Multiple Failure Modes Research Articles

Reliability Analysis of High-Pressure Tunnel System Under Multiple Failure Modes Based on Improved Sparrow Search Algorithm–Kriging–Monte Carlo Simulation Method

It is often difficult for a structural safety design method based on deterministic analysis to fully and reasonably reflect the randomness of mechanical parameters, while the traditional reliability analysis method has a large calculation cost and low accuracy. In this paper, based on the seepage–stress coupling numerical model, the random variables affecting the reliability of the collaborative bearing of surrounding rock and lining structures are successfully identified. Then, the improved sparrow search algorithm (ISSA) is used to optimize the hyper-parameters of the Kriging surrogate model, in order to improve the computational efficiency and accuracy of the reliability analysis model. Finally, the ISSA-Kriging-MCS model is used to quantitatively evaluate the reliability of the surrounding rock-reinforced concrete lining structure under multiple failure modes, and the sensitivity of each random variable is discussed in depth. The results show that the high-pressure tunnel structure has high safety and reliability. The reliability indexes of each failure mode decrease with the increase in the coefficient of variation (COV) of random variables. In addition, the same random variable also exhibits varying degrees of influence in different failure modes.

Reliability Analysis of Complex Structures Under Multi-Failure Mode Utilizing an Adaptive AdaBoost Algorithm

A reliability analysis can become intricate when addressing issues related to nonlinear implicit models of complex structures. To improve the accuracy and efficiency of such reliability analyses, this paper presents a surrogate model based on an adaptive AdaBoost algorithm. This model employs an adaptive method to determine the optimal training sample set, ensuring it is as evenly distributed as possible on both sides of the failure curve and fully contains the information it represents. Subsequently, with the integration and iterative characteristics of the AdaBoost algorithm, a simple binary classifier is iteratively applied to build a high-precision alternative model for complex structural fault diagnosis to cope with multiple failure modes. Then, the Monte Carlo simulation technique is employed to meticulously assess the failure probability. The accuracy and stability of the proposed method’s iterative convergence process are validated through three numerical examples. The findings of the study illuminate that the proposed method is not only remarkably precise but also exceptionally efficient, capable of addressing the challenges related to the reliability evaluation of complex structures under multi-failure mode. The method proposed in this paper enhances the application of mechanical structures and facilitates the utilization of complex mechanical designs.

Uncertainty Quantification in the Prediction of Remaining Useful Life Considering Multiple Failure Modes

Abstract Despite the substantive literature on remaining useful life (RUL) prediction, less attention is paid to the influence of epistemic uncertainty and aleatory uncertainty in multiple failure behaviors in the accuracy of RUL. The research question in this study was: can uncertainties be quantified in predicting the RUL of systems with multiple failure modes? The first objective was to quantify the uncertainties in the prediction of RUL, considering known multiple failure modes. This objective used vibration data from accelerated degradation experiments of rolling element bearings. The second objective was to calculate the uncertainties in the prediction of RUL, considering the multiple failure modes as unknown. The experimental data used in this objective were from run-to-failure tests of Li-ion batteries. An analysis was performed on how the uncertainties affect the RUL prediction in systems with known multiple failure modes and systems where the multiple failure modes were unknown. A Bayesian neural network (BNN) was used to quantify epistemic and aleatory uncertainty while predicting RUL. The results of the qualitative uncertainties on RUL in systems with multiple failure modes were presented and discussed. Also, the study yielded an RUL uncertainty quantification model for multiple failure modes. The proposed framework's performance in the RUL prediction was demonstrated. Finally, the epistemic and aleatory uncertainties were quantified in the system's RUL. It was shown that systems that fail due to the same failure mode tend to have similar uncertainty values over time. The results in this paper may lead to the design of more reliable systems that exhibit multiple failure modes.

An Optimal Two-Dimensional Maintenance Policy for Self-Service Systems with Multi-Task Demands and Subject to Competing Sudden and Deterioration-Induced Failures

Self-service systems, such as electric vehicle charging piles (EVCPs), are typically deployed without on-site personnel. While frequent maintenance ensures high service revenue, it also leads to significant maintenance setup costs. Therefore, balancing service revenue and maintenance costs is essential for profit maximization. In this paper, we develop a maintenance policy optimization framework to maximize the profit rate of a fleet of self-service systems. First, we propose a maintenance policy that ensures sufficient functional systems while preventing high corrective maintenance costs. Next, we model the fleet's state transition process and its profit rate by characterizing two unique failure-induced demand-and-system interactions: demand switching and stepwise demand arrival rates, where the demands involve multiple tasks and systems are subject to multiple failure modes with non-constant occurrence rates. We develop a Tabu-search algorithm with random exploration to optimize the maintenance policy. Building on this, we investigate the impacts of key model parameters through a case study of thirteen EVCPs in Hong Kong and draw implications for profit maximization.

An efficient surrogate model for system reliability with multiple failure modes

AbstractThe failure modes of complex systems are often highly dependent. Aiming at the reliability analysis of engineering systems with multiple failure modes, an efficient surrogate model for system reliability with multiple failure modes is proposed, namely, the Copula Adaptive Kriging surrogate model (CAKSM). First, the initial Kriging model is established based on the initial samples. In order to select more effective added samples to update the Kriging model, a new learning function, namely, Copula Unites with Expected Improvement Function (CUEIF), is established. The function can make a reasonable sample selection by considering the failure correlation. Then, based on the optimal Copula function, the reliability data of each failure mode are coupled and analyzed. In order to adapt to various computing scales, regularization is introduced to improve the prediction speed of the surrogate model, and the probability density function of each performance function is obtained by introducing nonparametric kernel density estimation. Finally, the effectiveness of the CAKSM method and the learning function CUEIF is verified through four examples.

PLIC-FSR-SYS: System reliability analysis based on parallel learning of influential components with filtered sample region

In practical engineering, system reliability analysis is highly concerned since many structures or products have multiple failure modes. Accordingly, this paper develops an innovative method for system reliability analysis by parallel learning of influential component limit-state functions with filtered sample region (PLIC-FSR-SYS) based on Kriging modeling. Different from the traditional adaptive learning methods that train only one component in each iteration when constructing the surrogate of the composite limit-state function, a new strategy is explored to adaptively identify several important components in one iteration so as to train them simultaneously. In the meanwhile, a filtering formula is explored to determine the fatal region so that the unimportant samples can be removed to further accelerate the training process. Based on the join forces of parallel learning of influential components and avoiding the training at unimportant samples, PLIC-FSR-SYS can achieve a fairly efficient system reliability analysis with multiple failure modes. Finally, four different case studies, including an engineering application to the ultra-voltage on-load tap-changer, are conducted to prove the effectiveness of the proposed method. The results indicate that compared to traditional adaptive learning methods, the proposed method makes a significant efficiency improvement for system reliability analysis with multiple failure modes.

Availability modeling of competing risk system with multiple failure modes incorporating hybrid hazard rate model

Availability modeling of competing risk system with multiple failure modes incorporating hybrid hazard rate model

Tough Monolayer Silver Nanowire-Reinforced Double-Layer Graphene.

Mixed-dimensional nanomaterials composed of one-dimensional (1D) and two-dimensional (2D) nanomaterials, such as graphene-silver nanowire (AgNW) composite sandwiched structures, are promising candidates as building blocks for multifunctional structures and materials. However, their mechanical behavior and failure mechanism have not yet been fully understood. In this work, we have performed integrated experimental, theoretical, and numerical studies to explore the performance and failure modes of graphene-AgNW composite under tensile and impact loading conditions. In situ tensile tests using a nanoindenter, implemented with a push-to-pull device and a laser-induced projectile impact test system, are used to shed light on load-bearing mechanisms in graphene-AgNW composites. Multiple failure modes have been observed in both experimental setups and analyzed with numerical and theoretical models. Results show that in the tensile loading the distribution of AgNW, as characterized by the effective free length, is the key parameter determining the failure mode. As for the impact failure scenarios, compared with failure modes observed in pure graphene cases, the mechanical reinforcing effect of AgNW will transform the failure mode from a scattered tensile fracture along radial directions to a shear failure that is constrained in a relatively local domain. Theoretical analysis using shear lag modeling, Timoshenko plate theory, molecular dynamics modeling, and finite element modeling approaches are adopted to further establish the failure modes.

Failure analysis and life extension of mechanical products under multiple service conditions

Abstract During the service of engineering machinery products, the interaction of mechanical load, service environment and other conditions leads to the diversity and uncertainty of failure modes. Mechanism analysis is difficult to accurately calculate the dynamic changes of performance. Most existing machine learning methods only focus on the remaining useful life, without considering the internal mechanism and connection of multiple failure modes. In this paper, a resistance degradation model is established based on the data of multiple service cycles. According to the service data under different working conditions, a dynamic resistance degradation model based on classification and regression tree (CART) and a product service life enhancement strategy based on working condition sequence optimization are proposed. Firstly, the characteristics of different working conditions are analysed. Secondly, the data of multiple sensors are fused, and CART is used to fit the degradation model. Finally, a product service life optimization problem is designed to increase the life. Experiments show that the proposed method can fit the resistance degradation process, and can significantly improve the life of mechanical products.

Operational reliability assessment of complex mechanical systems with multiple failure modes: An adaptive decomposition-synchronous-coordination approach

To effectively perform Complex Mechanical Systems Operational Reliability Assessment (CSORA) with multiple failures, the Adaptive Decomposition-Synchronous-Coordination Approach (ADSCA) is proposed by integrating Decomposition-Synchronous-Coordination strategy, adaptive modeling technology, surrogate model and multi-objective reliability assessment theory. In which, the main problem is decomposed into related sub-problems using a Decomposition-Synchronization-Coordination strategy, with sub-models coordinated to achieve synchronous mapping between multiple variables, thereby enhancing efficiency in large multi-subsystem problems. The adaptive modeling technique is adopted to construct a series of interrelated sub models based on the surrogate model as the basis function, improving performance. A multi-objective reliability assessment theory is adopted to achieve CSORA under multiple fault modes, where multiple weak links are integrated to comprehensively evaluate the reliability of complex systems. The significance of the ADSCA lies in its ability to manage complexity, enhance scalability, and improve the accuracy and efficiency of reliability assessments in complex mechanical systems. To demonstrate the effectiveness of the proposed approach, two illustrative examples are used. This includes approximation and probability analysis of nonlinear functions that exhibit multiple responses, as well as reliability analysis of temperature in civil aircraft braking system. The study can provide theoretical reference for the reliability evaluation of multi-failure operation in complex mechanical systems.

Reliability analysis of output electrical response performance of multi-state flexoelectric structures under single and multiple failure modes

This paper proposes a reliability model of flexoelectric beams in the electrical open and short circuit states when different failure modes and the multiple failure modes of the output electrical response performances are considered, respectively. The reliability indices of the flexoelectric beams in the two circuit states can be defined based on the output electrical response models. Sequentially, the importance sampling (IS) and the mixed importance sampling (IS) methods are respectively used to calculate the reliability of the flexoelectric beams in single and multiple failure modes. The reliability results of the flexoelectric beams are verified by comparing them with the results of the Monte Carlo Simulation (MCS). The numerical results show that the flexoelectric beam is entered into a relatively safe and reliable state when the critical value of the open circuit voltage of 0.235 V and the thickness of the flexoelectric beams of 1 mm are considered as well as the length-thickness ratio of 20.

Piecewise Linear Strength Models for Analyzing Multiple Failure Mechanisms in Rocks Materials.

Rock materials failures are accompanied by the co-existence of various failure mechanisms, including rock fracturing, shearing, and compaction yield. These mechanisms manifest macroscopically as multiple failure modes and nonlinear strength characteristics related to stress levels. Considering the limitations of current rock mechanics strength theories, which are primarily derived from single failure mechanisms, this study evaluates the applicability of alternative strength theories. Based on the extensional-strain criterion and the PMC (Paul-Mohr-Coulomb) model, a piecewise linear strength model was proposed that is suitable for analyzing multiple failure mechanisms in rocks, revealing the intrinsic mechanisms of multi-mechanism rock material failure. A multiple failure mechanism strength model in the form of inequalities was proposed, using the generalized shear stress, mean stress, and stress Lode angle as parameters. Strength tests conducted on sandstone and granite rock material samples under different stress conditions revealed distinct piecewise linear strength characteristics for both rock types, validating the rationality and applicability of the multiple failure mechanism model. The findings construct a multi-mechanism failure model for rocks, providing enhanced predictive capabilities and aiding in the prevention of rock structural failures.

Anisotropic damage evolution in solid fractures: A novel phase field approach with multiple failure criteria and directional-dependent structural tensor

This study proposes a novel phase-field fracture model based on unified phase field theory, aiming to overcome current limitations in simulating material complex fracture behaviors. Through this model, analytical solutions for two-dimensional bars subjected to tensile or compressive stresses are provided, enabling the coupling of multiple failure criteria and further proficient simulation of mode-I, mode-II, and mixed mode-I/II fractures, effectively addressing challenges faced in modelling materials with different or complex failure modes under various loading conditions. Furthermore, to account for the strong anisotropic failure behavior of materials, a novel directional-dependent structural tensor is proposed. The tensor correlates fracture energy with crack surface orientation, facilitating precise characterization of material damage evolution with multiple potential crack orientations. This tensor ensures the consistency of phase-field fracture evolution with predefined fracture patterns. The effectiveness of the proposed model is validated through case studies, emphasizing its robustness and superior predictive capability in capturing fracture behavior under various conditions. This research provides a more accurate and universally applicable approach for simulating material failure, particularly for complex or multiple failure mode material failure simulations.

Multi-failure mode reliability of monorail vehicle gear transmission system based on multi-index staged degradation

The operational condition of monorail vehicle is diverse and influenced by numerous factors, leading to multiple failure modes and complex performance degradation mechanisms in gear transmission system. Addressing the problem that current reliability evaluation method based on single-stage single-index degradation models fail to accurately evaluate the multi-failure mode reliability of monorail vehicle gear transmission system, this study comprehensively considers the stage and nonlinear characteristics of degradation processes, along with the interdependence of indexes at different stages, a multi-index staged degradation model is established based on nonlinear Wiener process and Vine-Copula function. By analyzing the positions of multi-index stage change points, a multi-failure mode reliability evaluation method of monorail vehicle gear transmission system considering segmented dependence is proposed. And using the Bayes-Gibbs stepwise parameter estimation method proposed in this study, model parameters for Wiener process and Copula function are estimated separately. Finally, the applicability and effectiveness of the proposed method are verified, and the results shows that, compared to traditional single-stage single-index models, the proposed method more accurately describes the dependence and stage patterns of the degradation process, yielding more rational and effective reliability evaluation results.

Efficient estimation procedure for failure probability function by an augmented directional sampling

AbstractFailure probability function (FPF) can reflect quantitative effects of random input distribution parameter (DP) on failure probability, and it is significant for decoupling reliability‐based design optimization (RBDO). But the FPF estimation is time‐consuming since it generally requires repeated reliability analyses at different DPs. For efficiently estimating FPF, an augmented directional sampling (A‐DS) is proposed in this paper. By using the property that the limit state surface (LSS) in physical input space is independent of DP, the A‐DS establishes transformation of LSS samples in standard normal spaces corresponding to different DPs. By the established transformation in different standard normal spaces, the LSS samples obtained by DS at a given DP can be transformed to those at other DPs. After simple interpolation post‐processing on those transformed samples, the failure probability at other DPs can be estimated by DS simultaneously. The main novelty of A‐DS is that a strategy of sharing DS samples is designed for estimating the failure probability at different DPs. The A‐DS avoids repeated reliability analyses and inherits merit of DS suitable for solving problems with multiple failure modes and small failure probability. Compared with other FPF estimation methods, the examples sufficiently verify the accuracy and efficiency of A‐DS.

Neural Networks For Particle Diffusometry In The Presence Of Flow And With Defocused Particles

Diffusion is a natural phenomenon in fluids. Its measurement can be done optically by seeding an otherwise featureless fluid with tracer particles and observing their motion. However, existing algorithms for particle-based diffusion coefficient measurement have multiple failure modes especially when the fluid has a flow or the particles are defocused. We present a method based on Convolutional Neural Networks (CNNs) for predicting diffusion coefficients in the presence of these real-world effects. The CNNs were trained, validated, and tested on crops from four-frame temporally averaged images. The study includes three simulated datasets: Gaussian-shaped particles under no fluid flow condition, Gaussian-shaped particles under an arbitrary flow condition, and defocused particles under no fluid flow condition. The results show that the CNNs have a low Mean Absolute Error (MAE) of 0.12 µm 2 /s between the true and predicted diffusion coefficient values for the dataset with Gaussian-shaped particles under no fluid flow condition. The CNNs have a slightly higher MAE of 0.19 µm^2 /s for Gaussian-shaped particles under arbitrary flow and defocused particles under no fluid flow conditions. The performance of the CNNs was also benchmarked against four conventional algorithms on these simulated datasets. The results show that the conventional algorithms perform better than the CNNs when the particles are assumed to be Gaussian and the fluid has no flow. However, the CNNs outperform conventional methods in the presence of flow or if the particles are defocused. Finally, the outputs of CNNs were compared against the outputs of conventional algorithms on experimental datasets. This provides uncertainty in the range of 0.19 µm^2 /s - 0.47 µm^2 /s, which is slightly larger than the errors obtained from simulated datasets. Hence, the study shows that CNNs can be used to reliably predict diffusion coefficients from complex particle datasets with as little as four frames.

A Multi-Performance Reliability Evaluation Approach Based on the Surrogate Model with Cluster Mixing Weight

Kriging surrogate model has extracted extensive attention in reliability evaluation, owing to its excellent applicability and operability nowadays, which confronts with difficulties in balancing the efficiency and accuracy for complicated mechanical assets with multiple failure modes. Consequently, this paper devises a multi-performance reliability analysis approach within the surrogate model framework, particularly innovative in its use of cluster mixing weight. Specifically, high-value test points are selected to fit the surrogate model after sorting the samples referring to the corresponding values; then, a cluster-based active learning strategy is employed to accomplish rapid convergence, and the particle swarm algorithm is utilized to optimize relevant parameters. Afterwards, the mixing weight for every performance referring to the contributions to the final reliability is determined, and the failure probability is subsequently predicted. Furthermore, the superiority of the proposed approach with the clustering surrogate model and mixing weight, compared with traditional sampling as well as other surrogate models, has been verified via case studies, contributing to overcoming the multi-performance reliability analysis oriented to complicated mechanical assets.

A Curved Surface Integral Method for Reliability Analysis of Multiple Failure Modes System with Non-Overlapping Failure Domains

Abstract In the study of reliability of systems with multiple failure modes, approximations can be obtained by calculating the probability of failure for each state function. The first-order reliability method and the second-order reliability method are effective, but they may introduce significant errors when dealing with certain nonlinear situations. Simulation methods such as line sampling method and response surface method can solve implicit function problems, but the large amount of calculation results in low efficiency. The curved surface integral method (CSI) has good accuracy in dealing with nonlinear problems. Therefore, a system reliability analysis method (CSIMMS) is proposed on the basis of CSI for solving multiple failure modes system reliability problems with non-overlapping failure domains. The order of magnitude of the failure probability is evaluated based on the reliability index and the degree of nonlinearity, ignoring the influence of low order of magnitude failure modes, and reducing the calculation of the system failure probability. Finally, CSIMMS and other methods are compared by three numerical examples, and the results show the stability and accuracy of the proposed method.

High-speed perforation of IN718 plates by spherical TC4 Ti alloy projectiles: Experiments and modeling

Data and knowledge on ballistic performance of Inconel-718 (IN718) alloy is critical for evaluating the resistance of turbine blades against foreign object impacts in aeronautical and astronautical industries. Ballistic perforation experiments (impact velocity 480 ms−1 to 1340 ms−1) with TC4 spherical projectiles (5-mm-diameter) are conducted on IN718 target plates (2-mm-thick) in this work, along with high-speed photography. The IN718 plates show multiple deformation and failure modes (bulging, dishing, petaling and plugging). The ballistic limit velocity for the investigated projectile/target combination is 823 ms−1. Postmortem material characterizations (optical photography, scanned electron microscopy and energy dispersive spectrometry) are conducted on recovered projectiles and bullet hole inner walls, and the observations indicate adiabatic shearing, surface melting and abrasion of projectile material. A finite element model based on the Johnson–Cook constitutive model and failure criterion is established, and reproduces the ballistic experiments. Dimensionless analyses are conducted on the dimensions of postmortem projectiles and bullet holes, and the projectile residual velocity/mass. On the basis of experimental observations and theoretical analysis of the projectile dynamics, the whole perforation process is divided into three stages (the entering, cratering and plugging stages). A multi-stage analytical model on perforation is then developed considering the effects of cavity expansion, projectile deformation and abrasion, and can well predict the projectile dynamics during entering and cratering stages.

Comprehensive crashworthiness simulation analysis of a large aircraft fuselage barrel section in various crash scenarios

To utilize computational techniques in investigate and evaluate the crashworthiness of typical fuselage section of a large transport aircraft in various crash scenarios, the finite element model of fuselage section was modeled and validated based on the vertical drop test of fuselage section at 6.02 m/s. The crash simulation analysis in various crash scenarios (such as concrete ground, soft soil grounds and water) were further conducted based on the validated model of fuselage section, and the crash response characteristics (deformation and failure mode, acceleration response and occupant injury risk, energy-absorbing characteristics) were analyzed and compared. The results show that the finite element model can accurately simulate the unrolling failure mode (three plastic hinges failure mode) of fuselage section and the failure of the connections of cargo floor crossbeams and fuselage frames, and it is in good agreement with the test results in terms of impact velocity and acceleration response. In the case of various soft soil grounds impact scenarios, the partial impact energy is absorbed by the soft soil ground, resulting in slightly different failure modes of fuselage section and a reduction in the peak acceleration. The water-entry impact failure mode of fuselage section is consistent with the rigid ground impact failure mode, the overall deformation degree of fuselage section decreases, but the degree of bending and upturning of fuselage frames increases with the increasing of the water-entry impact velocities The safety of occupants can be effectively protected in the soft soil ground and water-entry impact scenarios. The unrolling failure mode of fuselage section can be changed to the flattening failure mode (multiple plastic hinges failure mode) through the reasonable design to avoid the rupture of cargo floor crossbeams, and the more crash energy can be absorbed due to the production of more plastic hinges for the flattening failure mode, and the crashworthiness of fuselage section can be significantly improved.