Identification Of Failure Modes Research Articles

Combining improved DFMEA with knowledge graph for component risk analysis of complex products

With the development of information technology in manufacturing, product lifecycle iteration is accelerating. Efficiently analyzing and identifying component risks is important to ensure the reliability of products. This paper proposes a new method that combines improved design failure mode and effect analysis (DFMEA) with the knowledge graph for component risk analysis of complex products. A risk evaluation knowledge graph is built for the risk identification and knowledge provision in the process of DFMEA. Graph analysis is used to identify to-be-evaluated objectives based on historical maintenance data, which helps designers avoid the heavy task of identification. The graph matrix of the historical evaluations is used to calculate the comprehensive weights. A Dempster-Shafer (DS) evidence fusion method based on comprehensive weight evaluation information is proposed for the effective fusion of evaluation evidence. As an alternative to the exact risk ranking, we offer a risk trade-off VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) method for components classification that classifies components into different risk classes for different risk treatment measures. Finally, the proposed method is validated using a tunnel boring machine as an example, and comparison and sensitivity analyses are performed to demonstrate that the risk value shows a consistent and stable change with expert evaluation.

Production risk management of prawn crackers using fuzzy FMEA and fuzzy AHP methods

Prawn crackers hold a significant position within Indonesia's agro-industrial product landscape. The industrial process involved in their production is fraught with potential risks that could result in substantial losses for companies. PT. XYZ, a prominent prawn cracker producer in Indonesia, grapples with various risks, including non-compliant raw material supplies, machinery and equipment breakdowns, product defects, and process failures. Hence, there is an imperative need for comprehensive risk management involving further analysis, identification of potential failure modes, and formulation of mitigation strategies to curb losses. This research aims to ascertain the priority of potential failure modes and their corresponding mitigation strategies in the prawn cracker production process at PT. XYZ. The study employs risk analysis through the Fuzzy Failure Mode and Effect Analysis (Fuzzy-FMEA) method and determines mitigation strategies using the Fuzzy Analytical Hierarchy Process (Fuzzy-AHP) method. The findings reveal 11 identified failure modes in PT. XYZ's prawn cracker production process, with machinery and equipment damage emerging as the top-priority failure mode, giving a Fuzzy Risk Priority Number (FRPN) value of 7.620. The recommended mitigation strategy involves intensifying inspections of production machinery and equipment, both before and after use by workers, with a weight of 0.368.

On nonlinear dynamic analysis of hyperbolic tangent FG-GPL-reinforced shallow spherical cap under supersonic flow

On nonlinear dynamic analysis of hyperbolic tangent FG-GPL-reinforced shallow spherical cap under supersonic flow

System reliability analysis of Cable-stayed bridge using pair Copula and ICLHS with unequal weights under seismic loading

Considering the correlation between different components, quantifying the failure probability of a high-level hyper-static structure under non-stationary seismic loading is of great significance in guiding earthquake-resistant design and fortification of bridges. In this study, a high-pier and large-span railway cable-stayed bridge in the mountainous region were employed to evaluate the seismic performance. First, the basic theoretical methods of the non-stationary random seismic motion simulation, equivalent extreme value theory based on extreme value distribution (EVD), improved Latin hypercube sampling (ICLHS) with unequal weighted fractional moment estimation and Pair Copula technique are presented. Second, the finite element analysis model of the cable-stayed bridge was established based on the OpenSees platform, the seismic response samples of the bridge were calculated under three levels of seismic ground motion to determine the first passage failure probability of the main components of the bridge under the design failure modes. Finally, the Pair Copula function was introduced to estimate the seismic system reliability of the cable-stayed bridge. The optimal Copula type that can describe the correlation between the bridge components was selected through goodness-of-fit tests such as AIC, BIC, and the minimum distance method. The failure probability of the entire system was obtained by calculating the selected Copula function type under seismic loading.

Use of a Modified Threshold Function in Fuzzy Cognitive Maps for Improved Failure Mode Identification

Fuzzy cognitive maps (FCMs) provide a rapid and efficient approach for system modeling and simulation. The literature demonstrates numerous successful applications of FCMs in identifying failure modes. The standard process of failure mode identification using FCMs involves monitoring crucial concept/node values for excesses. Threshold functions are used to limit the value of nodes within a pre-specified range, which is usually [0, 1] or [-1, + 1]. However, traditional FCMs using the tanh threshold function possess two crucial drawbacks for this particular.Purpose(i) a tendency to reduce the values of state vector components, and (ii) the potential inability to reach a limit state with clearly identifiable failure states. The reason for this is the inherent mathematical nature of the tanh function in being asymptotic to the horizontal line demarcating the edge of the specified range. To overcome these limitations, this paper introduces a novel modified tanh threshold function that effectively addresses both issues.

OVERVIEW OF STUDIES ON EXTERNAL CONFINEMENT OF R/C COLUMNS USING UNIAXIAL GEOGRID

It has been established in the literature that using uniaxial geogrid as internal confinement for reinforced concrete (R/C) columns would improve the strength and ductility characteristics of the column. However, the potential of using uniaxial geogrid as external confinement remains largely unexplored. The aim of the study is to assess the effectiveness of utilizing uniaxial geogrid as external confinement as compared to FRP external confinement, as well as various configurations of internal confinement, including uniaxial geogrid and stirrups. The assessment encompasses the analysis of axial load-displacement behavior, identification of failure modes, and evaluation of structural ductility through metrics such as fracture energy and energy dissipation. Test results indicate significant benefits associated with external confinement using uniaxial geogrid, including strength, ductility, and energy capacity. This study contributes to the progression of knowledge by illustrating the efficacy of uniaxial geogrid as an external confinement method for reinforced concrete columns. Additionally, it offers valuable comparative insights into different confinement techniques, informing the selection and design of optimal strategies for reinforced concrete structures.

The risk-based operational safety strategy for Perkin-Elmer/Cetus DNA Thermal Cycler PCR systems

Introduction: this article was conducted in the field of polymerase chain reaction (PCR) test safety, where it is essential to understand the contribution and impact of risk analysis methods to improve test safety medical diagnosis at the institutional level. In this context, the current study focuses on failure modes and effects analysis (FMEA) applied to the Perkin-Elmer/Cetus deoxyribonucleic acid (DNA) thermocycler.Objective: the objective of this study is to provide practitioners with a comprehensive overview of the application of FMEA to improve the operational safety of Perkin-Elmer/Cetus DNA thermocyclers used in medical diagnostics in ataxic patients.Methods: to carry out this study, a methodology based on an exhaustive review of the scientific literature available in academic databases and relevant sources was used. Previous studies on FMEA analysis of DNA thermocyclers in common and expanded forms were collected and analyzed.Results: the results obtained demonstrated that FMEA is an effective tool to identify and evaluate possible failure modes of Perkin-Elmer/Cetus DNA thermal cyclers. Several defects were identified and their consequences, causes and classification based on their severity and frequency were determined. Likewise, corrective actions are proposed to reduce the identified risks.Conclusions: early identification of possible failure modes and implementation of corrective actions can reduce risk and guarantee the quality of the diagnoses carried out and therefore improve operational safety.

Identification of key potential risk areas and key potential failure modes in hemodialysis rooms by the FMEA method following routine prevention and control of the COVID-19 pandemic.

Hemodialysis is an important part of nosocomial infection prevention and control (IPC). This study aimed to identify the key potential risk areas and failure modes in hemodialysis rooms in hospitals and put forward a series of improvement measures to prevent and control the spread of the coronavirus disease 2019 (COVID-19). Hemodialysis patients are highly susceptible to COVID-19 and usually have a high incidence of severe illness and mortality after infection with COVID-19. Therefore, IPC in hemodialysis patients is of crucial strategic significance. Based on 30 domain experts' interviews and careful analysis of prevention and control documents, we constructed a comprehensive failure system for a model that identifies the potential risks for nosocomial COVID-19 infection in the hemodialysis room. Subsequently, a thorough risk assessment of the potential failure factors identified in our model was conducted. The failure key factors corresponding to the human element in medical waste (garbage) disposal (C2) are verified to be the highest risk factors. They are as follows: The cleaning staff did not dispose of different types of medical waste (garbage) (C21), did not wear masks according to the regulations (C22), and lacked knowledge and norms of nosocomial IPC (C23). This study provides valuable insights for hospital decision-makers on the potential failure factors related to COVID-19 infections in hemodialysis rooms. By working with hospital infection specialists, the suggested improvement measures can help reduce the risk of virus exposure among hospital medical staff, patients, and cleaning staff.

Identification of failure modes in interior permanent magnet synchronous motor under accelerated life test based on dual sensor architecture

Identification of failure modes in interior permanent magnet synchronous motor under accelerated life test based on dual sensor architecture

Experimental study on mechanical behavior of precast large-cantilevered UHPC bent caps with π-shaped cross-sections

To reduce the self-weight of the precast bent caps with large cantilevers commonly used in municipal bridge construction, an innovative thin-walled bent cap scheme made of ultra-high-performance concrete (UHPC) was proposed in this paper. Six scaled bent cap specimens, characterized by a scale ratio of 1:2.5, were subjected to static loading tests, encompassing both bending-shear and bending-shear-torsion combinations. Throughout the loading process, careful observations were made regarding the progressive development of crack patterns, stress distribution within various materials, and the identification of ultimate failure modes. It was observed that for the UHPC cap beams with small shear span-to-depth ratios, both the web shear cracks and flexural cracks were fully developed, leading to a combined shear and bending failure mode. With shear span-to-depth ratio increased, the effect of bending behavior became more and more dominate, resulting in an increased ductility. When subjected to a combined bending-shear-torsion loading, the UHPC bent caps exhibited conspicuous diagonal shear failure attributes, indicating that the torsion presented in the bent caps should be limited. In summary, the adoption of a π-shaped section for the UHPC bent caps with large cantilevers results in an impressive weight reduction of 40%∼50% compared to the conventional solid cap beam design. Moreover, the proposed design not only aligns with specifications in terms of cracking strength and ultimate bearing capacity but also facilitates monolithic precast, making it exceptionally well-suited for practical engineering applications.

Impact force curve feature and failure mode identification method of concrete-filled steel tube members under lateral impact

A collision accident caused by derailed trains is a potential safety hazard for the structure adjacent to high-speed rail lines. Concrete-filled steel tube (CFST) columns are frequently utilized in modern high-speed railway stations, however CFST station columns may suffer severe damage from train collisions because of their high impact energy. Therefore, the method of identifying CFST members' failure modes under large impact energy was investigated in this paper. Adopting numerical simulation by LS-DYNA, the corresponding relationship between the local indentation and impact process was researched, the characteristic impact force time-history curve was proposed, and the effects of impactor and CFST specimen parameters on impact force feature were analyzed. The plateau proportion was established and quantified, then the mapping relationship between plateau proportion and failure degree was acquired. Research results indicated that local indentation developed primarily at impact force plateau stage. CFST specimens showed bending deflection when plateau proportion was below 67.5%, experienced fracture failure when it was above 77%, and exhibited crack failure when it was in between respectively. Finally, the empirical formulas for plateau proportion and maximum deflection were established, and a simplified approach for predicting the failure modes was put forward. The research provides suggestions for identifying damage patterns and strengthening CFST columns subjected to lateral impact loads.

Safety Analysis of Lithium-Ion Cylindrical Batteries Using Design and Process Failure Mode and Effect Analysis

Cylindrical lithium-ion batteries are widely used in consumer electronics, electric vehicles, and energy storage applications. However, safety risks due to thermal runaway-induced fire and explosions have prompted the need for safety analysis methodologies. Though cylindrical batteries often incorporate safety devices, the safety of the battery also depends on its design and manufacturing processes. This study conducts a design and process failure mode and effect analysis (DFMEA and PFMEA) for the design and manufacturing of cylindrical lithium-ion batteries, with a focus on battery safety.

Data-driven crack image-based seismic failure mode identification for damaged RC columns

The seismic failure mode identification of reinforced concrete columns is crucial for post-earthquake stability and safety assessment of the damaged concrete buildings. In this paper, a multi-feature crack image-based methodology is proposed for non-destructive non-contact failure mode prediction in reinforced concrete columns damaged in an earthquake. A databank of 455 surface crack patterns from quasi-static experiments on 111 reinforced concrete columns with a variety of structural and geometric properties is collected by the authors aimed at the development and validation of the methodology. For the intricacy retrieval of the surface crack maps, multi-feature fractal-based procedures are taken into consideration. Various machine learning algorithms are employed to train the seismic failure mode predictive models using the characteristics of the damaged RC columns. Ten scenarios are defined based on the image-extracted indices, and structural and geometric characteristics of rectangular reinforced concrete columns. Hyperparameter tuning of the machine learning algorithms using GridsearchCV function has optimized the performance of the models. A k-fold cross-validation procedure is also executed to examine the robustness of the results. Results reveal that the predictive model in which the image-extracted indices and structural parameters are jointly used as input features of supervised learning provides 98% accuracy for the testing dataset with Extreme gradient boost algorithm. In addition, among the scenarios that only image-derived parameters are considered as input features, an accuracy of 83% is obtained with Extreme gradient boost algorithm for the testing dataset.

Analysis on Design Failure Mode of Residential Energy Storage System

Analysis on Design Failure Mode of Residential Energy Storage System

An ensemble model of explainable soft computing for failure mode identification in reinforced concrete shear walls

Shear walls are critical structural components that resist lateral forces. The failure mode of a shear wall directly impacts the overall structural behavior. Therefore, accurate identification of the failure mode of shear walls is essential for the design, construction, and maintenance of safe and reliable structures. This paper presents an ensemble learning (EML) approach that integrates various different types of models to predict the failure mode of shear walls. Utilizing 393 shear wall samples gathered from the literature, a new EML model with better generalization performance was constructed using random forest (RF), support vector machine (SVM), light gradient boosting machine (LGBM), and logistic regression (LR) models, and these sub-models were further optimized using bayesian optimization (BO). The results demonstrate that the EML model achieved the highest recognition accuracy at 92.37 % in eight models, and the prediction performance of sub-models significantly improved after optimization. Multiple model interpretation methods revealed that variables such as aspect ratio, wall length-to-thickness ratio, cross-sectional shape, and boundary element reinforcement indices play a particularly influential role in predicting failure mode. This study provides an in-depth explanation of the identified failure modes from both an overall and sample-specific perspective and constructs a graphical user interface (GUI) for demonstrating the recognition of failure modes. Overall, as a study of ensemble learning using different types of models to recognize the failure mode of shear walls, the EML model established in this paper can effectively predict the failure mode of reinforced concrete shear walls.

Micromechanical analysis of the suction bucket-granular soil interactions under the pull action of anchor lines: Embedded length effect

The micro-mechanism of interactions between suction buckets and granular soil under different embedded lengths remains unclear. This study investigates the micro-to-macro interactions between suction buckets and granular soil under an inclined pull action of anchor lines, taking the embedded length effect into consideration. The granular soil was modeled using the discrete element method (DEM), while the suction bucket was modeled using the finite element method (FEM). The peak pull force in DEM-FEM simulations was validated using model test results, while macro and micro behaviors of the structure-soil interaction were analyzed. The findings demonstrated that the embedded length effect shaped the pull force-displacement curve by influencing the interaction modes between structures and granular soil. Discontinuity and large soil deformation occurrences can be successfully simulated using the DEM-FEM method and the relationships to embedded lengths were discovered. Besides, clear relationships among bucket rotation angles, the effective supporting areas, vertical pull-out displacement, shear wear, and deformation of buckets, were established. Furthermore, a detailed investigation of particle-scale behaviors led to an identification of mechanical failure modes of suction bucket-granular soil interaction. The findings suggest that ignoring the embedded length effect could lead to misestimating the uplift capacity and failure modes of the suction buckets.

Shaking table test of prestressed high-strength concrete pipe piles reinforced with non-prestressed steel reinforcement

Prestressed high-strength concrete piles reinforced with non-prestressed steel reinforcement (PRHC piles) have been developed to improve their seismic performance. In this study, the seismic response and failure mechanism of PRHC piles was investigated using a shaking table test. The dynamic characteristics, dynamic soil pressure, superstructure response, acceleration magnification factors, maximum curvature ductility demand, bending moment response, and failure mode identification of the PRHC piles were determined. The performance of PRHC piles was compared with that of prestressed high-strength concrete pipe (PHC) piles, which showed that the PRHC piles experienced higher energy dissipation and lower maximum curvature ductility demands than PHC piles. In addition, the PRHC piles suffered bending failure at the upper-middle positions, and the plastic zones were higher than those of the PHC piles. The adverse effects of long-period and pulse-type ground motions on pipe piles should be considered in designing for strong seismic events. The addition of non-prestressed reinforcement to a pipe pile allows longer segments of the pipe pile to be involved in providing resistance.

DECISION ANALYSIS FOR OPTIMAL CONCEPTUAL DESIGN OF CONCURRENT BRAKE ACTUATOR

Enhanced capabilities and customization in designs demand a thorough conceptual design phase for products or equipment. To ensure favorable outcomes, a comprehensive analysis of multiple design concepts is vital. This paper aims to conduct a decision analysis to determine the most suitable design for a concurrent brake actuator (CBA) among a range of alternative design concepts. It presents the development of the conceptual design of the CBA mechanism, which serves as a foundational mechanism design for future CBA development. Four mechanism design concepts were generated by utilizing the expanding curvature contour design, linear contour design, tilted position linear slope, and the nonlinear radius profile of the cam roller. The assessment of potential failures in the CBA concept design was performed by employing the risk priority number (RPN) within the framework of Design Failure Mode and Effects Analysis (DFMEA). The data obtained from DFMEA was utilized to conduct thorough analyses of motion and stress performance for each conceptual design using commercial software. Subsequently, the most optimal concept design for the CBA was chosen. This decision was reached by selecting the CBA concept design that achieved the highest score during the evaluation process, which employed a weighted decision matrix. According to the findings, the optimal CBA concept design was determined to be CBA Design B with the highest total score of 102 based on an RPN score is 32 and maximum stress of 3.647 x 104 N/m2. Its expanding linear contour design effectively distributes nonlinear brake force while minimizing failure risk, forming the foundational framework for future CBA development.

Failure Mode Identification and Shear Strength Prediction of Rectangular Hollow RC Columns Using Novel Hybrid Machine Learning Models

Failure mode identification and shear strength prediction are critical issues in designing reinforced concrete (RC) structures. Nevertheless, specific guidelines for identifying the failure modes and for accurate predictions of the shear strength of rectangular hollow RC columns are not provided in design codes. This study develops hybrid machine learning (ML) models to accurately identify the failure modes and precisely predict the shear strength of rectangular hollow RC columns. For this purpose, 121 experimental results of such columns are collected from the literature. Eight widely used ML models are employed to identify the failure modes and predict the shear strength of the column. The moth-flame optimization (MFO) algorithm and five-fold cross-validation are utilized to fine-tune the hyperparameters of the ML models. Additionally, seven empirical formulas are adopted to evaluate the performance of regression ML models in predicting the shear strength. The results reveal that the hybrid MFO-extreme gradient boosting (XGB) model outperforms others in both classifying the failure modes (accuracy of 93%) and predicting the shear strength (R2 = 0.996) of hollow RC columns. Additionally, the results indicate that the MFO-XGB model is more accurate than the empirical models for shear strength prediction. Moreover, the effect of input parameters on the failure modes and shear strength is investigated using the Shapley Additive exPlanations method. Finally, an efficient web application is developed for users who want to use the results of this study or update a new dataset.

A Study on dFMEA-based Fire Risk Assessment of Lithium-ion Secondary Battery Testing Cells

In this study, Design Failure Mode and Effects Analysis (dFMEA) was performed to evaluate the fire risk of lithium-ion secondary battery testing cells used during the research and development stage. dFMEA was used for the failure risk analysis, risk assessment, and risk priority analysis of the anode, cathode, separator, electrolyte, and cell design of the testing cell. In addition, the evaluation criteria, including severity, occurrence, and detection, was reorganized into three categories and optimized for the testing cell application. Based on the failure risk analysis, a total of 11 causes of failure were identified. In particular, explosion and thermal runaway were predicted when a nickel-cobalt-manganese (NCM)-based anode material with a high nickel content was used, the separator had a high thermal contraction rate and low tensile strength characteristics, and there were errors in the electrolyte composition optimization. This cause of failure was classified as a moderate risk with a high fire risk by the risk priority analysis. Therefore, at the research and development stage, it is critical to evaluate the mutual reactivity and preliminary physical properties of battery materials and to construct safety equipment to reduce fire damage.