The Research of Reliability Evaluation Method Based on PoF

The reliability of circular electrical connectors was usually assessed according to standards like MIL-HDBK-217 (Reliability Prediction of Electronic Equipment). Given to their limitations and mislead results, a new assessment method needs to be presented. Some concerns on the physics of failure (PoF) modeling and reliability assessment of circular electrical connectors are addressed in this paper to overcome this defect. An original approach combining laboratory accelerated degradation testing (ADT) and PoF modeling is proposed for effectively assessing connector reliability. Random vibration and current stress are selected as acceleration factors that affect the contact life of electrical connectors. Then, an appropriate multiple-stresses ADT scheme is derived. The PoF model for electrical connectors under multiple stresses yielded the generalized Eyring relationship. According to the system reliability model for multiposition connectors, connectors' life follows Weibull distribution, which is derived from extreme-value distribution theory. Through ADT and data gained from tests, statistical analysis result yielded the estimated value of reliability character of MIL-C-38999 I series electrical connectors under the action of vibration and current stresses.

Review of shelf life evaluation methods and a physics of failure approach for shelf life estimation for electronic components

In this paper, the vibration durability of SAC305 solder interconnects of Heat-sink Very-thin Quad Flat-pack No-leads (HVQFN) assemblies is investigated by accelerated stress testing and Physics-of-Failure (PoF) simulations, under various types of vibration excitation. In future work, these results will be used to develop PoF models for vibration fatigue durability of Pb-free SAC305 solder interconnects and to extract model constants from test data.

This paper discusses educational tools, resources and experience in teaching physics-of-failure (PoF) modeling and data analysis methods used in accelerated life testing (ALT) and accelerated degradation testing (ADT). The concepts introduced in this paper include several probabilistic and highly technical approaches such as the PoF and mechanistic approaches to reliability prediction and assessments as well as advanced Bayesian analysis methods used in ALT and ADT to analyze life data obtained through various methods of constant and variable stress testing efforts. Other resources include codes and algorithms from the readily available computational tools R, MATLAB, and WinBUGS for solving advanced model and life estimation methods related to PoF, ALT and ADT analyses. This paper provides an overview of these resources and several example problems.

Several research studies have investigated the degradation of BaTiO3-based dielectric capacitor materials, focusing on the impact of composition, defect chemistry, and microstructural design to limit the electromigration of oxygen vacancies under electric fields at finite temperatures. Electromigration can be a dominant mechanism that controls failure rates in the individual multilayer ceramic capacitor (MLCC) components in testing the reliability of failures with highly accelerated lifetime testing (HALT) to determine the mean time to failure of MLCCs surface mounted onto printed circuit boards (PCBs). Conventional assumptions often consider these failures as independent, with no interaction between components on the PCB. However, this study employs a Physics of Failure (PoF) approach to closely examine transient degradation and its impact on MLCC reliability, emphasizing thermal crosstalk and its influence on dependent and independent failure rates. Finite element analysis thermal modeling and infrared thermography were used to assess the impact of circuit layout and component spacing on heat dissipation and thermal crosstalk under various electrical stress conditions. The study distinguishes between dependent and independent failures under a HALT, quantified through a β′ factor reflecting common cause failures due to thermal crosstalk. Through a series of experimental and statistical analyses, the β′ factor is evaluated with respect to temperature, voltage, and component spacing. These insights highlight the importance of understanding the nature of the data in reliability testing of MLCCs and optimizing the layout design of high-density circuits to mitigate dependent failures, improving overall reliability and informing better design and packaging strategies.

Power cables are critical assets for the reliable operation of the grid. The cable lifetime is generally estimated from the conductor temperature, and associated lifetime reduction. However, these tasks are intricate due to the complex physics-of-failure (PoF) degradation mechanism of the cable. This is further complicated with the different sources of uncertainty that affect the cable lifetime estimation. Generally, simplified or deterministic PoF models are adopted resulting in non-accurate decision-making under uncertainty. In contrast, the integration of uncertainties leads to a probabilistic decision-making process impacting directly on the flexibility to adopt decisions. Accordingly, this paper presents a novel cable lifetime estimation framework that connects data-driven probabilistic uncertainty models with PoF-based operation, and degradation models through Bayesian state-estimation techniques. The framework estimates the cable health state, and infers confidence intervals to aid decision-making under uncertainty. The proposed approach is validated with a case study with different configuration parameters, and the effect of measurement errors on cable lifetime are evaluated with a sensitivity analysis. Results demonstrate that ambient temperature measurement errors influence more than load measurement errors, and the greater the cable conductor temperature the greater the influence of uncertainties on the lifetime estimate.

High current density and high temperature gradient are major reliability concern for next generation high density power electronics. High current density experiments on lead free solder joints coated with NiAu and non-coated Cu pads were conducted at -20°C, -30°C, -40°C and -50°C ambient temperatures. The time to failure (TTF) shows that solder joints with NiAu coated Cu pads last longer. Results also indicates TTF plot shows that TTF rate increases exponentially when the solder joint temperature is higher than 64% of its melting temperature, and decreases exponentially reaching the maximum lifetime when the temperature is below this threshold temperature. The mass transport activation energy, Ea was determined using the test data and it was found to be 2.67 ± 0.05 and 3.65 ± 0.13 eV for coated and non-coated solder joints, respectively. These values are indicative of the dominant diffusion mechanism during the experiment. It was discovered that the thermomigration driving force was as high as electromigration driving force.

Uncertain right censoring (URC) which describes the situation that the censoring settings of test units are unrelated to their failure time often occurs in practical life testing. Besides, in practical life testing, the small sample situation is also common since the test resources are usually limited, which brings epistemic uncertainties to reliability evaluations due to the lack of information. Under such situation, the large sample‐based probability theory is not appropriate anymore to conduct reliability evaluations. In this paper, the belief reliability evaluation is conducted with uncertain right censored time‐to‐failure (TTF) data under the small sample situation based on the belief reliability theory. Firstly, to deal with epistemic uncertainties, a truncated normal uncertainty distribution of lifetime is given, and the belief reliability evaluation is presented using the uncertain measure. Then, the corresponding uncertain statistics method for unknown parameter estimation is provided with objective measures. Finally, a simulation study and a practical case are used to illustrate the proposed method. The results show that the proposed method is suitable to deal with epistemic uncertainties and can achieve more accurate and stable mean time‐to‐failure (MTTF) results with uncertain right censoredTTFdata under the small sample situation compared to classical probability and Bayesian reliability methods.

The small-creep disc test is seen as a promising solution to the problem of sampling from in-service components for remanent life estimation. However, experimental studies have revealed substantial scatter in failure times resulting from variations in test and apparatus geometries. These studies therefore suggest that there exists a set of conditions that both minimizes the scatter and also maximizes the sensitivity of the disc test to the determination of remanent life, so enabling reliable estimates of the remaining life to be made. The objective of this paper is to identify such an optimum. The large scatter present in small-disc test data would make the identification of this optimum problematic and inconclusive using an experimental approach. Instead this paper uses a numerical model of the disc test to predict failure times over a wide range of test conditions. The resulting response surface is then approximated using a polynomial and from this model the optimum set of test conditions is identified. Full verification of the model (and the optimum) can then be more successfully accomplished by carrying out experiments close to the optimum conditions where scatter is the smallest. The model was shown to be capable of predicting actual experimental creep test results. Using this approach, failure times were found to be most sensitive to disc thickness and hole diameter but least sensitive to disc diameter. Under clamping, the optimum test geometry was insensitive to the applied load but sensitive to the level of damage. Without clamping, the optimum geometry was insensitive to both of these. The paper gives optimum conditions for various levels of damage under both conditions.

A copula-based sampling method for data-driven prognostics

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.

We conduct an exploratory data analysis of a specific system test data set that includes concomitant variables measuring aspects of test effort as well as the failure count and time variables considered in standard software reliability analyses. We consider a family of Poisson process models for which failure rates may depend on time as well as on the test effort variables. We analyze failure rates computed from this test data using least squares parameter estimation and model selection via the predicted residual sum of squares (PRESS). >

Acid stimulation treatments are typically designed using a combination of experience, available field/lab testing data and a stimulation design software that ties it all together. The final design, given the large degrees of uncertainty, requires multiple iterations to arrive at the (near) optimum stimulation design. To account for the large number of variables and their uncertainty, a framework and associated tools have been developed for automating and optimizing the design process that allow to significantly reduce the optimization time. The adopted approach leverages modern web applications for overcoming the limitations associated with the legacy stimulation design tools. Python Dash is used for creating an interactive web application, allowing users to load a file containing all necessary inputs for the calculation and specify the ranges for different design parameters. The web app then generates a parameter space and spawns multiple parallel runs of the calculation engine. An optimization module utilizing existing python optimization libraries is developed within the web app framework, which guides the selection of the next sample point in the parameter space after each iteration until the optimum point is reached. After implementing this framework across multiple workflows, considerable improvements have been realized with time savings ranging from hours to days. A detailed case study is presented to demonstrate the application of the framework for designing a Limited Entry Liner (LELs) completion, where the nozzle distribution in different reservoir sections (compartments) is varied to achieve a desired acid placement into the formation and improve stimulation efficiency.

Building Information Modeling (BIM) and 4.0 represent the hottest topics in construction and Industry. BIM is a process to create and manage all of the information on a project across the infrastructure lifecycle. The output of this process is a digital model that contains all the information of the infrastructure. 4.0 is a term used to refer to the developmental project in the management of manufacturing and chain production. It includes cyber-physical systems, the Internet of Things, cloud computing and cognitive computing. But how can the explosive mixture of these two approaches generate a revolution and a successful result in the construction field? Our paper wants to focus on how the 4.0 interoperability and information transparency can support and provide information to the BIM process during construction and moreover during maintenance phase of the infrastructures. Furthermore, we illustrate some of the best practises on machines carrying out aspects of production on digitalized model where BIM represents a repository for machine output information. We show the related benefit for an intelligent supply chain in real time. The result will demonstrate how the fourth industrial revolution affects the construction world and why the BIM-4.0 strategy represents a time and cost efficiency, a unique opportunity.

PurposeMechanical products usually require deterministic finite element analysis in the design phase to determine whether their structures meet the requirements. However, deterministic design ignores the influence of uncertainties in the design and manufacturing process of mechanical products, leading to the problem of a lack of design safety or excessive redundancy in the design. In order to improve the accuracy and rationality of the design results, a robust design method for structural reliability based on an active-learning marine predator algorithm (MPA)–backpropagation (BP) neural network is proposed.Design/methodology/approachThe MPA was used to obtain the optimal weights and thresholds of a BP neural network, and an active-learning function applicable to neural networks was proposed to efficiently improve the prediction performance of the BP neural network. On this basis, a robust optimization design method for mechanical product reliability based on the active-learning MPA-BP model was proposed. Random moving quadrilateral sampling was used to obtain the sample points required for training and testing of the neural network, and the reliability sensitivity corresponding to each sample point was calculated by subset simulated significant sampling (SSIS). The total mass of the mechanical product and the structural reliability sensitivity of the trained active-learning MPA-BP model output were taken as the optimization objectives, and a multi-objective reliability-robust optimization design model was constructed, which was solved by the second-generation non-dominated ranking genetic algorithm (NSGA-II). Then, the dominance function was used in the obtained Pareto solution set to make a dominance-seeking decision to obtain the final reliability-robust optimization design solution. The feasibility of the proposed method was verified by a reliability-robust optimization design example of the bogie frame.FindingsThe prediction error of the active-learning MPA-BP neural network was smaller than those of the particle swarm optimization (PSO)-BP, marine predator algorithm (MPA)-BP and genetic algorithm (GA)-BP neural networks under the same basic parameter settings of the algorithm, which indicated that the improvement strategy proposed in this paper improved the prediction accuracy of the BP neural network. To ensure the reliability of the bogie frame, the reliability sensitivity and total mass of the bogie frame were reduced, which not only realized the lightweight design of the bogie frame, but also improved the reliability and robustness of the bogie.Originality/valueThe MPA algorithm with a higher optimization efficiency was introduced to find the weights and thresholds of the BP neural network. A new active-learning function was proposed to improve the prediction accuracy of the MPA-BP neural network.