Reliability Calculation Research Articles

Экспериментальные исследования динамической работы железнодорожных мостов при воздействии высокоскоростного подвижного состава

The issue of considering the dynamic interaction of the railway rolling stock and the structures of bridge structures is especially important when solving the tasks of increasing the speeds of trains on the existing railway lines, as well as when prescribing high-speed mode on designed bridges. Rational accounting of the parameters of dynamic interaction allows you to perform complete and reliable calculations of elements of bridge structures, which in turn has a beneficial effect on predicting the period and nature of the operation of structures and, because of the economic component of projects. The modern solution to the problem of dynamic interaction is reduced to determining the degree of influence of each component of the unified mechanical system of the bridge-travel. Objective: the purpose of this study is to determine the parameters of dynamic interaction through experimental measurements of stresses, accelerations, and displacements of structures of the bridge structure located on the existing railway line with an organized high-speed traffic movement. Methods: experimental measurement using high-frequency voltage sensors, accelerations and movements with subsequent statistical processing and analysis of the results. Results: graphs and tables displaying a change in the measured values of relative deformations, accelerations, and movements over time. Practical importance: The practical significance of this study is to assess the degree of influence of the components of the dynamic interaction of high-speed rolling composition and the bridge structure, which allows you to form a holistic picture of the nature of the process of dynamic interaction.

Moment-independent sensitivity analysis of a fold mechanism based on an active learning kriging method

The present article shows the reliability and sensitivity of a missile folding rudder calculated by MSC.ADAMS and MATLAB. For folding rudder, mass of the counterweight, maximum of the motive force, preloading force, and stiffness coefficients of the spring 1 and spring 2 are considered when calculating the reliability. Two methods, active learning kriging (ALK) and Monte Carlo simulation (MCS), are introduced to calculate the reliability. The results obtained by ALK and MCS are compared to ensure the accuracy of the reliability. Three failure modes are considered respectively in reliability calculation, including the unfolding time exceeding the specified value, the max load of the lock structure 1 or lock structure 2 exceeding the specified value. Moment-independent sensitivity index is introduced to assess the importance rank of each factor. Both cases with and without gap are discussed to explore the influence of gaps between sleeve and shaft. Comparison of two cases show that gaps could reduce reliability of the folding rudder. The influence of gaps cannot be neglected during the reliability calculated, especially for mechanism with several motion joints like the rudder folding. The works aim to provide valuable guidance for design and optimization of unfolding rudder. In addition, it is a beneficial trial for introducing reliability theory in weapon equipment field.

Active learning-based optimization of structures under stochastic excitations with first-passage probability constraints

In various efforts to manage the risk of structural failures caused by stochastic excitations such as wind and earthquake loads, the first-passage probability often needs to be calculated as a reliability constraint. Estimating the first-passage probability with low computational costs is essential, especially in structural optimization requiring iterative reliability calculations for design alternatives. This paper presents a new active learning-based framework for reliability-based design optimization (RBDO) of structures under stochastic excitations. A mixture-distribution-based formulation of the first-passage probability is utilized to handle the high-dimensional sequences of stochastic excitations during the optimization. The design parameter sensitivity of the first-passage probability is introduced to use a gradient-based optimizer in the RBDO iterations. These procedures employ heteroscedastic Gaussian process-based surrogates of the logarithmic responses. An active-learning scheme identifies the best training point to reduce the computational costs in the first-passage probability calculations and optimization. The numerical examples dealing with the optimal design of an eight-story building system and a tower structure subjected to stochastic wind loads demonstrate the accuracy and efficiency of the proposed method.

Modeling Si/SiGe quantum dot variability induced by interface disorder reconstructed from multiperspective microscopy

SiGe heteroepitaxial growth yields pristine host material for quantum dot qubits, but residual interface disorder can lead to qubit-to-qubit variability that might pose an obstacle to reliable SiGe-based quantum computing. By convolving data from scanning tunneling microscopy and high-angle annular dark field scanning transmission electron microscopy, we reconstruct 3D interfacial atomic structure and employ an atomistic multi-valley effective mass theory to quantify qubit spectral variability. The results indicate (1) appreciable valley splitting (VS) variability of ~50% owing to alloy disorder and (2) roughness-induced double-dot detuning bias energy variability of order 1–10 meV depending on well thickness. For measured intermixing, atomic steps have negligible influence on VS, and uncorrelated roughness causes spatially fluctuating energy biases in double-dot detunings potentially incorrectly attributed to charge disorder. Our approach yields atomic structure spanning orders of magnitude larger areas than post-growth microscopy or tomography alone, enabling more holistic predictions of disorder-induced qubit variability.

Correlation of the Molecular Cross-Sectional Area of Organic Monofunctional Compounds with Topological Descriptors.

The molecular cross-sectional area (σ) has proved to be an interesting molecular measure not only in the field of adsorption phenomena on solids but also in biochemistry, physiology, or surfactant chemistry. The existing methods to estimate the cross-sectional areas are either not readily applicable or can only be applied to a limited number of compounds. The aim of this work was to describe a method, as general as possible, quick and easy to perform. To that end, the molecular cross-sectional areas were correlated with topological indices. The Emmett-Brunauer formula was used to calculate the reference cross-sectional areas (σEB) of 431 compounds. The correlations of the Wiener (W), hyper-Wiener (WW), Balaban (J), and Randić (χ) indices with σEB were compared for n-alkanes as well as branched and cyclic alkanes. Only the Wiener and hyper-Wiener indices correlated well with σEB, with the data being best fitted by power law regression curves. The lower degeneracy of the hyper-Wiener index did not translate into any significant gain of performance when correlated with σEB. Following the parsimony principle, the less complex Wiener index was thus selected to correlate with the σEB of compounds representing 31 other monofunctional and structural families. The integration of all the compound families into a single curve allowed a quick rough estimation of the cross-sectional areas. The specific reference equations σEB = qWp were determined for the 34 selected families, allowing the fast and reliable calculation of the cross-sectional area of any monofunctional compound.

Efficacy assessment for multi-vehicle formations based on data augmentation considering reliability

Nowadays, aerial vehicle swarm (AVS) formations have been widely applied to military actions. Meanwhile, assessing their efficacy has also received increasing attention due to the significance for offline tactic planning and online formation switching, which places extra emphasis on the combination of empirical knowledge and experimental data, the transparency of assessment process and the explainability of assessment results. In practical engineering, the efficacy data collected from AVS formations are often incomplete due to limited experiments, and unreliable due to multi-source uncertainties, which poses a great challenge to the development of efficacy assessment system that balances reliability, explainability and accuracy. Oriented to the efficacy assessment of AVS formations under incomplete and unreliable efficacy data, the solution based on belief rule base (BRB) is developed. The reliable heterogeneous data augmentation (RHDA) method is first proposed to improve the availability of efficacy data collected from AVS formation with destination configuration by those collected from AVS formation with source configuration. Then, the feature reliability calculation method based on configuration similarity is designed to avoid the interference of subjective intention. Besides, the configuration importance measurement method based on first-order traceability analysis is put forward to provide decision-makers with references for selecting the most important source configuration when multiple ones exist. The feasibility and effectiveness of developed solution are verified by appropriate simulation experiments.

Towards Robust Visual Understanding: from Recognition to Reasoning

Models that learn from data are widely and rapidly being deployed today for real-world use, but they suffer from unforeseen failures due to distribution shift, adversarial attacks, noise and corruption, and data scarcity. But many failures also occur because many modern AI tasks require reasoning beyond pattern matching -- and such reasoning abilities are difficult to formulate as data-based input-output function fitting. The reliability problem has become increasingly important under the new paradigm of semantic ``multimodal'' learning. My research provides avenues to develop robust and reliable computer vision systems, particularly by leveraging the interactions between vision and language. In this AAAI New Faculty highlights talk, I will cover three thematic areas of my research, ranging from robustness in computer vision, open-domain reliability in visual reasoning, and challenges and opportunities in evaluation of generative models. Readers are encouraged to refer to my website (www.tejasgokhale.com) for more details and updates from my lab's activities towards the goal of robust visual understanding.

Mind your language! Lessons from the application of an English published version of a Japanese horse personality instrument to a French population

Replicability is a fundamental tenet of the scientific method and scientific reporting, but there is a preponderance to publish scientific research in English to increase international recognition, regardless of the country of origin of the research. Questionnaires are widely used to assess personality in animals. These psychometric instruments are mainly published in English but can be used all over the world in other languages. However, without safeguards relating to the translation process, the replicability of the quality of the instrument may change from its originally reported value. This study focuses on the particular issue of cross-cultural reliability of psychometric instruments used for assessing animals that have been translated from their original context. We examined the replicability of the structure of a personality scale originally used in Japanese (but reported in the English literature) on an English population (n=100), and then the reliability of the structure of a French translation of the English version with additional translational safeguards (e.g. back-translation and sense checking) on a French population (n = 159 horses). Horses were rated by 3 evaluators to also allow calculation of inter-rater reliability. We found that there was greater reliability and similarity of structure between the adapted English translation and French version of the Japanese scale, than with the originally published structure of the instrument used in Japan. These results highlight the importance of never assuming the reliability and thus validity of semantic instruments used to assess animal behaviour which have been published in a different language to that in which they were originally developed.

Optimizing the methodology for accurate and accessible slip length measurement with atomic force microscopy

The remarkable performance of cooling devices employing nano- and microscale channels has drawn considerable interest, highlighting the need for surfaces with large slip lengths to improve their efficiency. However, the large errors in slip length associated with existing techniques hinder a clear understanding of slip phenomena. In this paper, we evaluate existing analytical methods for slip length measurement with atomic force microscopy (AFM) and propose a new reliable method. We performed force curve measurements on mica, silica and highly ordered pyrolytic graphite surfaces in water using an AFM equipped with a colloidal probe. The obtained force curves were analyzed through three methods: two commonly utilized procedures, namely the recursive and intercept methods, and a novel one called the two-parameter method which we developed. Our analyses showed that the recursive method yielded slip lengths with relatively large errors, fluctuation of ±5.8 nm, which were due to inaccuracies in the cantilever's spring constant and the fluid viscosity. On the other hand, it was found that the intercept method leads to restrictions on the choice of fitting range because of the simplified formula for viscous drag. As a result, by altering the data range, the calculated slip length shows significant variations within the ranges of ±27.5 nm. The two-parameter method, unlike the standard ones, overcome these drawbacks. This method requires no pre-measured parameters, and the slip length fluctuation is independent of the fitting range and only ±3.6 nm, which is around 2/3 of that observed in the recursive method and 1/8 of that in the intercept method. Our study optimizes existing analytical protocols and offers a new way for accessible and reliable calculations of slip lengths.

A four-stage fast reliability assessment framework for renewables-dominated strong power systems with large-scale energy storage by temporal decoupling and contingencies filtering

Reliability assessment for renewables-dominated strong power systems presents significant challenges, primarily due to the overwhelming computational demands posed by large-scale renewable generation and energy storage. In response to the limitations and lack of scalability in traditional methods, this study introduces a novel four-stage fast reliability assessment framework tailored for renewables-dominated strong power systems. First and foremost, the pre-dispatch and temporal decoupling of energy storage serve as vital foundations for the fast assessment framework. Following the pre-dispatch model, an adaptive scenario clustering technique is proposed to eliminate redundant scenarios while retaining extreme ones. Consequently, a quick verification process for contingencies is established, employing DC optimal power flow in the worst-case scenario to identify potential load shedding. Then, all contingencies are filtered to only effective ones for reliability indices computation. Finally, a modified reliability calculation model with linear AC power flow is built using the filtered contingency set and reduced scenarios. The efficacy of this approach is validated through four numerical experiments, demonstrating impressive efficiency and computation feasibility. For accuracy, the proposed method shows a 3.7% average error in a modified IEEE RTS system, and for efficiency, the proposed method finishes N-2 analysis in 424 s for a 62-bus-212-line case as well as 4924 s for a 200-bus-490-line case.

A novel multi-criteria conflict evidence combination method and its application to pattern recognition

In recent years, the Dempster–Shafer Theory (DST) has been widely applied in areas such as target classification and multi-modal fusion due to its advantages in uncertain reasoning. However, in DST, when there exists highly conflicts between Sources of Evidence (SoEs), it often leads to counterintuitive fusion results, thereby affecting the performance of the final fused decision-making. To eliminate the potential impact of highly conflicts during the fusion process, this paper primarily focuses on modifying the SoEs themselves to achieve the discounting fusion. Currently, existing literature has proposed numerous discounting fusion methods to address highly conflict fusion. However, in these discounting strategies, the determination of discounting factors is typically based on a single criterion. Considering that a single indicator cannot comprehensively and accurately assess the reliability of each evidence source, this paper introduces a novel conflict evidence combination method based on a multi-criteria evaluation strategy. In this method, the Best and Worst Method (BWM) is initially used to prioritize the best SoE, determining the solution points for each criterion and subsequently selecting the ideal solution. Then, combining the selected criteria: distance, relative entropy, and divergence metrics, reliability calculation is performed based on the Stable Preference Order Theory for Ideal Solution (SPOTIS), which achieves a stable preference order strategy towards the ideal solutions. Finally, leveraging the discounting fusion method in DST, modification and fusion of the original highly conflict SoEs are achieved. Through extensive experiments, the effectiveness and practicality of the proposed method in this paper have been validated.

Enhanced FRER Mechanism in Time-Sensitive Networking for Reliable Edge Computing.

Time-Sensitive Networking (TSN) and edge computing are promising networking technologies for the future of the Industrial Internet. TSN provides a reliable and deterministic low-latency communication service for edge computing. The Frame Replication and Elimination for Reliability (FRER) mechanism is important for improving the network reliability of TSN. It achieves high reliability by transmitting identical frames in parallel on two disjoint paths, while eliminating duplicated frames at the destination node. However, there are two problems with the FRER mechanism. One problem is that it does not consider the path reliability, and the other one is that it is difficult to find two completely disjoint path pairs in some cases. To solve the above problems, this paper proposes a method to find edge-disjoint path pairs considering path reliability for FRER in TSN. The method includes two parts: one is building a reliability model for paths, and the other one is computing a working path and a redundant path with the Edge-Disjoint Path Pairs Selection (EDPPS) algorithm. Theoretical and simulation results show that the proposed method effectively improves path reliability while reducing the delay jitter of frames. Compared with the traditional FRER mechanism, the proposed method reduces delay jitter by 15.6% when the network load is 0.9.

Assessment of Organizational and Technological Reliability Monolithic Works

Every year in the construction process new technologies are introduced, the use of new building materials increases, a large number of contractors are involved, with a significant increase in the scale of construction and the need to ensure the reliability of construction. There arises a necessity to forecast and correct activities of construction organizations, to give adequate assessments of the adopted organizational and technological solutions (OTS), which, asa rule, leads to a decrease in the prescriptive term of construction. Monolithic structure construction process design is closely connected with the necessity of clear and correct choice of the necessary organizational-technological solutions. Depending on the made decisions it is possible to estimate the reliability of monolithic works. This in its turn gives the possibility to forecast the terms of production, the cost of monolithic works on the stage of design of dwelling buildings and industrial objects directly. The article offers a methodology for calculating the evaluation of organizational and technical reliability of the process of monolithic structures. For the successful performance of monolithic works it is necessary to ensure the reliable functioning of all participants of the process from designers to construction teams. The article presents the calculation of organizational and technological reliability index (OTR) of monolithic works. To calculate the index the author proposed a model and introduced the concept of reliability of the considered process of monolithic works as a probability of achieving the final goal. At the same time, the process of monolithic works performance both for residential and civil objects is taken as a complex stochastic system, which includes a set of co-dependent relations. The application of the assumed approach for the calculation of organizational and technological reliability of monolithic works with small changes, refinements can be applied to the calculation of other works in the construction of buildings and structures throughout the life cycle of construction. At the same time, this methodology makes it possible to obtain a numerical value of reliability assessment, which is very relevant when choosing organizational and technological solutions.

Reliability Assessment of Power Plant Operations through Boolean Function Expansion and Mean Time to Failure Analysis

This paper presents an investigation into the reliability assessment of power plant operations using Boolean Function expansion and Mean Time to Failure (MTTF) analysis. The study focuses on a complex system comprising two power generators within a power house. The primary objective of this system i. This paper presents an investigation into the reliability assessment of power plant operations using Boolean Function expansion and Mean Time to Failure (MTTF) analysis. The study focuses on a complex system comprising two power generators within a power house. The primary objective of this system is to ensure uninterrupted power supply from the power house to critical consumers via an output main switch. Reliability calculations are conducted considering failure times of various components, including cables, generators, and main switchboards, assuming arbitrary distribution patterns. Additionally, MTTF for the system under an exponential failure time distribution is determined. Graphical representations are employed to illustrate the utility and efficacy of the proposed models to ensure uninterrupted power supply from the power house to critical consumers via an output main switch. Reliability calculations are conducted considering failure times of various components, including cables, generators, and main switchboards, assuming arbitrary distribution patterns. Additionally, MTTF for the system under an exponential failure time distribution is determined. Graphical representations are employed to illustrate the utility and efficacy of the proposed model.

Reliability of permanent downhole systems: Minimum sample and quality index

Permanent downhole monitoring systems are responsible for measuring pressure and temperature time series and enable uninterrupted reservoir characterization during the oil field production period, playing a key role in the oil and gas industry. Located in hostile pressure and temperature environments (i) close to the reservoir, in the case of the PDG (Permanent Downhole Gauge) sensor, and (ii) at the wellhead, in the case of the TPT (Pressure and Temperature Transducer) and PT (Pressure Transducer), its data are transmitted from the subsea environment to the Floating Production Storage and Offloading (FPSO), where the Master Control System (MCS) provides the information in engineering format. This information fulfills its function in the FPSO plant and finally is stored in an onshore data historian. Such complexity, importance, and maintenance difficulty of this system make it necessary to control and manage its reliability. Therefore, the objective of this work is to increase the availability and maximize the useful life of the downhole permanent monitoring system through the reliability calculation, using the Weibull estimate with 2 parameters, and the application of an index quality of statistical inferences. The proposed method for estimating reliability uses a database containing information from permanent downhole monitoring systems of the PDG, TPT, and PT type, from January 1st, 2008 to January 9th, 2014, and considers only the failures that occur until the arrival of the data in the MCS. From the reliability results, it can be observed that stratifications of this database could generate samples with a smaller number of observations, thus inferring reliability even with a small number of samples. The deepening of this method results in the definition of the minimum sample that allows removing reliability inferences without statistical significance and a quality index that allows classifying the reliability estimates of stratified sets of the largest sample of a database. It is worth mentioning here that both methodologies developed in this work are inserted in a well monitoring system that intends to contribute to increasing the availability of pressure and temperature data for the management of well operations.

Design of an acoustic direction finding system based on split array frequency domain beamforming algorithm

According to the technical requirement, the principle of the acoustic direction finding and positioning algorithm is introduced, and based on the engineering implementation requirements of split array frequency domain beam forming algorithm, a hardware implementation method based on FPGA +DSP is introduced, which use FPGA to realize the logic control and two pieces of MS320C6678 multi-core DSP to realize the digital beam forming. The hardware system design scheme can realize the program-controlled amplification, filtering, collection, real-time data storage and beam forming processing of the multi-channel acoustic analog signals for acoustic receiving array. At the same time, it can also communicate with the upper computer to achieve upper computer instructions, data transmission, and parameter settings. It has been verified through engineering practice that this scheme has high data processing efficiency and reliable direction finding calculation effect.

Reliability Computing Methods of Probabilistic Location Set Covering Problem Considering Wireless Network Applications

Reliability Computing Methods of Probabilistic Location Set Covering Problem Considering Wireless Network Applications

Weibull Technique for Evaluation of Swelling: Composite Graphite Resin Electrode for Electrochemical Treatment of Gold Mining Wastewaters

This paper evaluated the swelling of graphite resin electrodes developed for utilization in the electrochemical treatment of gold mining wastewater. Graphite-resin electrodes were developed from used dry cells and resin using non-heat treatment processes (segregation). The Microstructure of the electrode was determined using a scanning electron microscope (Carl Zeiss Smart Evo 10) to ascertain the composition of the electrode. The swelling property of the electrodes was measured using the standard method through a combination of gold mining wastewater and chloride salt solutions. Effects of operational factors (particle size, percentage binder and compressive “compacting” pressure) on the swelling of the electrodes were monitored and evaluated statistically (using analysis of variance). Weibull probability distribution (2 and 3 parameters) was applied to the swelling through Microsoft Excel Solver and Moment Likelihood Method to ascertain the usefulness of the electrode in environmental pollution control through computation of reliability. The study revealed that the swelling was in the range of 1.48 % to 2.24 %, particle size (F5,20 =196.48, p = 2.76 x 10-16), percentage binder (F4,12 =181.58, p = 1.27 x 10-10), and compressive pressure (F3,12 = 106.69, p = 6.43 x 10-9) were significant factors that influence swelling of graphite-resin electrode at 95 % confidence level. the values of α and β for 2-parameters Weibull distribution are 63.162 and 15.098, and 1.265 and 10.089 for MSE and MLM methods, respectively. The Table shows that the values of α, β and θ for 3-parameters Weibull distribution are 3.679, 8.097 and 0.168, and 4.350, 7.165 and 0.198 for MSE and MLM methods, respectively. It was concluded that particle size and compacting pressure are significant factors that had an effect on the swelling of graphite resin electrodes for treatment water and wastewater.

Detection and classification of progressive supranuclear palsy from MRI images using deep learning

A timely and reliable computer aided MR image-based evaluations is required for the detection and classification of Progressive supranuclear palsy (PSP). It is a neurodegenerative ailment that is clinically very difficult to identify due to a high degree of overlap in characterized symptoms. Unlike other ailments, the primary constraint regarding PSP is the limited research work in this area. The main aim of our study is to establish a generalized model by comparing traditional custom CNN and transfer learning models such as DenseNet121, ResNet50, InceptionV3, VGG16, EfficientNetB0, Xception, MobileNet and InceptionResNetV2 which are fine tuned for the detection and classification of PSP with higher accuracy rates. Existing research has primarily focused on reducing the time complexity of neural networks and has only had success with low-level features. Furthermore, obtaining a significant volume of distributed labelled data is difficult. In our research, 125 T1 protocol based high resolution MRI images of 65 PSP and 60 normal control patients were considered. The image dataset is pre-processed, normalized and augmented before deploying them to the respective networks. The results propose that ‘InceptionResNetV2’ model can be considered as a generalised model for the detection and classification of PSP. The network offered a classification accuracy of 95%, Precision of 100%, Sensitivity of 92.8%, F1 score of 96.2% and a Specificity of 100% which is significantly higher compared to other models considered in the study and also the existing conventional ML models, thereby providing a prerequisite for significant diagnostic implementation.

Exploring Uncertainties for Crashed Vehicle Travelling Velocity Tolerance in One-Dimensional Crash Configuration

Evidence suggests that measurement uncertainty is among the most important factors for reliability assessment. Uncertainty plays a vital role in maximising velocity calculation accuracy in crash reconstruction works. It serves as a reliable calculation tolerance, whereby its contribution becomes more significant when the calculated initial velocity is nearly approaching the posted speed limit. The results suggested that damage width and midpoint offset have a low sensitivity of Delta-V from 0.5 km/h to 0.75 km/h for up to 80 cm and 60 cm measurement errors, respectively. For the crush profile variable, a lesser measurement error of 8 cm results in 0.9 km/h deviation in Delta-V. Meanwhile, vehicle mass, drag factor and post-impact displacement have a higher sensitivity of Delta-V, as compared to the two previous variables. The calculation results may deviate from the actual figure for 1 km/h with a missing 75 kg adult occupant. Deviation of almost 2 km/h initial velocity was observed for as low as 0.5 drag factor determination fault. Moreover, with 1 m displacement measurement inaccuracy is also giving rise to the resulting initial speed of 2 km/h. Overall, the principle direction of force recorded the greatest velocity sensitivity among the investigated variables. Within 20o and 45o of inaccurate principle direction of force, the Delta-V deviation increases exponentially, up to 4.25 km/h. The presented findings are beneficial in terms of crash investigator judgment for the thoroughness measurement while conducting the assessment of the crashed vehicle and the crash scene. This investigation will also contribute to enhancing our understanding of tolerance determination for more accurate velocity estimation.