Multi-failure Modes Research Articles

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.

Reliability analysis for running safety of vehicle on slab track via an improved second-order fourth-moment approach

The reliability assessment for running safety of vehicle on slab track under temperature action and seismic excitation is challenging due to the extensive computational efforts. This study proposes an improved second-order fourth-moment (ISOFM) approach for the reliability analysis for running safety of vehicle on slab track under temperature action and seismic excitation. The dimensionless limit state function (LSF) for running safety of vehicle on slab track is formulated considering four failure modes. The proposed ISOFM approach is verified by the Monte Carlo simulation (MCS) method. The proposed ISOFM approach greatly reduces the number of computations of nonlinear LSF. The merit of the proposed ISOFM approach lies in significantly enhancing the computation efficiency and precision of reliability considering nonlinear LSF with multi-failure modes. Additionally, the influences of temperature action and seismic excitation on the reliability for running safety of vehicle on slab track are discussed. The reliability for vehicle running safety decreases more significantly with the increment in overall temperature compared to the positive temperature gradient (PTG). The reliability for running safety of vehicle on slab track decreases nonlinearly with the increase of the earthquake intensity.

A novel null-hypothesis approximation method of limit state function in multi-failure mode reliability analysis of structural system

A novel null-hypothesis approximation method of limit state function in multi-failure mode reliability analysis of structural system

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.

Reliability Analysis for RV Reducer by Combining PCE and Saddlepoint Approximation Considering Multi-Failure Modes

Abstract RV (Rotate Vector) reducer is an essential mechanical transmission device extensively used in industrial machinery, robotics, aerospace and other fields. The dynamic transmission characteristics and strength of the cycloidal pin gear, turning arm bearing of RV reducer significantly affect the motion accuracy and reliability of the whole equipment. Uncertainties from manufacturing and assembly error, working loads add complexity to these effects. Developing effective methods for uncertainty propagation and reliability analysis for the RV reducer is crucial. In this work, the mail failure modes of RV reducer are studied, and an effective reliability analysis method for RV reducer considering the correlation between multi-failure modes by combining polynomial chaos expansions (PCE) and saddlepoint approximation method (SPA) is proposed. This paper develops an uncertainty propagation strategy for RV reducer based on dynamic simulation and PCE method with high accuracy. On this basis, a surrogated cumulant generating function (CGF) and SPA are combined to analyze the stochastic characteristic for the failure behavior. Based on the probability density function (PDF) and cumulative distribution function (CDF) calculated by SPA, copula function is employed to quantify the correlations between the multi-failure modes. Then, the system reliability with multi-failure modes is estimated by SPA and optimal copula function. The proposed method provides an effective reliability assessment technology with high-accuracy for complex system under unknown physical model and distribution characteristics. The validity of the proposed approach is illustrated RV-320E reducer reliability estimation, offering a basis to improve the performance of complex dynamic system. .

Multi-Objective Inspection Optimization of Periodically Inspected Multi-Failure Mode System

This paper examines the multi-objective availability and cost optimization of a periodically inspected system. A system with [Formula: see text] independent failure modes is specifically considered. The principal characteristic of the proposed model is that the inspection and maintenance time is nonnegligible and corrective maintenance results in as good as a new unit. This paper focuses on evaluating the general expressions for limiting availability and long-run average cost of the system undergoing periodic inspections. The multi-objective optimization issue is solved by first generating the Pareto-optimal solutions using particle swarm optimization and then selecting the best compromise solution using a fuzzy method. A numerical illustration is also presented to highlight the applicability of the proposed work.

Failure mode division and remaining useful life prognostics of multi-indicator systems with multi-fault

Modern large-scale industrial systems are complex in structure, and their health states are usually reflected by multiple indicators. The performance degradation of multiple indicators surpasses respective threshold, causing multiple system faults, and multiple failure modes such as redundancy, fusion, and competition among different fault combinations exist, introducing new challenges when predicting remaining useful life (RUL) of the system. This study aims to establish a unified multi-failure mode division framework and the RUL prediction model under different failure modes for multi-indicator systems. Firstly, combination relationship between different fault types caused by multi-indicators outweighing their threshold is analyzed, and different redundancy, fusion, and competition failure modes are defined. Next, the formal fault type definition and its remaining time before occurrence under different failure modes are provided. The distribution calculation model of remaining time for different fault types is derived. The corresponding system's RUL prediction model under multi-fault competition is established, and degradation modeling, parameter estimation, and RUL distribution calculation are performed using the state-space model. Eventually, the validity of the RUL prediction model according to multiple failure modes is verified by numerical experiments. Taking XJTU-SY bearing and C-MAPSS datasets as two examples, the applicability and feasibility of the given method are proved.

On the buckling propagation and its arrest in buried offshore pipeline crossing strike-slip fault

This study focused on buckling propagation and its arrest in buried offshore pipelines crossing strike-slip faults. A buried offshore pipeline with integral arrestors was analyzed using nonlinear vector-form-intrinsic-finite-element-method-based shell elements and modified soil springs. The effects of different external pressures on structural response were investigated. Integral arrestors with different design parameters were tested to prevent flattening and flipping propagation. The results showed seven or five fluctuations in the flattening parameters with alternating orthogonal directions along the pipeline. These fluctuations led to multi-failure modes, including S-shaped and Z-shaped flexures, two local collapses followed by flattening propagations, and a single local collapse followed by flipping propagation. Integral arrestors should be designed specifically to provide sufficient local bending rigidity in the circumferential direction. The arrest mode of flattening propagation is stopping the earliest fluctuations whilst that of flipping propagation is stopping the earliest fluctuations and depressing the downstream reverse ovalities. For the same arrestor thickness, the smallest effective arrestor length to stop flattening propagation was significantly less than that for flipping propagation. The arrest is temporary because of gross deformation of cross-sections at integral arrestors or the downstream pipes, and secondary failure accidents are likely to occur with any external perturbation.

A multi-mode failure boundary exploration and exploitation framework using adaptive kriging model for system reliability assessment

Various adaptive reliability analysis methods based on surrogate models have recently been developed. A multi-mode failure boundary exploration and exploitation framework (MFBEEF) was proposed for system reliability assessment using the adaptive kriging model based on sample space partitioning to reduce computational cost and use the characteristics of the failure boundary in multiple failure mode systems. The efficiency of the adaptive construction of kriging model can be improved by using the characteristics of the center sample of the small space to represent the characteristics of all samples in the small space. This method proposes a failure boundary exploration and exploitation strategy and a convergence criterion based on the maximum failure probability error for a system with multiple failure modes to adaptively approximate the failure boundary of a system with multiple failure modes. A multiple-failure-mode learning function was used to identify the optimal training sample to gradually update the kriging model during the failure boundary exploration and exploitation stages. In addition, a complex failure boundary-oriented adaptive hybrid importance sampling method was developed to improve the applicability of the MFBEEF method to small failure probability assessments. Finally, the MFBEEF method was proven to be effective using five system reliability analysis examples: a series system, a parallel system, a series–parallel hybrid system, a multi-dimensional series system with multiple failure modes, and an engineering problem with multiple implicit performance functions.

Reliability modeling of weighted-k-out-of-n: G system under multiple failure modes with dependent components

In engineering practice, some weighted-k-out-of-n: G systems often show multiple performance levels, resulting in multi-failure-mode mechanism. For these complex degradation systems, a novel reliability model with heterogeneous components is proposed on account of the number of working components, the total weight, and the sojourn time in some a stage, where different weights and thresholds are taken into account in different stages, and so do failure modes. After presenting the indispensable definitions of first passage time and the system lifetime, the system reliability indexes are derived by the Copula method to depict the dependence between components. Furthermore, maximum likelihood estimation of the Copula parameters is adopted and a Copula-based expression for component importance in each type is obtained. Finally, the reliability of the overall supercharging subsystem of an advanced fighter air energy system, as an example of weighted-k-out-of-n: G system with multi-failure modes is analyzed by the suggested model, which offers the comparison of independent and dependent cases. Meanwhile, sensitivity analysis is available to point out the effects on the system.

Failure mechanisms and acoustic emission pattern recognition of all-CFRP cylindrical honeycomb sandwich shell under three-point bending

The winding-based method is used for fabricating all carbon fiber reinforced plastic (CFRP) cylindrical honeycomb sandwich shell. The theoretical model of multi-failure modes of the cylindrical honeycomb sandwich shell is established under three-point bending. The multiple failure mechanisms of the honeycomb sandwich shell under three-point bending are revealed by the established three-dimensional failure mechanism map. The theoretical prediction of multi-failure modes is verified by experiments and finite element method. The acoustic emission (AE) technology is implemented to detect the evolution rule of internal damage of honeycomb sandwich shell. The damage patterns of the acoustic emission signals with different failure modes are identified by the unsupervised k-means clustering algorithm. The meso-damage mechanisms are also disclosed by the digital microscope. It was found that high peak frequency of the AE signals represents the face-core debonding failure. Finally, the optimal geometric design of the honeycomb sandwich shell under the bending load was carried out. The damage characteristics from mesoscale damage to macroscale failure provide a crucial foundation for discriminating multiple damage failure modes of structures.

Transfer learning and direct probability integral method based reliability analysis for offshore wind turbine blades under multi-physics coupling

Reliability of blades has a profound impact on both serviceability and safety of offshore wind turbines. Reliability estimation of wind turbine blade is a complex and time-consuming problem with multi-physics coupling and multi-failure modes correlation. Developing physics-of-failure modeling and reliability analysis methods with high efficiency and accuracy is a long-term challenge. In this work, a high availability and cost-effectiveness reliability estimation framework for offshore wind turbine blade by combining transfer learning (TL) and direct probability integral method (DPIM) is proposed. Extensive performance simulation of offshore wind turbine blade is a complex and necessary task, this paper develops a new adaptive sampling strategy to improve the validity of sample selection in the design space; On this basis, a physics-of-failure surrogate modeling approach is proposed by introducing TL method to fuse two kinds of multi-physics coupling analysis data, then the performance of all critical loads can be predicted efficiently in advance to provide a reliable design; Further, this paper provides an efficient reliability estimation method for offshore wind turbine blades by combining DPIM and surrogate model. Finally, the validity of the proposed approach is illustrated by numerical example and offshore wind turbine blade reliability estimation. The proposed framework provides a cost-effective alternative to higher loads simulation efforts and safety factors selection.

Remaining useful life prediction of lubrication oil by integrating multi-source knowledge and multi-indicator data

Insufficient data restrains the accuracy of the remaining useful life (RUL) prediction for lubricating oil. For this problem, multi-indicator modeling can be an effective solution. However, the unknown coupling effects of the indicators still impose difficulties. To address this issue, a coupling model integrating both knowledge and data is proposed. Originally, multi-source knowledge is embedded to guide both the state characterization and the threshold setting for improving the accuracy of the multi-indicator oil RUL prediction. In particular, the multi-indicator evidences and the knowledge-based rules, are adopted to handle overlapping information and inconsistent decision-making by considering interactions between indicators. A three-layer hierarchical model integrating evidence and rules is firstly established to connect the indicator, attribute, and oil state. Furthermore, the rule-base containing multi-failure modes is constructed to extract the random thresholds from the training samples. Finally, an exponential Wiener stochastic process embedding the oil degradation mechanism is used to describe data fluctuation. After the training stage, the probability density function of RUL is estimated. Finally, the proposed method is validated with four sets of test data from a real-world hydraulic pump bench test.

Extremum hybrid intelligent-inspired models for accurate predicting mechanical performances of turbine blisk

In complex aeroengine structures, it is necessary to consider multi-physical loads involving vibrational, structural, aerodynamical and heat loads for safe design. Turbine blisk commonly involves multi-failure modes such as stress, strain, deformation, fatigue, and creep under multi-physical loads during operation, so that efficient and accurate simulating approach is one of main issues in structural safety design. Machine learning (ML) approaches using artificial intelligence (AI) are workable to improve the simulation efficiency of mechanical responses under nonlinear dynamic structural analysis. In this work, hybrid artificial neural network (ANN) models are proposed to simulate the failure modes of turbine blisk. The accuracy of AI-based ANN is discussed using six music-inspired optimization algorithms named as harmony search (HS), improved HS (IHS), global-best HS (GHS), improved GHS (IGHS), adaptive GHS (AGHS) and Gaussian GHS (GGHS). Herein, these six music-inspired optimization algorithms are employed to find the undetermined coefficients and hyperparameters for the ANN models, and two adjusting strategy is introduced to explore the optimal values of the undetermined coefficients and hyperparameters in the IGHS, AGHS and GGHS. These hybrid models are used to perform the approximation of dimensional mechanical capacities such as stress, strain and deformation of aeroengine structures. These hybrid models are compared by using tendency, accuracy and agreement for failure modes of turbine system. It was conducted that the music-inspired basis harmony optimization procedures can provide the acceptable nonlinear connection between input and output responses of aeroengine turbine blisk in training phase of ANN models. The GGHS algorithm shows superior performance with the highest accuracy and tendency among other HS optimization algorithms.

A Weighted Collaborative Prediction Method of RUL for Integrated Circuits in Multi-Failure Modes Based on Transformer

Accurately predicting the Remaining Useful Life (RUL) of integrated circuits in multi-failure modes is critical for ensuring the safe and efficient operation of electronic devices. In this paper, we propose a novel RUL prediction method that combines a locally weighted regression method, a Transformer deep neural network, and a multi-failure modes weighted collaborative prediction approach. We first use a locally weighted regression method to eliminate noise from the original feature time series. Then, the feature time series is input into the proposed classification model to classify the failure modes and establish the failure mode weighted function. Finally, the feature time series is fed into the proposed regression model for RUL prediction with weighted processing to obtain accurate RUL predictions. Our experimental results demonstrate that our method effectively captures the degradation information of integrated circuits and generates precise RUL predictions. We demonstrate the efficacy of our method on a circuit stimulation model, showing that it outperforms several state-of-the-art RUL prediction methods. Our proposed method has the potential to enhance the reliability and safety of electronic devices in various applications.

Retracted: Spare Parts Stocking Decision Strategy and Service Logistics Cost Optimization of Two-Echelon Service Logistics System considering Multifailure Mode.

[This retracts the article DOI: 10.1155/2022/7607985.].

Multi-failure mode correlation and dynamic reliability evaluation of helical gears of ring die pellet mill

Multi-failure mode correlation and dynamic reliability evaluation of helical gears of ring die pellet mill

Risk Assessment of Dike Based on Risk Chain Model and Fuzzy Influence Diagram

For the risk assessment of flood defense, a comprehensive understanding of risk factors affecting dike failure is essential. Traditional risk assessment methods are mostly based on experts’ experience and focus on just one type of failure mode of flood defensive structures. The risk resources, including the analytical factors and non-analytical factors, were summarized firstly according to the general experience of dikes. The uncertainty of the resources that affect dike safety can be quantified by membership degree. Hence, a fuzzy influence diagram based on fuzzy mathematics was proposed to assess the safety of the dikes. We evaluated the multi-failure modes at the same time by a fuzzy influence diagram. Taking a dike as an example, the expected value of the dike failure was 6.25%. Furthermore, the chance of damage to this dike was “very unlikely” according to the descriptive term of the Intergovernmental Panel on Climate Change (IPCC). The evaluation result was obtained as a probabilistic value, which enabled an intuitive perception of the safety of the dikes. Therefore, we provided some reasonable suggestions for project management and regular maintenance. Since the proposed method can account for uncertainties, it is well suited for the risk assessment of dikes with obvious uncertainties.

Damage mechanisms of 2.5D SiO2f/SiO2 woven ceramic matrix composites under compressive impact

Fiber-reinforced SiO2f/SiO2 woven ceramic matrix composites (CMCs) are widely applied in the aeronautics and astronautics field due to their many physical and chemical advantages. However, compressive impact loads affect many application scenarios; thus, the damage morphologies and failure modes of these composites under compressive impact should be studied, particularly in the through-thickness direction. In this study, a comparative analysis using the split Hopkinson pressure bar (SHPB) experiment and finite element analysis (FEA) model revealed the damage mechanisms. The compressive impact was simulated in Abaqus/Explicit, and the cohesive element was selected to simulate cohesion of the interface according to the Hashin criterion for multi-mode failure. The results show that 2.5-dimensional SiO2f/SiO2 woven CMCs do not have sufficient plasticity to restrain the propagation of micro-cracks under high strain rates after the elastic stage under compressive impact. Many micro-cracks propagated and formed large cracks in the adiabatic shear zone. The adiabatic shear zones were generated when a compressive impact load was applied to the SiO2f/SiO2 CMCs at a high strain rate, and these impacts deformed the SiO2f/SiO2 CMCs. The adiabatic shear zone occurred at a high strain rate at the weakest point inside the fields of the SiO2f/SiO2 CMCs. The micro-cracks propagated and accumulated in this zone. Plastic fracture is the major failure mode for the SiO2f/SiO2 CMC specimens; this failure was characterized by fiber yarn fracture and the formation of micro-voids and micro-cracks due to matrix fracture. The large cracks, new voids, and interfacial debonding lead to the failure of the SiO2f/SiO2 CMCs. Combined action due to micro-crack formation and its propagation in the adiabatic shear band leads to a softening mechanism of the strain rate.

Seismic Fragility Analysis of Aqueduct Structural Systems Based on G-PCM Method

In order to accurately predict the seismic fragility of an aqueduct system, the General Product of Conditional Marginal (G-PCM) method was applied to the seismic fragility analysis of the aqueduct structural system, consisting of interrelated components such as the aqueduct body, pier, and support. First, a finite element dynamic analysis model of a three-span aqueduct with an equidistant simply-supported beam was established, based on the OpenSees platform. The uncertainties of structure, ground motion, and structural capacity were considered, and then the incremental dynamic analysis (IDA) method was used to calculate the seismic fragility of the three individual components, such as the aqueduct pier, the plate rubber bearing at the cap beam, and the PTFE sliding plate bearing at the aqueduct platform. Subsequently, seismic fragility curves of the aqueduct system were established using the G-PCM method and were compared with the traditional second-order bound method. The results showed that the two bearings of the aqueduct are more likely to be damaged than the pier; the failure probability of the aqueduct system is higher than that of any single component; and the seismic fragility curves of the aqueduct system acquired via the G-PCM method were all within the range of the failure probability obtained by the second-order bound method and had a better accuracy, which is suitable for the seismic fragility analysis of multi-failure mode aqueduct systems.