Multiple Failure Modes Research Articles

A new framework for system reliability-based design from quantile value perspective

This study develops a new framework for system reliability-based design (SRBD) from a quantile value perspective. The design parameter of interest is treated as a variable and separated from each limit state function, enabling the failure threshold of the design parameter for each failure mode to be obtained. The so-called system failure threshold is defined as the maximum or minimum individual failure thresholds and a single run of MCS is employed to obtain the cumulative distribution function (CDF) of the system failure threshold. An SRBD aiming at any failure probability is performed by finding an upper or lower quantile value on the determined CDF. Four illustrative examples including a simple beam, a semi-gravity retaining wall, a cantilever retaining wall, and a soil-nailed wall are used to demonstrate the implementation procedures. The results are validated according to direct Monte Carlo simulations. The proposed approach is applicable when the design parameter is monotonically related to the original performance functions concerning multiple failure modes. The proposed approach can play a complementary role to the existing SRBD methods due to its simplicity and efficiency. Complex optimization or iteration procedures involved in the conventional SRBD methods are not required.

The motion reliability analysis of a series system with multiple coordinated manipulators

Coordinated manipulators can construct a series system, such that all the position error of the end-effector is required to remain within the permissible limit. This article proposed a new method to handle the problem of reliability analysis involving in multiple failure modes. The extreme value of the position error is introduced to construct a surrogate model to replace the original failure event, the moment with fraction order instead of the traditional integer is applied to derive the best-fit distribution of the extreme value based on the maximum entropy principle. The innovations mainly include: (1) the high efficient dimension reduction via the surrogate model constructed with the extreme value and (2) a better probability density function is derived through the fractional moment. A series system consists of three manipulators is used as an example to demonstrate the efficiency and accuracy of the proposed method.

A systematic method of remaining useful life estimation based on physics-informed graph neural networks with multisensor data

Data-driven models, especially deep learning models, are proposed for remaining useful life (RUL) estimation with multisensor signals. Various treatments to reduce data sensitivity, addressing the difficulty of learning dynamic topologies, and coping with the lack of engineering physics guidance for model training limit the performance of these models and their use. This study proposes a systematic method to estimate RUL with multisensory data under dynamic operating conditions and multiple failure modes. Firstly, ARMA regression is introduced into the graph convolutional network(GCN) model. This allows the information loss in the GCN model following training to be lifted with low computational complexity. Secondly, the physics equations of balancing for economy and security in preventive maintenance policies is introduced in the loss function for training. This involves in a way to impose a higher penalty on delayed predictions, so to focus the neural network training on the control of high-risk situations. Finally, the method is validated on the popular C-MAPSS dataset. Compared with other cutting-edge methods, the proposed method can ensure high-fitting accuracy with strong security. In practice, the controllability and flexibility of deep learning models are enhanced, ensuring the reduction of high-risk, uncertain situations while sacrificing as little accuracy as possible.

A novel technique for multiple failure modes classification based on deep forest algorithm

A novel technique for multiple failure modes classification based on deep forest algorithm

Pipelines reliability assessment considering corrosion-related failure modes and probability distributions characteristic using subset simulation

A methodology using the subset simulation and the modified Metropolis-Hastings algorithm with delayed rejection (SS-MMHDR) is developed to evaluate the time-dependent reliability of corroded pipelines under multiple failure modes. This methodology considers the competition between the small leak and burst failures and the correlated non-normal distribution of the defect variables, and characterizes the defect geometric probability distribution from extensive inspection data. Results demonstrate that SS-MMHDR has high accuracy and low variability in evaluating the pipeline failures, outperforming the SS-MMH and Monte Carlo simulation algorithms, especially under low probability levels, but with slightly increased computation time. The Generalized Extreme Value Type II (GEVII) distribution is the optimal distribution for internal corrosion defect lengths from the Kolmogorov-Smirnov test. Lognormal, Weibull and Normal distributions provide more conservative estimates on burst and composite failure probabilities and limiting maintenance times than GEVII. Effects of operating pressure and its variability, corrosion rate on failure probability, and pipeline limiting maintenance time are also revealed through numerical cases.

Active learning-based structural reliability evaluation Kriging model and sequential importance sampling

The efficient analysis of structural system reliability with multiple failure modes and small failure probabilities presents significant challenges for active Kriging methods. This paper proposes a novel method, PAK-SEIS, which combines the Parallel Active learning Kriging model and the Sequential Importance Sampling method to overcome these challenges. The proposed method includes a new sequential importance sampling method that integrates the sequential Monte Carlo simulation (MCS) and kernel density estimation to construct the optimal importance density. Additionally, the proposed parallel learning strategy allows for the selection of multiple new training samples, reducing performance function calls and refining iterations of Kriging models. The PAK-SEIS method gradually approximates the optimal importance density by updating multiple new training samples and failure modes in parallel at each iteration based on a small number of candidate samples. Candidate samples generated in the final sequence can cover all limit state surfaces, and all Kriging models are accurately constructed. The PAK-SEIS method can effectively address system reliability problems with small failure probability, multiple failure regions, and implicit functions. Two examples demonstrate the efficiency and accuracy of the proposed PAK-SEIS method.

Risk Factors for All-Cause Early Reoperation Following Tumor Resection and Endoprosthetic Reconstruction: A Secondary Analysis from the PARITY Trial.

Oncologic resection and endoprosthetic reconstruction of lower-extremity musculoskeletal tumors are complex procedures fraught with multiple modes of failure. A robust assessment of factors contributing to early reoperation in this population has not been performed in a large prospective cohort. The aim of the present study was to assess risk factors for early reoperation in patients who underwent tumor excision and endoprosthetic reconstruction, with use of data from the Prophylactic Antibiotic Regimens in Tumor Surgery (PARITY) trial. Baseline characteristics were assessed, including age, sex, tumor type, tumor location, presence of a soft-tissue mass, diabetes, smoking status, chemotherapy use, and neutropenia. Operative factors were recorded, including operative time, topical antibiotics, silver-coated prosthetics, endoprosthetic fixation, extra-articular resection, length of bone resected, margins, tranexamic acid, postoperative antibiotics, negative-pressure wound therapy, and length of stay. Univariate analysis was utilized to explore the differences between patients who did and did not undergo reoperation within 1 year postoperatively, and a multivariate Cox proportional hazards regression model was utilized to explore the predictors of reoperation within 1 year. A total of 155 (25.7%) of 604 patients underwent ≥1 reoperation. In univariate analysis, tumor type (p < 0.001), presence of a soft-tissue mass (p = 0.045), operative time (p < 0.001), use of negative-pressure wound therapy (p = 0.010), and hospital length of stay (p < 0.001) were all significantly associated with reoperation. On multivariate assessment, tumor type (benign aggressive bone tumor versus primary bone malignancy; hazard ratio [HR], 0.15; 95% confidence interval [CI], 0.04 to 0.63; p = 0.01), operative time (HR per hour, 1.15; 95% CI, 1.10 to 1.23; p < 0.001), and use of negative-pressure wound therapy (HR, 1.93; 95% CI, 1.30 to 2.90; p = 0.002) remained significant predictors of reoperation within 1 year. Independent variables associated with reoperation within 1 year in patients who underwent tumor resection and endoprosthetic reconstruction included tumor type (benign aggressive bone tumor versus primary bone malignancy), operative time, and use of negative-pressure wound therapy. These results will help to inform patients and surgeons regarding the risk of reoperation by diagnosis and reinforce operative time as a factor influencing reoperation. These results also support further investigation into the use of negative-pressure wound therapy at the time of surgery in this patient population. Therapeutic Level II. See Instructions for Authors for a complete description of levels of evidence.

Seismic fragility assessment of storage tanks considering different sources of uncertainty

Liquid storage tanks are critical facilities in petroleum depots that must remain intact and functional following an earthquake. The seismic fragility function is a useful tool for quantifying structural vulnerability. This study accordingly explored the effects of different uncertainties on the seismic fragility of a case study floating-roof storage tank by applying an appropriate approach to seismic fragility analysis. First, an orthogonal sensitivity analysis was performed using a finite element model to determine the random model variables most affecting the seismic responses of the storage tank. Considering the different sources of uncertainty, seismic fragility was subsequently evaluated by combining a cloud analysis with model sampling. Then, the seismic fragility curves of the storage tank system were developed by studying the correlations among multiple failure modes. The results indicate that the computational requirements of the present method were significantly less demanding than those of direct Monte Carlo simulation. Geometric uncertainty was found to exert the greatest impact on the seismic responses. The maximum difference among the fragility function parameters for different sources of uncertainty was 28.9%. The damage assessment of the tank system was shown to be more reasonable when considering the correlations among multiple failure modes. This analysis technique can accordingly provide practical guidance for the seismic design of vertical floating-roof storage tanks.

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.

A Physics-informed Neural Network for Wind Turbine Main Bearing Fatigue

Unexpected main bearing failure on a wind turbine causes unwanted maintenance and increased operation costs (mainly due to crane, parts, labor, and production loss). Unfortunately, historical data indicates that failure can happen far earlier than the component design lives. Root cause analysis investigations have pointed to problems inherent from manufacturing as the major contributor, as well as issues related to event loads (e.g., startups, shutdowns, and emergency stops), extreme environmental conditions, and maintenance practices, among others. Altogether, the multiple failure modes and contributors make modeling the remaining useful life of main bearings a very daunting task. In this paper, we present a novel physics-informed neural network modeling approach for main bearing fatigue. The proposed approach is fully hybrid and designed to merge physics-informed and data-driven layers within deep neural networks. The result is a cumulative damage model where the physics-informed layers are used model the relatively well-understood physics (L10 fatigue life) and the data-driven layers account for the hard to model components (i.e., grease degradation).

An improved active Kriging method for reliability analysis combining expected improvement and U learning functions

The reliability assessment of structures with multiple failure modes and small failure probability is challenging due to the time-consuming simulations required. Active learning Kriging methods for structural reliability with multiple failure modes have shown high computational efficiency and accuracy. However, selecting the appropriate sample and its failure mode to update the Kriging models remains a key problem. In this paper, we propose a new learning function and stopping criterion to further improve the efficiency of structural system reliability analysis. Firstly, we propose a new learning function that combines the expected improvement function and the U learning function. This function selects the most suitable samples, balancing the degree of expected improvement of samples to the limit state surface and the degree of misclassification probability of samples. Secondly, we propose a new stopping criterion that considers both the accurate construction of limit state surfaces and the probability of accurately predicting the signs of samples. This criterion avoids premature or late termination of the active learning process. Thirdly, the sequential MCS simulation method is employed in the active learning process to efficiently evaluate small failure probability problems. By analyzing four examples, we verify the accuracy and efficiency of the proposed structural reliability analysis method.

Lumped mass models for use in predicting collapse of an isolated building

Development of fragility functions is an importantstep in the seismic evaluation of isolated buildings. To develop collapse fragility curves, a series of detailed dynamic ground motion analysis with multiple intensity measure levels must be conducted, often requiring considerable computational time. In addition, including more modeling detail to enable the prediction of multiple failure modes can make numerical convergence more challenging. Therefore, the possibility of simplified models to predict the collapse probabilityof isolated buildings is attractive. The collapse fragility curves derived from two lumped mass models with differing degrees of simplification are compared to a full nonlinear model. This is done for both sliding and rubber bearings with varying moat wall or restraining rim (for sliding bearings). The comparison demonstrates that the simplified model proposed in this study can be a potential alternative for estimating fragility functions of isolated buildings, as they can significantly improve the computational efficiency withrelatively smallcompromises on accuracy. This will allow for faster validation of system design when incorporating performance targets under large earthquakes.

Efficient System Reliability Analysis for Layered Soil Slopes with Multiple Failure Modes Using Sequential Compounding Method

Evaluating the system reliability of layered soil slopes is a challenging issue because multiple failure modes may be included along the slip surfaces, which makes the overall failure probability greater than any individual slip surface. In this paper, an efficient system reliability analysis concerning the layered soil slopes is conducted based on the sequential compounding method (SCM) that has the ability to compound multiple failure events into an equivalent event sequentially. First, the first order reliability method (FORM) is employed to quantify initial reliability indices and correlation coefficients among these failure modes. Subsequently, the SCM is used to calculate the equivalent reliability indices and correlation coefficients until the multiple failure events are reduced to a compound event, and then the system reliability of the slope is obtained accordingly. The application of the approach to probabilistic evaluation of layered slopes is illustrated by two typical examples, and the correctness is verified by a Monte Carlo simulation (MCS). The results show that the SCM can deliver accurate system failure probability and greatly improve the computational efficiency compared with the MCS, which is an advantageous and promising strategy in evaluating the system reliability of layered soil slopes.

Reliable composite fault diagnosis of hydraulic systems based on linear discriminant analysis and multi-output hybrid kernel extreme learning machine

With increasingly stringent in requirements on the reliability and safety of hydraulic systems, data-driven fault diagnosis has emerged as a popular area of research. Hydraulic systems may have multiple failure modes, and accurately diagnosing compound failures in multi-component systems is a daunting task. In this paper, a method of multi-output classification by combining linear discriminant analysis (LDA) with the hybrid kernel extreme learning machine (HKELM) is proposed to diagnose compound faults in hydraulic systems. Data selection based on LDA is used in place of expert knowledge to screen out sensitive channels of each component from multi-channel signals. The multi-output strategy is embedded into the HKELM, which can simultaneously output the fault status of multiple components to diagnose the health of the system. An improved Hamming loss is also proposed to evaluate the total error in the multi-output classification because it has greater applicative relevance than classification accuracy. The results of experiments show that the proposed method can diagnose composite faults in multi-component systems with an accuracy higher than 99.5% and an error of only 0.20% on a dataset of hydraulic systems. As a shallow feed-forward network model, it can be used for real-time fault diagnosis due to its efficiency.

A mechanism reliability analysis method considering environmental influence and failure modes’ correlation: a case study of rifle automaton

In order to overcome the challenge of quantifying the influence of environmental conditions and the coexistence of multiple failure modes involved in mechanism reliability modelling under different environments. In this paper, we propose a method for the analysis of mechanism reliability that takes into account the influence of environmental factors and failure modes’ correlation, quantifies the influence of environmental factors as the random distribution and degradation path of parameters, and derives the Copula description of failure mode correlation from the historical data of environmental experiments. On the basis of the discrete mechanism dynamics model, the output parameters of the characteristic points are calculated, and the failure rate of each failure mode is calculated based on the failure criterion and the performance margin theory. Additionally, the dynamic change pattern of the mechanism reliability is compared with the Kaplan-Meier estimation of the corresponding environmental test history data to assess the validity of the calculation results. The reliability modelling problem of a motion mechanism of an automatic rifle automaton in a high and low temperature environment is applied to the method, and the reliability calculation results are close to those of Kaplan-Meier estimation of the test history data, and all are within the upper and lower bounds given by the reliability boundary theory, demonstrating the method's validity.

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 Analysis and Accelerated Test Development for Rotor Magnetic Bridge of Electric Vehicle Drive Motor

Motor rotor magnetic bridges operate under multiple physical field loads, such as electromagnetic force, temperature, and centrifugal force. These loads can cause fatigue and aging failure of the bridges, especially when the rotor is operating continuously at high speeds and high temperatures. Therefore, the failure analysis and accelerated test cycle development of magnetic bridges is a major aspect of their reliability evaluation. This paper studies rotor multi-physics load transfer characteristics and establishes a rotor magnetic bridge failure physical model. A simulation analysis is conducted from the electromagnetic field, thermal field, structural field, and thermomechanical coupling field to determine the risk point load responses and failure-dominant loads. In addition, the accuracy of the simulation model is verified by actual bench tests. Considering the influence on the rotor bridge’s life under the coupling of multiple failure modes, the fatigue failure model under alternating loads and the fatigue aging coupling failure model are established, respectively. Through a damage analysis, the whole life cycle damage targets for both failure modes are determined, and the test condition levels are screened based on the load frequency distribution and damage distribution. The multi-objective optimization method is used to calculate the number of test cycles and finally develop accelerated test cycle conditions that can reproduce multiple failure modes. This research can provide support for rotor bridge reliability design and verification, as well as product quality development.

Reliability Updating with a System Reliability Method Based on Adaptive Kriging

Reliability updating can improve the failure probability estimates of structural systems when new observations are obtained. At present, an efficient reliability updating method is one that transforms the equality observation information into the inequality type by introducing an auxiliary variable and then employs structural reliability methods to perform reliability updating. For this method, the computation of posterior failure probability Pr(F|Z), where Z is an observation event, requires two reliability analysis processes to estimate Pr(Z) and Pr(F∩Z), respectively. To improve the efficiency, this paper develops a reliability updating method with output observations, where the computation of Pr(Z) and Pr(F∩Z) is integrated into one system reliability problem, which is then solved by adaptive kriging with truncated candidate region (AKTCR). A composite adaptive learning process is developed to construct the kriging model, which can provide accurate estimates for both Pr(Z) and Pr(F∩Z). In this way, the proposed method can also deal with the problem of multiple failure modes and multiple observation events. In addition, a combined global and local sampling method is embedded into the proposed algorithm to reduce the size of candidate sample pool and improve the modeling efficiency. Finally, three examples can prove the efficiency and accuracy of the proposed reliability updating method.

System dynamic reliability evaluation of multiple failure modes of earth dams subjected to strong earthquake excitation

System dynamic reliability evaluation of multiple failure modes of earth dams subjected to strong earthquake excitation

Analytical Probabilistic Methods to Analyze the Stability of the Rock Wedge with Multiple Failure Modes

Analytical Probabilistic Methods to Analyze the Stability of the Rock Wedge with Multiple Failure Modes