Multiple Failure Research Articles

A three-dimensional smoothed particle hydrodynamics analysis of multiple retrogressive landslides in sensitive soil

Numerical modelling and analysis of three-dimensional multiple retrogressive landslides of Saint-Jude landslide in sensitive clay is conducted. The investigations of the three-dimensional effects in determining the extent of landslides are concerned. To accurately simulate the extremely large deformation caused by progressive soil failure, a special form of SPH formulation known as finite particle method (FPM) is adopted. A hypoelastic–plastic model with strain softening is implemented to capture the key feature of the mechanical behaviour of the sensitive clay in undrained condition. The multiple progressive failure process, the final run-out stretch, and the retrogression distance of the Saint-Jude landslide are compared quantitatively with the data from site investigation. Furthermore, three-dimensional effects and influences of various factors, including slope morphology and soil properties, are discussed for better understanding of the development of retrogressive landslides in sensitive soil.

Mycobacterium tuberculosis sepsis with multiple intermuscular abscesses and respiratory failure as the main manifestations: a case report

BackgroundTuberculous sepsis is uncommon in individuals without human immunodeficiency virus (HIV) infection, and some patients may not exhibit clinical signs and symptoms of suspected sepsis upon admission, leading to delayed diagnosis and treatment.Case presentationThis report present the case of a 60-year-old female patient who presented with erythema, edema, and pain in her right upper limb accompanied by fever and chills. Further evaluation revealed multiple intermuscular abscesses caused by suspected gram-positive bacteria. Despite receiving anti-infection treatment, the patient rapidly progressed to septic shock and respiratory failure. Metagenomic next-generation sequencing (mNGS) analysis of blood samples detected Mycobacterium tuberculosis complex groups (11 reads). Additionally, mNGS analysis of fluid obtained from puncture of the abscess in the right upper extremity also suggested Mycobacterium tuberculosis complex groups (221 981 reads). Consequently, the patient was diagnosed with tuberculous sepsis resulting from hematogenous dissemination of Mycobacterium tuberculosis. Following the administration of anti-tuberculosis treatment, a gradual recovery was observed during the subsequent follow-up period.ConclusionIt is noteworthy that atypical hematogenous disseminated tuberculosis can be prone to misdiagnosis or oversight, potentially leading to septic shock. This case illustrates the importance of early diagnosis and treatment of tuberculosis sepsis. Advanced diagnostic techniques such as mNGS can aid clinicians in the early identification of pathogens for definitive diagnosis.

A Structural Reliability Analysis Method Considering Multiple Correlation Features

The paper analyzes the correlation features between stress strength, multiple failure mechanisms, and multiple components. It investigates the effects of different correlation features on reliability and proposes a method for structural reliability analysis that considers the joint effects of multiple correlation features. To portray the stress–strength correlation structure, the Copula function is utilized and the influence of the correlation degree parameter on reliability is clarified. The text describes the introduction of time-varying characteristics of structural strength and correlation parameters. A time-varying Copula is then constructed to calculate the structural reliability under the stress–strength correlation characteristics. Additionally, a time-varying hybrid Copula is constructed to characterize the intricate and correlation features of multiple failure mechanisms and components. The article proposes the variational adaptive sparrow search algorithm (VASSA) to obtain optimal parameters for the time-varying hybrid Copula. The effectiveness and accuracy of the proposed method are verified through actual cases. The results indicate that multiple correlation features significantly influence structural reliability. Incorporating multiple correlation features into the solution of structural reliability yields safer results that align with engineering practice.

A general phase-field model for simulating impact-sliding contact failure

Multiple failure modes of cyclic impact-sliding contacts, including brittle fracture, shear failure, and low-cycle fatigue, are investigated within the work. For this regard, a general thermo-elasto-plastic phase-field model is developed and validated that considers brittle-ductile failure mode transitions and fatigue degradation effects. And a temperature-dependent cyclic constitutive relation, combined with thermal softening, damage degradation, strain hardening, and fatigue degradation, is introduced to explain the cyclic thermo-elasto-plastic behavior of bearing steels. Combining the dynamic point/line contact model and the general phase-field model, the damage evolution and failure mode transitions of typical bearing parts under impact-sliding contacts are studied. The results indicate that cage-pockets and balls undergo low-cycle fatigue at low impact-sliding velocities, while guiding surfaces of the cage and ring are subjected to shear failure during cyclic high-speed sliding. Moreover, it is revealed that the abnormal phenomenon of more severe damage for the harder material than the softer material is due to multiple effects of strong thermal softening, damage degradation, and fatigue degradation.

Three-dimensional numerical simulation of dynamic strength and failure mode of a rock mass with cross joints

To study the dynamic mechanical properties and failure characteristics of intersecting jointed rock masses with different joint distributions under confining pressure, considering the cross angle α and joint persistence ratio η, a numerical model of the biaxial Hopkinson bar test system was established using the finite element method–discrete-element model coupling method. The validity of the model was verified by comparing and analyzing it in conjunction with laboratory test results. Dynamics-static combined impact tests were conducted on specimens under various conditions to investigate the strength characteristics and patterns of crack initiation and expansion. The study revealed the predominant factors influencing intersecting joints with different angles and penetrations under impact loading. The results show that the peak stress of the specimens decreases first and then increases with the increase of the cross angle. When α < 60°, regardless of the value of η, the dynamic stress of the specimens is controlled by the main joint. When α ≥ 60°, the peak stress borne by the specimens decreases with increasing η. When α < 60°, the initiation and propagation of cracks in the cross-jointed specimens are mainly controlled by the main joint, and the final failure surface of the specimens is composed of the main joint and wing cracks. When α ≥ 60° or η ≥ 0.67, the secondary joint guides the expansion of the wing cracks, and multiple failure surfaces composed of main and secondary joints, wing cracks, and co-planar cracks are formed. Increasing lateral confinement significantly increases the dynamic peak stress able to be borne by the specimens. Under triaxial conditions, the degree of failure of the intersecting jointed specimens is much lower than that under uniaxial and biaxial conditions.

Effects of limit state data on constructing accurate surrogate models for structural reliability analyses

Engineering problems are mainly defined in implicit processes; hence, the fully probabilistic analyses, e.g., Monte Carlo simulations (MCS), are expensive to implement. In practice, two approaches to overcome the issues are either reducing the size of simulations or developing surrogate models for actual problems. The latter does not sacrifice the size of MCS and requires less insight into probabilistic calculation; hence, it is preferable to most engineers. This study proposes an efficient framework to develop reliable and accurate surrogate models by considering data at the limit state margins (LS data). Effects of involving LS data in the training process and performances of the proposed metamodels are investigated for most issues relating to reliability analyses, including nonlinear performance functions, multiple failure modes, and implicitly defined problems. Two machine learning algorithms, including artificial neural networks and the Gaussian process, are employed to prove the ability of the proposed method. Investigations reveal that the limit state data plays a vital role in developing accurate surrogate models for reliability analyses, and accumulating them into the training dataset helps quickly construct accurate metamodels. This work contributes a practical framework for reliability analyses because the LS data can be detected easily without insight into probabilistic calculations.

A novel method of bilateral biliary decompression by EUS-guided hepaticogastrostomy with bridging stenting using the partial stent-in-stent method for reintervention of multiple metal stent failure

Video 1A novel method of bilateral biliary decompression by EUS-guided hepaticogastrostomy with bridging stenting using the partial stent-in-stent method for reintervention of multiple metal stent failure.We report a case in which anterior and posterior drainage was performed using the partial stent-in-stent method via the transpapillary approach. The patient had a bismuth type IV biliary obstruction, but only the right hepatic lobe was drained due to obstruction of the left portal vein. For the recurrent stent dysfunction, the patient underwent placement of a plastic stent within an uncovered self-expanding metal stent to correct stent dysfunction. A 7F plastic stent inside a metal stent is shown.The patient later experienced stent failure and jaundice due to tumor progression and was admitted for plastic stent replacement. Neither imaging results nor symptoms suggested duodenal stenosis. The transpapillary approach was attempted first but was unsuccessful. Duodenoscopy was challenging to perform because of duodenal stenosis. Fluoroscopy confirmed the duodenal stenosis. The plastic stent was extracted using an upper endoscope. Multiple uncovered metal stents are shown (1 stent in the anterior bile duct and 2 stents in the posterior bile duct). Jaundice did not resolve despite plastic stent removal.The patient refused to undergo percutaneous biliary drainage, so a decision was made to perform an EUS-guided hepaticogastrostomy (HGS) instead. The left bile duct was observed in the stomach. The left bile duct was punctured with a 19-gauge FNA needle. A 0.025-inch hydrophilic guidewire was directed into the left bile duct. Enhancement of the bile duct showing malignant hilar biliary obstruction (bismuth IV) is seen. Insertion of the guide wire into the posterior bile duct is shown.The stent mesh was then dilated using a balloon dilator. However, there was difficulty inserting the catheter. Additional dilation was performed using a spiral dilator. This instrument is a tapered tip dilator that fits into 0.025-inch guidewires and is expandable to 7F. Insertion of a second guidewire with a larger caliber was done to straighten the bile duct and help stabilize stent insertion. A 0.035-inch hydrophilic guidewire into the posterior bile duct using a double-lumen cannula and insertion of a 0.025-inch hydrophilic guidewire into the anterior bile duct are shown.The stent mesh was then dilated using a spiral dilator. A metal stent was placed through the anterior bile duct at a steep angle. Insertion and deployment of the first uncovered self-expanding metal stent (8× 60mm) from the anterior bile duct into the left bile duct is shown. Multiple metal stents were implanted into the hilar area, and the new stent was placed using the partial stent-in-stent method to prevent overexpansion. Guidewire seeking the posterior bile duct from inside the deployed stent through the stent mesh is shown.The stent mesh was then dilated using a balloon dilator. Insertion and deployment of an uncovered self-expanding metal stent (8× 60mm) from the posterior bile duct to the left bile duct using the partial stent-in-stent method is shown. Enhancement of the bile duct shows drainage from the right bile duct. The fistula of the HGS was only dilated with the spiral dilator. The risk of bile leakage was low, so we decided to implant a plastic stent. A 7F× 15-cm plastic stent was placed from the posterior bile duct into the stomach. Anterior and posterior segment drainage by EUS-HGS with bridging stenting using the partial stent-in-stent method is shown, with left segment drainage by EUS-HGS with the plastic stent.We performed EUS-HGS on a patient with multiple metal stents in place. There were no adverse events, and total bilirubin levels were reduced by more than half within 2 weeks. Six months have passed without stent dysfunction.

A treelike framework combining fault diagnosis and RUL prediction

Most existing deep learning methods consider the remaining useful life (RUL) prediction problem under a single failure mode and cannot solve the RUL prediction problem with multiple failure modes coexisting caused by component coupling in actual engineering systems. Thus, considering these issues, this paper proposes a novel tree network framework to address fault classification and RUL prediction in parallel, and the RUL prediction results are fused output, which are suitable for bearing RUL prediction with multiple faults. First, this paper develops a fault recognizer combining a frequency domain classifier and deep convolutional neural network to improve model selection accuracy. Secondly, this paper proposes a feature fusion algorithm based on the Gini coefficient, and the fused indicators are input into the RUL prediction sub-network for model training. Finally, the RUL sub-network prediction results are dynamically weighted and fused with the fault classification results to obtain the RUL based on SoftMax. The bearing dataset XJTU-SY is introduced to verify the efficiency of the proposed method, and computational results show that the developed framework can effectively predict RUL compared with other traditional methods, especially for RUL prediction under multiple failure modes.

A self-supervised learning framework based on masked autoencoder for complex wafer bin map classification

Wafer bin map (WBM) automatic classification is one of the critical challenges for semiconductor intelligent manufacturing. Many deep learning-based classification models have performed well in WBM classification, but all require a large amount of labeled data for training. Since real-world WBMs are highly complex and can be labeled correctly only by seasoned engineers, such requirements undermine the practical value of those methods. Several self-supervised learning methods have recently been proposed for WBM to improve classification performance. However, they still require much labeled data for fine-tuning and are only adapted for binary WBM with a single gross failure area. To address these limitations, this study introduces a self-supervised framework based on masked autoencoder (MAE) for complex WBMs with mixed bin signatures and multiple gross failure area patterns. A patchMC encoder is proposed to improve MAE’s representation ability for complex WBMs with mixed bin signatures. Moreover, the pre-trained MAE encoder with a multi-label classifier fine-tuned by labeled WBMs enables a few-shot classification of complex WBMs with multiple gross failure areas. Experimental validation of the proposed method is performed on a real-world complex WBM dataset from Intel Corporation. The results demonstrate that the proposed method can make good use of unlabeled WBMs and reduce the demand for labeled data to a few-shot level and, at the same time, guarantees a classification accuracy of more than 90%. By comparing MAE with other self-supervised learning methods, MAE outperforms other existing self-supervised methods for WBM data.

Structural Expected Lifetime Estimation for Systems With Multiple Failure Modes Based on Adaptive Learning Kriging Models

Structural Expected Lifetime Estimation for Systems With Multiple Failure Modes Based on Adaptive Learning Kriging Models

Two-Phase Adaptive Kriging Model Based Importance Sampling Method for Estimating Time-Dependent Failure Probability

To improve the computational efficiency of the time-dependent failure probability (TDFP) to system with dynamic loads, materials degeneration and other time-varying factors, a two-phase adaptive Kriging model-based importance sampling (IS) method is proposed. The main contribution of this article is that a single Kriging model is established and adaptively refined to gradually approach the optimal IS probability density function (PDF) and estimate TDFP in two sequential phases. In the first phase, a generally approximate IS PDF is constructed by Kriging model based on its prediction characteristic and subsequently updated until convergence. Then, a simple rejection sampling technique is used to robustly generate IS samples for the estimation of TDFP. In the second phase, current Kriging model is subsequently refined for accurately identifying the classification of the IS samples generated in the last phase. Finally, the TDFP can be estimated using the products obtained in the above two phases. As the IS PDF established by Kriging model is used for directly approximating the optimal one, the proposed method has the capacity to deal with the complicated problem containing multiple failure regions. Additionally, since IS procedure can dramatically improve the sampling efficiency, the proposed method is an effective technique to the time-consuming TDFP analysis problems.

Abnormal data detection and recovery of sensors network based on spatiotemporal deep learning methodology

This paper proposes a novel deep-learning-enabled framework for abnormal data detection and recovery considering spatial-temporal correlation among sensors. The wavelet scattering transform is adopted to replace the first layer of one-dimension convolutional neural network classification model. The multiple typical sensor failure modes of sensor are identified including bias, drifting, gain, precision degradation, and three complete faults (constant; constant + noise; noise). The bidirectional long-short time memory (BLSTM) network is developed to recover abnormal data through positive and negative propagation. The BLSTM cell involving forget, input, and output gate is utilized to adaptively transmit valuable information of long-term data. Different sensor configurations are conducted to select the optimal input sensors of BLSTM model. The proposed models are then validated by three engineering cases. The results show that the failure mode diagnosis and recovery accuracy are over 90 %. Sensor selection has a large impact on the data reconstruction, and selecting sensors with higher correlation can achieve a higher accuracy. In addition, the data recovery accuracy of the proposed BLSTM model surpasses that of traditional long-short time memory models.

Exploring the effectiveness of endometrial receptivity array and immune profiling in patients with multiple implantation failure：A retrospective cohort study based on propensity score matching

This study aimed to evaluate the effectiveness of the endometrial receptivity array (ERA), endometrial immune profiling, and a combination of both in improving the pregnancy outcomes for multiple implantation failure patients. According to patients’ willingness, 1429 women who incurred at least two or more consecutive implantation failures in IVF/ICSI treatment opted for frozen embryo transfer and were divided into four groups: ‘No test’, ‘Immune Profiling’, ‘ERA’ and ‘ERA+ Immune Profiling’. Women in three test groups underwent timed endometrial biopsy for ERA, immune profiling, a combination of both. We observed the overall incidence rates of the displaced window of implantation (WOI) and endometrial immune dysregulation were 75.14% and 79.29%, respectively. After 1:1 propensity score matching (PSM), our data revealed that the ‘ERA’ and ‘ERA + Immune Profiling’ groups demonstrated significantly higher rates of biochemical, clinical, ongoing pregnancy, and implantation compared to the ‘No test’ group (p < 0.01). The ‘Immune Profiling’ group showed a higher implantation rate compared to ‘No test’ group (p < 0.05). Furthermore, when comparing three test groups, the ‘ERA + Immune Profiling’ group exhibited notably higher rates of clinical and ongoing pregnancy compared to the ‘Immune Profiling’ group (p < 0.017). However, there was no association between endometrial immune profiling and ERA phases, and their results did not differ between embryo implantation and non-implantation in these patients. Our findings underline the increased implantation rates by use of ERA and endometrial immune profiling in patients with multiple implantation failure, either individually or corporately. Moreover, a combination of both could improve their pregnancy outcomes significantly.

Shock fatigue damage failure boundary study of BGA solder joints based on shock response spectrum

Multiple shock damage failure of ball grid array (BGA) solder joints is one of the important reasons for reusable spacecraft mission failure. Shock fatigue damage failure boundary of BGA solder joints is proposed based on shock response spectrum (SRS), which can be used to assess the damage failure state. Weibull distribution is employed to characterize the shock fatigue life for a specified SRS. A Bayesian estimation method based on the Bootstrap approach is proposed to estimate Weibull distribution parameters. By using the time-domain load synthesized method based on wavelet and the experimentally validated shock fatigue life model, the shock fatigue life data of BGA solder joints corresponding to different SRS are extended. The variation patterns of the scale and shape parameters with the SRS parameters is analyzed. Then the shock fatigue damage failure boundary based on SRS is determined for a given design life and reliability. The results show that the critical SRS amplitude increase with the rise of the inflection point frequency and the low-frequency slope, respectively. The developed method is expected to provide references for the environment-adaptive design of BGA solder joints in reusable spacecraft.

Multimodal recognition and prognostics based on features extracted via multisensor degradation modeling

Remaining useful life (RUL) prediction is an important issue in prognostics and health management (PHM). Numerous studies have been conducted to construct degradation models for RUL prediction. However, their models fail to handle the scenarios where multiple failure modes exist, especially when the failure modes are unknown (unlabeled) beforehand and need to be recognized. This paper develops a multimodal recognition and prognostic method based on features extracted via multisensor degradation modeling. Specifically, we assume the failure mode of a unit follows a multinomial distribution. Given the failure mode distribution, we characterize the degradation status of the unit via degradation models based on each sensor signal and a constructed health index (HI). Our innovative idea is to extract features as the derivatives of the degradation status to comprehensively utilize information from multiple sensors for more effective failure mode recognition and RUL prediction. We develop a fusion coefficient-integrated expectation-maximization (FCIEM) algorithm to estimate model parameters by using data from historical units. Finally, we recognize the failure mode and predict the RUL of in-service units based on their extracted features and degradation status. Numerical experiments and a case study of aircraft engines were conducted to evaluate the performance of our proposed method.

A novel reliability analysis methodology based on IPSO-MCopula model for gears with multiple failure modes

In order to accurately and quickly predict the failure probability of gears with multiple failure modes, a novel reliability analysis methodology based on the mixed Copula (MCopula) function model is proposed to deal with the complex correlation among different failure functions. Firstly, we construct a novel MCopula model based on three famous Copula functions: Gumbel Copula, Clayton Copula, and Frank Copula functions. Secondly, we use and improve the particle swarm optimization (PSO) method to optimally calculate the weight coefficients in the proposed MCopula model. Thirdly, the maximum likelihood estimation (MLE) method is adopted to estimate related parameters in the proposed MCopula model. Finally, we verify the proposed reliability analysis methodology with a standard life-prediction case of a strut system and a practical life-problem case of a gear pair system. Comparison results of both cases show that, by using the proposed methodology, the failure probability of a gear pair system with multiple failure correlations can be quickly calculated through a small number of samples and can be estimated as accurately as that by the Monte Carlo scheme. Consequently, our proposed novel methodology successfully analyzes the reliability problems for a gear pair system with multiple failure modes. The proposed methodology can be further applied to solve the reliability problem for other machine parts.

Stability Analysis of Geosynthetic-Reinforced Slopes Considering Multiple Potential Failure Mechanisms Based on the Upper Bound Theorem

Stability Analysis of Geosynthetic-Reinforced Slopes Considering Multiple Potential Failure Mechanisms Based on the Upper Bound Theorem

Reliability evaluation of components with multiple failure modes based on mixture Weibull distribution using expectation maximization algorithm

Reliability evaluation of components with multiple failure modes based on mixture Weibull distribution using expectation maximization algorithm

Small Female Occupant Response in Reclined and Upright Seated Postures in Frontal Impacts.

The objective of this study was to compare the kinematics of the head-neck, torso, pelvis, and lower extremities and document injuries and their patterns to small female occupants in frontal impacts with upright and reclined postures using an experimental model. Six postmortem human surrogates (PMHS) with a mean stature of 154 ± 9.0 cm and mass of 49 ± 12 kg were equally divided between upright and reclined groups (seatback: 25 deg and 45 deg), restrained by a three-point integrated belt, positioned on a semirigid seat, and exposed to low and moderate crash velocities (15 km/h and 32 km/h respectively). The response between the upright and reclined postures was similar in magnitude and curve morphology. While none of the differences were statistically significant, the thoracic spine demonstrated increased downward (+Z) displacement, and the head demonstrated an increased horizontal (+X) displacement for the reclined occupants. In contrast, the upright occupants showed a slightly increased downward (+Z) displacement at the head, but the torso displaced primarily along the +X direction. The posture angles between the two groups were similar at the pelvis and different at the thorax and head. At 32 km/h, both cohorts exhibited multiple rib failure, with upright specimens having a greater number of severe fractures. Although MAIS was the same in both groups, the upright specimens had more bi-cortical rib fractures, suggesting the potential for pneumothorax. This preliminary study may be useful in validating physical (ATDs) and computational (HBMs) surrogates.

Failure Modes of Flexible LiCoO2 Cathodes Incorporating Polyvinylidene Fluoride Binders with Different Molecular Weights.

Understanding the mechanical failure modes of lithium-ion battery [Li-ion batteries (LIBs)] electrodes is exceptionally important for enabling high specific energy and flexible LIB technologies. In this work, the failure modes of lithium cobalt oxide (LCO) cathodes under repeated bending and the role of the polymer binder in improving the mechanical durability of the LCO electrodes for use in flexible LIBs are investigated. Mechanical and electrochemical evaluations of LCO electrodes (areal capacity of ≥2.5 mA h cm-2) employing poly(vinylidene fluoride) (PVDF) binder were carried out, followed by extensive optical and electron microscopies. We find that the molecular weight (MW) of the PVDF significantly influenced the surface and bulk microstructure of the LCO electrodes, particularly the distribution of carbon additive and binder, which plays a crucial role in affecting the mechanical and electrochemical properties of the electrodes. Multiple mechanical failure modes (e.g., surface scratches and microcracks) observed in the LCO electrodes subjected to repeated bending originated from the use of low MW PVDF; these failure modes were successfully mitigated by using a high MW PVDF. Remarkably, the optimized flexible LCO electrode incorporating high MW PVDF showed comparable discharge capacity retention during galvanostatic cycling after repeated bending (7000 cycles at 50 mm bending diameter) to electrodes not subjected to the repeated bending. This study highlights the importance of carrying out a comprehensive investigation of the failure mechanisms in flexible electrodes, which identified the pivotal role of the PVDF MW in the electrode microstructure and its effects on the electrode resilience to failure during repeated bending.