Multiple Failure Modes Research Articles

Expected lifetime prediction for time- and space-dependent structural systems based on active learning surrogate model

Predicting the expected lifetime of structural systems is fundamental for effective design and maintenance. However, existing methods based on physics overlook critical spatial considerations, particularly those related to random field inputs. In this study, we propose a new active learning surrogate-based method to predict the expected lifetime of structural systems affected by both time and space factors. The method starts by training an initial Kriging model with random samples and time-space coordinates. An extended expected feasibility function is proposed to select new training samples and their corresponding time-space coordinates, refining the Kriging model. A novel parallel learning strategy is proposed to expedite computations. By using the constructed Kriging model and the first failure time of random samples, we can predict the expected lifetime. The proposed method is adaptable to structural systems with multiple failure modes, implicit functions, and random field variations. The efficiency and accuracy of the proposed method are validated through four examples.

Centrifuge modeling of loess slope failure induced by rising water level utilizing intact sample

Understanding the failure process of bottom-saturated loess slopes is of great significance in loess areas where flood irrigation is commonly utilized to alleviate the scarcity of precipitation. In this study, the failure process of a loess slope with an increased groundwater level was recreated and the multiple failure modes of loess landslides were revealed. A centrifugal model test was conducted using a groundwater-recharge device. An intact loess sample retrieved from the Heifangtai Loess Terrace was employed in the test. The model test was monitored using a video recorder, high-speed camera, and soil/pore water pressure sensors, and the results were validated by utilizing an intermittently investigated field landslide. Based on the monitored data and acceleration, the test was divided into three periods: the initial acceleration period with no water inlet (0–40 g), bottom saturation period (40–60 g), and failure occurrence period (60–80 g). The soil/pore water pressure and degree of deformation were relatively low, with a steadily increasing trend during the first two periods. With the enrichment of the soil water content, retrogressive sliding, deep subsidence, and surface sinkhole failures occurred successively up to areas with relatively high pore water pressure during the last period. The results of the field landslide investigation showed multiple failure modes, as observed in the model test. The results suggest that the coexistence of multiple failure modes could gradually evolve into croplands and promote water infiltration into the deep loess, increasing the groundwater level and accelerating the failure process of the slope. Despite the overall effects of multiple failure patterns on the evolution of the slope, each failure pattern had a relatively independent evolutionary process within a certain area, which could be further analyzed for the early recognition of loess landslides. This study also indicates that the challenges of centrifuge modeling for water-related materials with intact soil samples are the boundary conditions and data monitoring within the model.

A reliability analysis method for electromagnet performance degradation based on FMEA and fuzzy inference system

AbstractElectromagnets are often used in indirect control for industrial applications. The ability of an electromagnet to control objects should decrease with performance degradation. And electromagnets product poses a danger to people and objects in the working environment. So, it is very difficult to analyze the reliability of electromagnetic performance degradation because of the complicated working condition. Failure Mode and Effect Analysis (FMEA) is the most commonly used tool for product reliability analysis. The new version of FMEA uses integer as evaluation value, which cannot represent the hesitation psychology of the evaluator. The Action Priority (AP) table of the FMEA describes the relationship between the evaluation of influencing factors and the risk level of the failure mode, which provides rules for determining the risk level of the failure mode. However, the AP table may result in multiple failure modes having the same ranking, which does not align with the intention of FMEA to prevent failures. Therefore, this paper proposes a reliability analysis method for electromagnetic performance degradation based on FMEA and FIS. Firstly, the Double Hierarchy Hesitant Fuzzy Linguistic Term Set (DHHFLTS) is used as the evaluation language to describe the hesitation psychology of evaluators. Secondly, the AP table of FMEA is used as FIS fuzzy inference rule. In this way, the idea of FMEA to determine the risk level of failure mode is retained and the problem of FIS fuzzy rule making is overcome. Then, FIS defuzzification AP table inference results to determine the risk ranking of failure modes. This avoids situations where the order of failure modes is equal. Finally, a performance degradation model of the electromagnet is constructed based on the Wiener process, and the calculation results of the new method are verified.

Reliability analysis of aerospace planetary roller screw mechanism considering multiple failure modes

Abstract Faults in planetary roller screw mechanisms under typical aerospace operating conditions are usually caused by the combined effects of multiple coupled failure modes. Reliability analysis methods that ignore correlation often lead to significant errors. In this paper, the failure models of the planetary roller screw mechanism under fatigue elastic failure, wear failure, and fracture failure are analyzed based on the stress-strength interference model, and failure functional functions are provided. The improved O. Ditlevsen second-order reliability bound theory is used to characterize the relationships between multiple failure modes. Using this method, reliability analysis and calculation of planetary roller screw pairs considering multiple failure modes have been performed, verifying the rationality and effectiveness of this method.

Reliability analysis of rolling bearings considering failure mode correlations

AbstractAs an essential mechanical component, a rolling bearing can exhibit multiple failure modes that may occur independently or in correlation with one another. A reliability analysis method that meticulously accounts for the interdependencies among various bearing failure modes is presented in this paper. The examination of wear and fatigue failure mechanisms in rolling bearings is carried out using the Physics of Failure (PoF) approach. By considering the influence of uncertain variables, the limit state functions for individual failure modes are formulated through the application of stress‐strength interference theory. In the context of wear failure, the limit state function is derived using working clearance as the characteristic quantity. On the other hand, the limit state function for fatigue failure is constructed with a focus on fatigue damage accumulation. The Copula function is used to characterize the relationship between wear failure and fatigue failure, and a reliability calculation model for rolling bearings is developed, considering the correlation between these failure modes. Ultimately, the proposed method is utilized to assess the reliability of bearings under two different sets of test conditions. The feasibility of this method is confirmed through test data, demonstrating its effectiveness in predicting bearing reliability. Through the application of this method, engineers can optimize bearing size parameters, select appropriate initial clearances, and enhance the reliability design of bearing.

Reliability analysis of arbitrary systems based on active learning and global sensitivity analysis

System reliability analysis aims at computing the probability of failure of an engineering system given a set of uncertain inputs and limit state functions. Active-learning solution schemes have been shown to be a viable tool but as of yet they are not as efficient as in the context of component reliability analysis. This is due to some peculiarities of system problems, such as the presence of multiple failure modes and their uneven contribution to failure, or the dependence on the system configuration (e.g., series or parallel). In this work, we propose a novel active learning strategy designed for solving general system reliability problems. This algorithm combines subset simulation and Kriging/PC-Kriging, and relies on an enrichment scheme tailored to specifically address the weaknesses of this class of methods. More specifically, it relies on three components: (i) a new learning function that does not require the specification of the system configuration, (ii) a density-based clustering technique that allows one to automatically detect the different failure modes, and (iii) sensitivity analysis to estimate the contribution of each limit state to system failure so as to select only the most relevant ones for enrichment. The proposed method is validated on two analytical examples and compared against results gathered in the literature. Finally, a complex engineering problem related to power transmission is solved, thereby showcasing the efficiency of the proposed method in a real-case scenario.

An efficient analysis method for system reliability of semi-rigid base asphalt pavement structure based on direct probability integral method

The structural design parameters of asphalt pavement inherently possess randomness. Reliability theory aids in considering the stochastic nature of these parameters in asphalt pavement design. However, as the number of considered random factors increases, the complexity of reliability calculation models and the consumption of computational resources also significantly escalate. To analyze the structural reliability of semi-rigid base asphalt pavements more efficiently and accurately, a method combining Weighted Kernel Density Estimation (Weighted-KDE) with Direct Probability Integration Method (DPIM) using sampling strategies was employed. Furthermore, integrating the equivalent extreme value principle, an asphalt pavement structural system reliability analysis method was proposed, considering multiple failure mode correlations. The numerical solution steps of this method are also outlined. Based on the typical semi-rigid asphalt pavement structure in China, this study utilized the elastic layer system method (the program Apbi) and a computational program developed in MATLAB to compare the efficiency and accuracy of calculating reliability between DPIM and the Monte Carlo Simulations (MCS). The computational outcomes indicate that, under the premise of selecting 18 random parameters, DPIM can achieve the required accuracy in assessing the reliability of asphalt pavement structure and system reliability. Analyzing how the number of samples affects the computational results reveals that using weighted-KDE and DPIM only needs a few hundred sample points to limit the relative error within 4 %, compared to the results obtained from 105 samples in MCS. The research findings demonstrate that DPIM incorporating the principle of equivalent extreme value events can serve as an efficient analytical approach for assessing the system reliability of asphalt pavement structures characterized by more complex failure modes.

Multiple-failure mode division and condition-based maintenance decision making for systems with multi-indicator performance degradation

In prognostics and health management research, it is typically assumed that the health status of a system can be reflected by a single performance indicator. However, modern industrial systems are complex and often require multiple indicators to represent different aspects of system health. Furthermore, these indicators may be associated with multiple fault types, leading to multiple failure modes. This study focuses on systems with multi-indicator performance degradation. We construct a division model of multiple failure modes, and investigate the optimal condition-based maintenance decision under multiple failure modes. By constructing division models for systems with two-indicator performance degradation and systems with three-indicator performance degradation, a common multiple-failure mode division model for systems with multi-indicator performance degradation is to distinguish different fault types that may occur in such systems. Taking the systems with two-indicator performance degradation as an example, a condition-based maintenance strategy is developed that considers the differences in maintenance types and effectiveness corresponding to different faults types. A joint probability recursive model under the influence of strategies is derived, and a cost-rate model in a finite time horizon is established to determine the optimal inspection cycle and maintenance threshold for each indicator. Taking the steel rolling system as an application case, the correctness and effectiveness of the proposed strategy and model are verified. The results indicate that the division model can represent the complex relationships among multiple indicators and fault types and that the strategy considering multiple maintenance types and effects can reduce the cost rate of the system.

Joint Learning of Failure Mode Recognition and Prognostics for Degradation Processes.

The paper aims to develop a joint learning method for failure mode recognition and RUL prediction of operating units. Specifically, the developed method addresses a challenging issue in practice, i.e., how to effectively conduct failure mode recognition and RUL prediction as a joint task based on interpretable extracted degradation features from multiple sensor signals. To implement this method in practice, four steps are included as follows: First, collect multiple sensor signals, failure time, and failure modes of historical units. Second, construct the joint learning model based on features extracted from sensor signals by considering the degradation mechanism. Third, estimate model parameters using the data of historical units. Fourth, recognize the failure mode and predict the RUL of an in-service unit. Since the proposed method is a data-driven neural network with flexible model structure that considers complex data relationships, it is expected to be applicable to many practical situations and use cases, especially for manufacturing systems with complex structures and unknown failure thresholds.

Exploiting excavation-induced displacement for slope heterogeneity characterization and stability analysis

Slope displacement data is valuable but underexplored in current slope stability analysis. This study combines a SimSLE-based displacement back analysis and the strength reduction technique to exploit excavation-induced displacement for slope heterogeneity characterization and stability analysis.Numerically synthesized, undrained slopes are used as examples to illustrate the effectiveness of the proposed approach. Results demonstrate that the approach is valid. Displacement data can significantly improve the identification of slip zone and slope stability analysis. Displacement information, affected by the slope stability state, only captures heterogeneities of shear strength within the plastic area. The accuracy of slope stability analysis, including displacement data and the required displacement sampling density, depends on the mean, the variance, and the correlation scale of shear strength. The less development of slip zone or the multiple failure modes associated with the mean of shear strength decreases the accuracy of the slope stability analysis via displacement data. Increasing the displacement sampling density improves little for the former and is necessary for the latter. The large variance and the small correlation scale of shear strength further increase the difficulty of slope stability analysis and its required sampling density. Practically, the proper displacement sampling density for slope stability analysis is suggested.

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.

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 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.

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

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.

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.

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

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.

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.