Reliability Evaluation Method Research Articles

Accuracy of ultrasound-guided fine-needle aspiration cytology in evaluation of thyroid nodules using different ultrasonographic and cytological features.

Fine-needle aspiration cytology (FNAC) is a reliable method for preoperative evaluation of thyroid nodules particularly if ultrasound-guided (USG-FNAC). The main purpose of this study is to evaluate the efficacy of USG-FNAC and its accuracy. We retrospectively studied 212 thyroidectomy cases with preoperative ultrasonography and FNAC data during the period 2015-2022 using TI-RADS for final ultrasound diagnosis and Bethesda system for cytological diagnosis. The studied cases were 200 females and 12 males. Thyroid cancer was more prevalent under 20 years old (78.5%). Papillary thyroid carcinoma comprises 84% of all cancer cases. Significant ultrasound features (p-value <0.05) favour malignancy were hypoechogenicity (66%), mixed echogenicity (84%), irregular border (61%), microcalcification (68%) and rim halo (63.6%). Malignancy was found in 21% of TI-RADS-2, 65% of TI-RADS-4 and 100% of TI-RADS-5. There is a significant difference between different categories of Bethesda system. All cases in Cat-VI were malignant (100%). Malignancy was also found in 81% of Cat-V, 20% of Cat-IV, 33% of Cat-III, 16% of Cat-II and 43% of Cat-I. Cytological features consistent with malignancy were as follows: grooving (94%), nuclear irregularities (89%), nuclear pseudoinclusion (89%) and little colloid (82%). In our study, USG-FNAC sensitivity was 83%, specificity 85%, PPV 85%, NPV 83% and accuracy 84%. Ultrasound features in favour of malignancy in thyroid nodules are hypoechoic or complex echogenicity, irregular border, punctuate calcification and presence of rim halo. Cytological features in favour of malignancy are grooving, nuclear irregularities, nuclear pseudoinclusion and little or absent colloid.

Effects of a physical exercise programme on bodyweight, body condition score and chest, abdominal and thigh circumferences in dogs

BackgroundResearch on the effects of physical exercise on canine body composition is limited. The aim of this study was to investigate the effects of a physical exercise programme on bodyweight, body condition score (BCS) and chest, abdominal and thigh circumferences in dogs. Twenty-one healthy dogs of different breeds exercised together with their owners during an eight-week programme consisting of jogging and strength exercises. Standardised measurements were performed in triplicates with a measuring tape on standing dogs. Chest circumference was measured at three anatomical locations, abdomen at two and thigh at one. Data on bodyweight, BCS (9-point scale) and circumferences were analysed with mixed model repeated measures analyses to evaluate changes after the programme and effects of target distance.ResultsSeven dog owners choose a target distance of 2 km and 14 owners choose 5–10 km. Mean BCS decreased (P = 0.007) after the programme (5.1 ± 0.9 vs. 4.7 ± 0.6) but there was no effect of target distance. Almost all chest and abdominal circumference measurements decreased (P ≤ 0.007) with the 2 km group driving the reduction in chest circumference and the 5–10 km group driving the reduction in abdominal circumference. In contrast, thigh circumference (28.8 ± 0.4 vs. 30.2 ± 0.4) increased (P = 0.007) while bodyweight was maintained. There were positive correlations between BCS and abdominal/chest ratios before and after the programme (Pearson correlation; R square ≤ 0.43, P ≤ 0.0012) but the mean ratio remained constant.ConclusionsResults indicated a redistribution between total body fat and muscle mass in body composition of normal weight to slightly overweight dogs after the physical exercise programme. The use of bodyweight alone was not a reliable evaluation method to complement the BCS assessment. However, repeated measurements of chest, abdominal and thigh circumference might aid in the assessment of body composition in dogs performing physical exercise. Further research should include a control group and objective evaluations of total body fat and lean mass, in order to investigate the effectiveness of physical exercise as a freestanding method for decreasing BCS and increasing muscle mass in overweight dogs.

Multi-failure mode reliability of monorail vehicle gear transmission system based on multi-index staged degradation

The operational condition of monorail vehicle is diverse and influenced by numerous factors, leading to multiple failure modes and complex performance degradation mechanisms in gear transmission system. Addressing the problem that current reliability evaluation method based on single-stage single-index degradation models fail to accurately evaluate the multi-failure mode reliability of monorail vehicle gear transmission system, this study comprehensively considers the stage and nonlinear characteristics of degradation processes, along with the interdependence of indexes at different stages, a multi-index staged degradation model is established based on nonlinear Wiener process and Vine-Copula function. By analyzing the positions of multi-index stage change points, a multi-failure mode reliability evaluation method of monorail vehicle gear transmission system considering segmented dependence is proposed. And using the Bayes-Gibbs stepwise parameter estimation method proposed in this study, model parameters for Wiener process and Copula function are estimated separately. Finally, the applicability and effectiveness of the proposed method are verified, and the results shows that, compared to traditional single-stage single-index models, the proposed method more accurately describes the dependence and stage patterns of the degradation process, yielding more rational and effective reliability evaluation results.

A parallel algorithm for reliability assessment of multi-state flow networks based on simultaneous finding of all multi-state minimal paths and performing state space decomposition

This article presents a new efficient method for reliability evaluation of two-terminal Multi-state Flow Networks (MFNs). Indirect State Space Decomposition (SSD) methods are usually more efficient than direct SSD methods. However, indirect SSD methods require searching for all d-MPs/d-MCs. The presented method requires only an MFN, a system demand requirement d, and the probability distributions of states of components as an input. The presented algorithm uses a partition technique to decompose a state space into smaller and distinct subspaces. The algorithm tries to determine a d-flow with a lower bound for each subspace. If a flow exists and it is a d-MP, the algorithm computes the probability that a capacity vector belongs to this subspace and is bounded below by the d-MP and decomposes this subspace into smaller subspaces. For all subspaces without d-MP, the presented algorithm uses parallel computations and the SSD method to compute the probability that a capacity vector belongs to this subspace and is bounded below by a d-MP. The numerical experiments show that the efficiency of the presented method improves in comparison with other indirect SSD methods if the number of available logical processes and the number of d-MPs increase.

An analytical method for reliability evaluation of power distribution system with time-varying failure rates

Reliability evaluation is crucial in assessing the power supply capacity and guiding the planning and operation of the distribution system. Traditional analytical reliability evaluation usually assumes a constant failure rate for components, implying that the life distribution follows an exponential distribution. However, in reality, the failure rate of components varies over time due to external disturbances and aging. As a result, traditional analytical reliability evaluation methods are no longer applicable. To address this issue, this paper presents an analytical method for evaluating the reliability of the distribution system with time-varying failure rates. The method considers short-term and long-term time-varying failure rates using density evolution and non-homogeneous Poisson process techniques, respectively. Both steady-state and instantaneous indices are calculated to reflect the level of system reliability. Finally, the proposed method is applied to the IEEE RBTS Bus 6 test system, demonstrating its correctness and efficiency.

Evaluating the Reliability of Machine-Learning-based Predictions used in Nuclear Power Plant Instrumentation and Control Systems

The field of data-driven, neural-network-based machine learning (ML) has seen significant growth, with applications in various information and control systems. Despite promising real-world uses, the reliability of models remains questionable. Conventionally, reliability is assessed based on predictive fidelity, accuracy, and training effectiveness; however, quality developmental procedures and excellent training performance metrics do not guarantee operational reliability. Instead, an ML model's predictive performance depends on the training set's representativeness to the intended operational space. It is known that ML algorithms excel at interpolation but struggle with extrapolation tasks. Anomalies and feature drift can also reduce operational performance. Determining whether a new sample is an interpolation or extrapolation task involves out-of-distribution (OOD) detection for assessing its proximity to the existing training data. Thus, we present a real-time, model-agnostic individual prediction reliability evaluation method called Data Auditing for Reliability Evaluation (DARE) for applying OOD detection to the training dataset. We demonstrate on a feedforward neural network ML-integrated digital twin for predicting fuel centerline temperatures during loss-of-flow transients. DARE acts as a “data supervisor” in determining the model's applicability under different operating conditions. In this manner, we demonstrate how training data can serve as inductive evidence to support the reliability of ML predictions.

A Model and Data Hybrid-Driven Method for Operational Reliability Evaluation of Power Systems Considering Endogenous Uncertainty

A Model and Data Hybrid-Driven Method for Operational Reliability Evaluation of Power Systems Considering Endogenous Uncertainty

Comprehensive quality evaluation of Lysimachia christinae Hance via fingerprint, spectrum-effect relationship, and quantitative analyses of multiple components by single marker.

Lysimachia christinae Hance (LCH) is a traditional medicine used to treat gallstone disease and cholecystitis. Despite its known anti-inflammatory and choleretic effects, its quality has not been extensively evaluated. In this study, we aimed to establish a reliable quality evaluation method for LCH via fingerprint, spectrum-effect relationship, and quantitative analyses of multicomponents by a single marker (QAMS). First, the fingerprints and anti-inflammatory and choleretic activities of 14 LCH batches were determined. Then, the gray relation analysis method was used to analyze the peak areas of the fingerprint profile and pharmacodynamic data. Subsequently, the characteristic peaks were tentatively identified using high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry. Finally, rutin was selected as the internal reference material, and QAMS was used to analyze the LCH components. Pharmacodynamic experiments confirmed that LCH exerted anti-inflammatory and choleretic effects. Moreover, 15 flavonoids related to the anti-inflammatory and choleretic effects of LCH were identified. Notably, relative error percentage between the QAMS and external standard method was less than 5%. This study successfully established a comprehensive evaluation method for the qualitative and quantitative analyses of LCH.

Nonlinear chatter and reliability analysis of milling Ti-6Al-4V with slender ball-end milling cutter

Slender ball-end milling cutters with high length/diameter ratio can offer increased accessibility in the deep cavity machining of parts in the aviation industry. In this paper, a comprehensive nonlinear machining dynamics model for milling Ti-6Al-4V with the slender ball-end milling cutter is proposed to predict and understand on cutting stability, vibration characteristics and dynamic reliability. The tool tip dynamics model of the slender ball-end milling cutter is established including the structural nonlinearity of bearing joints, the contact flexibility of joint interfaces and the influence of tool geometry. Generated nonlinear dynamic cutting force is calculated considering the multiple regenerative effect, the nonlinearity of tool away from cutting and the run-out of tool. The effect of process damping is included in the nonlinear time-delayed differential equation of the machining dynamics model. Furthermore, the reliability evaluation method for the dynamic accuracy of milling Ti-6Al-4V workpieces is established with the application of Monte Carlo method and active learning Kriging model. Some milling experiments on Ti-6Al-4V workpiece are designed to validate the proposed model by comparing the predicted and measured results. The milling stability, vibration characteristics and dynamic accuracy reliability of milling Ti-6Al-4V with the slender ball-end milling cutter are discussed. The increase in the fractal dimension of joint interfaces can lead to the improvement of milling stability, and increased fractal roughness results in reduced milling stability. The reliability in the dynamic accuracy of milling Ti-6Al-4V workpiece is 99.26 %, and the distribution parameters in the fractal dimension of joint interfaces have the greatest influence on the reliability of dynamic accuracy.

Mission reliability evaluation and selective maintenance approach for manufacturing systems considering epistemic uncertainty

For manufacturing systems, the states of operational performance and functional output are essential indicators of their reliability evaluation, operational health state monitoring, and maintenance decision. However, due to the instability of the 5M1E factors, that is, Man, Machine, Method, Material, Measurement, and Environment, in the manufacturing process, certain components within these systems could introduce epistemic uncertainty factors and thus interfere with the results of evaluation and decision. Consequently, a novel integrated mission reliability evaluation and selective maintenance approach considering epistemic uncertainty is proposed. First, an uncertain–random operational model for manufacturing systems, in which the uncertain operational mechanisms are specified, is established as the foundation. Second, a mission reliability evaluation method for uncertain–random manufacturing systems that fuses the conformity and fitness quality data is proposed to cope with the challenge of epistemic uncertainty. Third, a selective maintenance decision model that maximizes the system’s mission reliability is developed in consideration of the uncertain factors within the operation process. Finally, a case study of a ferrite phase shifting unit manufacturing system is conducted to validate the proposed approach.

An Interwell Connectivity Assessment Model for Polymer Flooding Short-Term Development Data Based on A-LSTM and EFAST Methods

Interwell connectivity assessment in polymer-driven reservoirs is critical for setting appropriate injection rates and improving oil recovery. Traditional deep learning techniques often lack accuracy and reliability when applied to short-term oilfield production data. In response, the A-LSTM algorithm is proposed, which integrates the attention mechanism with a long- and short-term memory network (LSTM). The predictive accuracy of A-LSTM is assessed and juxtaposed with LSTM and support vector regression (SVR) algorithms for short-term single-well daily oil production analysis. The Huber loss function was utilized to quantify the difference between predicted and actual results, resulting in a dynamic production prediction model. An interwell connectivity (IWC) assessment model is then obtained by fusing the dynamic production prediction model with the EFAST method, thus demonstrating the superior prediction accuracy of A-LSTM in oil production prediction and connectivity assessment. Moreover, the credibility of the assessment is further corroborated through numerical simulations and interwell tracer tests. The study results showed that the interwell connectivity evaluation model based on the A-LSTM algorithm and EFAST method is not only capable of accurately predicting the single-well daily oil production using a small sample dataset but also a highly reliable method for interwell connectivity evaluation, and the application of the interwell connectivity assessment model can further guide polymer flooding work in oilfields.

Kinematic accuracy reliability evaluation method of space deployable mechanism based on dynamic model

Space deployable mechanisms are widely used in aerospace missions. The uncertainties in the geometric or physical parameters seriously affect the kinematic accuracy of the mechanism during the deployment process. In this paper, a kinematic accuracy reliability evaluation method of the space deployable mechanism based on dynamic model and adaptive hybrid surrogate models is presented. Firstly, the dynamic model of the space deployable mechanism is established based on the Lagrange method, and the motion error function considering the factors such as the mass, dimension and load is established. In order to improve the efficiency, an adaptive hybrid surrogate model reliability evaluation method based on improved weight measurement method is further proposed. The weight measurement method considers the global errors and local errors of the models. Finally, the Monte Carlo method is used to verify the accuracy of the hybrid surrogate model method. The relative error of the failure probability calculated by the proposed method is within 2.5 %, indicating that the proposed method has high calculation accuracy and efficiency.

Boom-Membrane Space Structure’s Incomplete Deployment After Storage in OrigamiSat-1 Ground Demonstrator

This study investigates the deployment reliability of multifunctional membrane deployable space structures that can be compactly stored and deployed in a two-dimensional direction using carbon composite booms to realize the construction of thin-film solar arrays and membrane antennas. Herein, two aspects are experimentally clarified using the structural models of a nano-satellite OrigamiSat-1, which is a 1 m-by-1 m square multifunctional deployable membrane structure demonstrator. First, a measurement system is developed to quasi-statically quantify the deployment torque of the structural models that failed to complete deployment after long-term storage and fell into an undesirable equilibrium shape. The measurement results confirm that the deployment torque decreases with long-term storage of the structure, and the torque in the deployment direction becomes negative at the deployment angle of the undesirable equilibrium shape. Therefore, as the second contribution, design modifications are made to the boom and membrane, and quasi-static and dynamic deployment tests are employed to evaluate their effects. The deployment reliability evaluation method and two design updates proposed herein will contribute to future space demonstrations and the utilization of lightweight membrane deployment structures with high packaging efficiency.

Electric current stressing enhanced damping properties in Sn5Sb solder

PurposeThe purpose of this paper is to investigate the effects of electric current stressing on damping properties of Sn5Sb solder.Design/methodology/approachUniformly shaped Sn5Sb solders were prepared as samples. The length, width and thickness of the samples were 60.0, 5.0 and 0.5 mm, respectively. The damping properties of the samples were tested by dynamic mechanical analyzer with a cooling system to control the test temperature in the range of −100 to 100°C. Simultaneously, electric current was imposed to the tested samples using a direct current supply. After tests, the samples were characterized using scanning electron microscope, electron backscatter diffraction and transmission electron microscope, which was aimed to figure out the damping mechanism in terms of electric current stressing induced microstructure evolution.FindingsIt is confirmed experimentally that the increase in damping properties is due to Joule heating and athermal effects of current stressing, in which Joule heating should make a higher contribution. G–L theory can be used to explain the damping properties of strain amplitude under current stressing by quantitative description of geometrically necessary dislocation density. While the critical strain amplitude and high temperature activation energy decrease with increasing electric current.Originality/valueThese results provide a new method for vibration reliability evaluation of high-temperature lead-free solders in serving electronics. Notably, this method should be also inspiring for the mechanical performance evaluation and reliability assessment of conductive materials and structures serving under electric current stressing.

Study on reliability of control system in deepwater CBHP MPD system

Deepwater managed pressure drilling technology, with unique advantages in dealing with narrow density window, has been widely used in deepwater drilling operations. As the key equipment to realize managed pressure drilling, the control system in constant bottomhole pressure managed pressure drilling system plays an important role in drilling operations. In this study, a new reliability evaluation method based on fault tree analysis and dynamic Bayesian network model has been proposed to analyze the reliability of the control system. First, the structure of the control system in the deepwater managed pressure drilling system was analyzed to set up a fault tree model of the control system. Second, considering the influence of time on the reliability, a dynamic Bayesian network model of the control system was established. Third, the proposed model was partly verified using the verification method based on three axioms. Fourth, in consideration of the repair factor, the reliability and availability of the control system were calculated by using the exponential distribution model of failure time. Finally, the sensitivity of the control system was analyzed, and the mutual information, variance and entropy were calculated to find out weak links in key components of the control system.

Reliability Evaluation Method for Accelerated Degradation Test based on Nonlinear Gamma Process

Reliability Evaluation Method for Accelerated Degradation Test based on Nonlinear Gamma Process

The Function of Software in Satellite System and Its Reliability Analysis

This paper aims to discuss the function of software in satellite system and the reliability analysis of satellite software. Firstly, the overview of satellite system and the importance of software in it are introduced. Then, the concept of reliability, reliability evaluation method and factors affecting reliability of satellite software are expounded in detail. Then, the strategy to improve the reliability of satellite software is discussed. Finally, the article looks forward to the future development trend of satellite software reliability, including the application of artificial intelligence, machine learning, and distributed ledger technology. These new technologies are expected to further improve the reliability of satellite pieces and provide a solid guarantee for the safety of satellite communication.

Automatic ROI Selection with a Reliability Evaluation Method for Cirrhosis Detection Using Ultrasound Images

Cirrhosis is a liver disease resulting from abnormal continuation of fibrosis, and ultrasound imaging is widely used for cirrhosis diagnosis because of its non‐invasiveness. However, due to unclear appearances of cirrhosis on ultrasound images, diagnoses are difficult and individual results possibly differ depending on the physician's experience. Recently, computer‐aided diagnostic systems using image processing and machine learning have been developed to help physicians detect cirrhosis as a ‘Second opinion’. Some related studies have focused on a scenario where physicians set ROIs (Region of Interests) manually because selecting reliable ROIs for training a classifier and classification of patients is indispensable. But, the accuracy of such systems depends inherently on the quality of ROIs, and thus the workloads of physicians increase. In this paper, we propose a reliability evaluation method (REM) for each ROI based on its posterior probability and relationship to peripheral ROIs. The assumption of our proposal is that reliable regions of cirrhosis and normal can be observed in certain regions predominantly. We evaluated the effectiveness of the REM and its optimization for practical use. Experimental results showed that our proposed method curated reliable ROIs and improved classification performance in terms of AUC (Area Under the Curve). © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Reliability evaluation method for PID feedback control system considering performance degradation

The reliability of proportional-integration-derivative (PID) feedback control systems is crucial for products due to their critical functions. Traditional reliability evaluation methods of PID feedback control systems usually pose difficulties in comprehensively considering the performance compensation property of feedback mechanisms and the degradation phenomenon in operating processes. This paper proposes a new reliability evaluation method for the PID control system under the framework of belief reliability theory to consider both deterministic and uncertain laws regarding functional logics and degradation effects. For deterministic laws, the interdisciplinary, degradation, and margin equations are established with respect to the steady state error. Various internal and external influencing factors are integrated into these equations by modeling state space and characterizing degradation mechanisms. For uncertain laws, the metric equation is constructed accordingly considering diverse uncertain factors. A Monte Carlo-based algorithm and an analysis framework are further developed to calculate the belief reliability. Finally, the proposed method is implemented to an aero-power hydraulic servo control system as a specific illustration, demonstrating its effectiveness and rationality.

Symmetry recovery in zygomaticomaxillary complex fractures compared to normal unfractured population: A new reliable 3D evaluation

ObjectiveThis study aims to quantify the facial symmetry of surgically treated zygomaticomaxillary complex (ZMC) fractures through a new reliable three-dimensional evaluation method, which is crucial for improving post-operative aesthetic and functional outcomes. Material and methodsHealthy patients and patients with surgically treated ZMC fractures were retrospectively reviewed. Using Brainlab Elements® the zygomatic bone and the orbit of each patient was segmented and mirrored. Subsequently, the mirrored side was matched with the other side via volume-based registration, using the segmented orbit as reference. Volumetric asymmetry was measured using 3-matic software, and a surface-based matching technique was used to calculate the mean absolute differences (MAD) between the surfaces of the two sides of the ZMC. The reliability of this novel method using volume-based registration was tested, and the intra-class correlation coefficient was assessed. ResultsThe MAD between the surfaces of the left and right sides in the control group was 0.51 mm (±0.09). As for the ZMC fracture group, MAD was 0.78 mm (±0.20) and 0.72 mm (±0.15) pre- and post-operatively, respectively. The MAD showed statistically significant differences between pre- and post-operative groups (p = 0.005) and between control and post-operative groups (p < 0.001). The intra-class correlation coefficient was high (≥0.99). ConclusionsThis evaluation method using mirroring and volume-based registration to determine the symmetrical position of the ZMC is reliable. The surface-based measurements revealed an improved symmetry after surgery. However, the symmetry of the treated patients remained lower than the control group.