained, as well as the frequency of failure concerning the length and mass of the trains, load status, etc. The systematization of coupling failure helped to establish conditions leading to failure and to define the parameters causing it. Risk factors for coupling failure were determined using FMECA risk assessment. Preventive measures are recommended for the revision of maintenance. Risk analysis of coupling system failure can be different depending on the time of analysis (regulations, exploitation conditions) and the applied maintenance practice. FMECA analysis applied to train coupling systems based on regulation shows different permissible risk values that don't match exploitation data.

Process safety analysis using operational data and Bayesian network

AbstractFault detection and diagnosis (FDD) methods have recently experienced significant advances. These methods provide valuable information from an abnormal situation management perspective. However, traditional FDD methods do not consider system failure analysis, which is required from the process safety perspective. This work seeks to overcome this barrier and presents a methodology to assess process system failure probability based on process operational data and system knowledge. The methodology is built using the principal component analysis (PCA) and a Bayesian network (BN). The PCA is used for FDD, while the BN determines the probability of system failure once a fault is detected. This portion of the network is based on the logical relationship of the data, operational thresholds, and system failure conditions. The proposed methodology is tested and verified on a level‐controlled tank system and the real‐life failure scenario of the 2010 Tesoro heat exchanger explosion. The results suggest that the proposed methodology helps to assess process system failure probability based on process operating conditions. The current work is expected to give a stimulus on digital process safety education.