Reliability Analysis Of Components Research Articles

RETRACTED: Reliability analysis of mechanical components based on a nonlinear fatigue damage accumulation model

The dynamic reliability models of mechanical components with the failure mode of fatigue are mainly based on the linear damage accumulation theory, which is also called the Miner’s rule. But the Mi...

Advances in structural integrity and reliability analysis for critical components

Advances in structural integrity and reliability analysis for critical components

Component Analysis of Purification System of RSG-GAS

COMPONENT ANALYSIS OF PURIFICATION SYSTEM OF RSG-GAS. Component reliability analysis is required in the aging management of RSG-GAS that has reached an age of 30 years. One of the required analyses is the assessment of the distribution of repair data and the estimation of related parameters. The Primary Purification System (KBE01) and the Purification and Warm Water Layer System (KBE02) are important components of RSG-GAS. By knowing the repair data distribution, the parameters of the most frequently occurring component repair and the average of the repair period can be estimated, so that the required provision of spare parts for the smooth operation of the reactor can be predicted. The purpose of this study is to analyze the components of the KBE01 and KBE02 systems through the data distribution approach using the matching test method. With the matching test, the form of data distribution can be determined, so the parameter of the average component repair period that can be used as a comparison of the maintenance period of the components can be estimated. The repair times of KBE01 and KBE02 in RSG-GAS on Core 52 through Core 88 (2006-2015) were analyzed using goodness-of-fit test. The repair times of AA068 and AP001 KBE01 follow the exponential distribution with average repair times of 631.6 and 451.2 days, respectively. The repair times of WWL and AA002 KBE02 followed an exponential distribution with average repair times of 239.5 days and 888.0 days.

Fatigue reliability analysis of mechanical components for airflow control in pneumatic solenoid valve

Pneumatic solenoid valves have been broadly used in vehicles to perform automatic control task. For safety reason, they have to be highly reliable and must have long fatigue lives. The failure of the mechanical components including a conical spring and rubber seal can induce airflow disorder, which renders the unreliability of the pneumatic solenoid valve. A method is presented to study the fatigue reliability of the mechanical components used for airflow control in a pneumatic solenoid valve. The statistics of the parameters used to derive the probability distributions of the stresses/strains in the mechanical components are obtained from the measurements of the parameter properties and static load testing of the components. The existing material S-N curves and stress/strain probability distributions are used to determine the reliabilities of the mechanical components. Based on the series system criterion, the fatigue reliability of the pneumatic solenoid valve with the consideration of the airflow disorder failure mode is computed for the life of 1 million cycles.

Reliability analysis of component of affination centrifugal 1 machine by using reliability engineering

Problems that appear in a company that produces refined sugar, the production floor has not reached the level of critical machine availability because it often suffered damage (breakdown). This results in a sudden loss of production time and production opportunities. This problem can be solved by Reliability Engineering method where the statistical approach to historical damage data is performed to see the pattern of the distribution. The method can provide a value of reliability, rate of damage, and availability level, of an machine during the maintenance time interval schedule. The result of distribution test to time inter-damage data (MTTF) flexible hose component is lognormal distribution while component of teflon cone lifthing is weibull distribution. While from distribution test to mean time of improvement (MTTR) flexible hose component is exponential distribution while component of teflon cone lifthing is weibull distribution. The actual results of the flexible hose component on the replacement schedule per 720 hours obtained reliability of 0.2451 and availability 0.9960. While on the critical components of teflon cone lifthing actual on the replacement schedule per 1944 hours obtained reliability of 0.4083 and availability 0.9927.

Reliability modeling and analysis of heavy-duty CNC machine tool spindle under hybrid uncertainty

Heavy-duty CNC machine tools have been used to process and manufacture the products and the key components with concerns the national economy and defense security, and the quantity of heavy-duty CNC machine tools measures the industrialization level and the comprehensive strength of a country. Compared with the general machine tools, heavy-duty CNC machine tools have a more complex system structure, smaller size sample, less experimental data, incomplete information, more complex failure mechanism and other characteristics. The heavy-duty CNC machine tools as well as its sub-systems are often influenced by hybrid uncertainty and the correlated failures of different components. Therefore, the existing reliability technologies cannot be applied directly, thus new reliability technologies for specific to heavy-duty CNC machine tools are needed. Aiming at the problems analyzed above, this paper focus on the reliability modeling and analysis of heavy-duty CNC machine tool spindle under hybrid uncertainty. Firstly, this paper analyzed various uncertainty factors influencing the reliability of the heavy-duty CNC machine tool spindle and the limitations of the existing uncertainty quantification methods, constructed a unified framework for hybrid uncertainty quantification based on the imprecise probability theory. Secondly, based on the uncertainty quantification framework and stress-strength interference theory, this paper proposed an imprecise structural reliability analysis and modeling method for mechanical components. Finally, the models proposed in this paper were verified through the imprecise structural reliability analysis of a certain type of milling spindle, the imprecise structural reliability models under different failure modes are proposed. And the results are compared with the results calculated by Monte Carlo method. It has shown that the proposed method has a higher computational efficiency.

Reliability analysis of the PZL-130 Orlik TC-II aircraft structural component under real operating conditions

Reliability analysis of the PZL-130 Orlik TC-II aircraft structural component under real operating conditions

Design Optimization Method for Composite Components Based on Moment Reliability-Sensitivity Criteria

AbstractIn this paper, a Reliability-Sensitivity Based Design Optimization (RSBDO) methodology for the design of the ceramic matrix composites (CMCs) components has been proposed. A practical and efficient method for reliability analysis and sensitivity analysis of complex components with arbitrary distribution parameters are investigated by using the perturbation method, the respond surface method, the Edgeworth series and the sensitivity analysis approach. The RSBDO methodology is then established by incorporating sensitivity calculation model into RBDO methodology. Finally, the proposed RSBDO methodology is applied to the design of the CMCs components. By comparing with Monte Carlo simulation, the numerical results demonstrate that the proposed methodology provides an accurate, convergent and computationally efficient method for reliability-analysis based finite element modeling engineering practice.

Fatigue reliability analysis of a turbine disc under multi-source uncertainties

Life and reliability analysis of hot section components like high pressure turbine (HPT) discs plays an important role for ensuring the engine structural integrity. HPT disc operates under high temperatures to withstand complex loadings, its basic parameters, including the applied loads, material properties and working environments, have shown multi-source uncertainties. The influence of these uncertainties on the structural response of the turbine disc cannot be ignored. According to this, the variations of applied loads and material properties are quantified for fatigue reliability analysis of turbine disc. In particular, material response variability is modeled by using the Chaboche model and Fatemi-Socie damage criterion. Moreover, the inhomogeneity of its constituent material is also considered through combining FE simulation with Latin hypercube sampling. Finally, fatigue reliability analysis of a HPT disc under multi-source uncertainties is conducted for different flight missions.

The relationship between the monitored performance of tutors and students at PBL tutorials and the marked hypotheses generated by students in a hybrid curriculum

ABSTRACTIntroduction: There have been a number of published studies examining the link between the effectiveness of the problem-based learning (PBL) process and students’ performance in examinations. In a hybrid PBL/lectures curriculum, the results of such studies are of limited use because of the difficulty in dissociating the knowledge gained at lectures from that gained through PBL-related activities. Hence, the objectives of this study were: (1) to develop an instrument to measure the performance of tutors and students at PBL tutorials, and (2) to explore the contribution of such performances to the marks attained by students from the hypotheses generated at PBL tutorials.Methods: A monitoring instrument for assessing the performances of non-expert tutors and students at tutorials was developed and validated using principal component analysis and reliability analysis. Also, a rubric was formulated to enable a content expert to assign marks to the quality of hypotheses generated.Results: The monitoring instrument was found to be valid and reliable. There was a significant correlation between the performance of tutors at tutorials and hypotheses marks. In contrast, there was no significant correlation between the performance of students and hypotheses marks.Discussion: The monitoring instrument is a useful tool for improving the PBL process, especially where the medical programme depends on non-expert PBL tutors. In addition to ensuring good PBL processes, it is important that students achieve the desired output at PBL tutorials by producing hypotheses that help them understand the basic sciences underlying the clinical cases. The latter is achieved by the use of an open-ended rubric by a subject expert to assign marks to the hypotheses, a method that also provides additional motivation to students to develop relevant and detailed hypotheses.

Dignity and Psychosocial-Related Variables in Advanced and Nonadvanced Cancer Patients by Using the Patient Dignity Inventory-Italian Version

ContextThe Patient Dignity Inventory (PDI) is a valid and reliable instrument to measure dignity, as a state of physical, mental, social, and spiritual well-being in palliative care patients and an essential dimension for a comprehensive patient-centered approach. ObjectivesWe examined the factor structure and correlation of the Italian version of the PDI (PDI-IT) with psychosocial variables among advanced and nonadvanced cancer outpatients in two Italian centers. MethodsIn a sample of 194 patients, principal component analysis, reliability analysis (Cronbach's coefficient alpha), and correlation analysis of the PDI-IT were performed. Concurrent validity was evaluated with respect to the Italian versions of Patient Health Questionnaire-9 (PHQ-9), as a measure of depression, the Mini-Mental Adjustment to Cancer-Hopelessness Scale, as a measure of dysfunctional coping, and the Demoralization Scale (DS-IT), as a measure of demoralization. ResultsThree factors were extracted by exploratory factor analysis, which accounted for 64.38% of the variance, namely existential distress (Cronbach's α = 0.95), psychological distress (Cronbach's α = 0.88), and physical distress (Cronbach's α = 0.81), with a Cronbach's α coefficient for the PDI-IT total score of 0.96. PDI-IT factors were significantly intercorrelated and shared between 42% and 53% of the variance. Higher scores on all the PDI-IT factors and PDI-IT total were found among patients who were clinically depressed (PHQ-9) and among those who were demoralized on the DS-IT. Significant correlations were also found between all PDI-IT and the DS-IT, PHQ-9, and the Mini-Mental Adjustment to Cancer-Hopelessness Scale. ConclusionsThe study confirmed that the PDI-IT is a valid instrument to be applied in oncology and measuring three factors, namely existential, psychological, and physical distress, as core dimensions of dignity, to be monitored and treated in clinical settings.

A fast uncertainty analysis for material nonlinear problems based on reanalysis and semi-analytic strategy

A novel semi-analytic method for interval-based uncertainty analysis is suggested to calculate the uncertainty measure index in this study. As nonlinear problems commonly cannot be solved analytically, an analytic interval-based uncertainty analysis method for linear problems is deduced, and an explicit expression of the measure index of uncertainty is given. To extend this form to nonlinear analysis, a semi-analytic uncertainty analysis (SAUA) method is suggested by integrating gradient-based optimization methods and Taylor expansion. Initially, a pseudo optimal solution is obtained. Sequentially, a Taylor expansion is used to calculate the performance function based on the pseudo optimal solution. In this way, the uncertainty measure index can be obtained analytically. Considering the bottleneck of surrogate model, an efficient reanalysis assisted material nonlinear analysis (RAMNA) method is integrated. Thus, the efficiency and accuracy of the suggested method are both enhanced. The SAUA method has been applied to large-scale reliability analysis of vehicle components. The results show that the SAUA is effective and efficient, especially for large-scale problems. Furthermore, the SAUA method can also be extended for other mechanical design, and contribute to shortening the design period.

Big data-enabled multiscale serviceability analysis for aging bridges☆

This work is dedicated to constructing a multi-scale structural health monitoring system to monitor and evaluate the serviceability of bridges based on the Hadoop Ecosystem (MS-SHM-Hadoop). By taking the advantages of the fault-tolerant distributed file system called the Hadoop Distributed File System (HDFS) and high-performance parallel data processing engine called MapReduce programming paradigm, MS-SHM-Hadoop features include high scalability and robustness in data ingestion, fusion, processing, retrieval, and analytics. MS-SHM-Hadoop is a multi-scale reliability analysis framework, which ranges from nationwide bridge-surveys, global structural integrity analysis, and structural component reliability analysis. This Nationwide bridge survey uses deep-learning techniques to evaluate the bridge serviceability according to real-time sensory data or archived bridge-related data such as traffic status, weather conditions and bridge structural configuration. The global structural integrity analysis of a targeted bridge is made by processing and analyzing the measured vibration signals incurred by external loads such as wind and traffic flow. Component-wise reliability analysis is also enabled by the deep learning technique, where the input data is derived from the measured structural load effects, hyper-spectral images, and moisture measurement of the structural components. As one of its major contributions, this work employs a Bayesian network to formulate the integral serviceability of a bridge according to its components serviceability and inter-component correlations. Here the inter-component correlations are jointly specified using a statistics-oriented machine learning method (e.g., association rule learning) or structural mechanics modeling and simulation.

Markov chain splitting methods in structural reliability integral estimation

Monte Carlo simulation methods involving splitting of Markov chains have been used in evaluation of multi-fold integrals in different application areas. We examine in this paper the performance of these methods in the context of evaluation of reliability integrals from the point of view of characterizing the sampling fluctuations. The methods discussed include the Au–Beck subset simulation, Holmes–Diaconis–Ross method, and generalized splitting algorithm. A few improvisations based on first order reliability method are suggested to select algorithmic parameters of the latter two methods. The bias and sampling variance of the alternative estimators are discussed. Also, an approximation to the sampling distribution of some of these estimators is obtained. Illustrative examples involving component and series system reliability analyses are presented with a view to bring out the relative merits of alternative methods.

Determination of environmental load factors for ISO 19902 code in offshore Malaysia using FORM structural reliability method

Environmental load factor, used for the design of Jacket platform design in Malaysia, are based on calibration of platforms located in Gulf of Mexico and North Sea. This load factor is assumed to be high for locations where moderate climate governs i.e. regions lying near equator like Malaysia which is at 7° north of the equator. For Jacket platforms, safety can be achieved by taking into consideration of hazards produced by rare events of wave, wind and current. Environmental loads are not like live loads acting on land-based structure but are more severe due to unpredictable weather conditions. These environmental loads can act with unexpected severity on offshore structures. Material strength of tubular components and joints plays significant role against risk. The resistance can also be reduced because of damage to Jacket, continuous action of wave forces causing fatigue and corrosion. Four platforms from three different regions of Malaysia were selected for the reliability analysis. Probabilistic techniques were used to evaluate and estimate the load and resistance uncertainty. FORM method of reliability was used for structural reliability analysis of Jackets. Modified environmental load factor is proposed for offshore Malaysia considering component, joint and system reliability analysis based on calibration.

New Methods for Automated Fatigue Crack Growth and Reliability Analysis

A new methodology has been developed for automated fatigue crack growth (FCG) life and reliability analysis of components based on finite element (FE) stress and temperature models, weight function stress intensity factor (SIF) solutions, and algorithms to define idealized fracture geometry models. The idealized fracture geometry models are rectangular cross sections with dimensions and orientation that satisfactorily approximate an irregularly-shaped component cross section. The fracture model geometry algorithms are robust enough to accommodate crack origins on the surface or in the interior of the component, along with finite component dimensions, curved surfaces, arbitrary stress gradients, and crack geometry transitions as the crack grows. Stress gradients are automatically extracted from multiple load steps in the FE models for input to the fracture models. The SIF solutions accept univariant stress gradients and have been optimized for both computational efficiency and accuracy. The resulting calculations are used to automatically construct FCG life contours for the component and to identify hot spots. Finally, the new algorithms are used to support automated probabilistic assessments that calculate component reliability considering the variability in the size, location, and occurrence rate of the initial anomaly; the applied stress magnitudes; material properties; probability of detection; and inspection time. The methods are particularly useful for determining the probability of component fracture due to fatigue cracks forming at material anomalies that can occur anywhere in the volume of the component. The automation significantly improves the efficiency of the analysis process while reducing the dependency of the results on the individual judgments of the analyst. The automation also facilitates linking of the life and reliability management process with a larger integrated computational materials engineering (ICME) context, which offers the potential for improved design optimization.

Numerical Simulation of Random Vibration for Electrical Connectors

Random vibration is one of the main factors influencing the reliability of electrical connectors. In this paper, a Micro-USB electrical connector was chosen to be an object for this research. The finite element method was applied in random vibration analysis. At first, a three-dimensional model of the electrical connector was established by using Pro/E and ANSYS Workbench software. Then, the modal characteristics analysis was carried out and its modal parameters can be obtained from this analysis and the reliability of the electrical connector under the natural frequency can be also verified. Finally, according to results of the simulation, the equivalent stress and deformation response under 1 sigma levels hadn't changed much. The conclusions can provide a reference for the reliability analysis of electronic components.

Dynamic Fuzzy Reliability Analysis of Mechanical Components with Small Samples of Load

Dynamic fuzzy reliability models of mechanical components are proposed in this paper in the case of small samples of load. The proposed models are established based on the distribution of load obtained from a load history, which reflects the relationship among the occurrence frequency of loads with different magnitude in a given time period, rather than the distribution at each load application. To deal with the uncertainty of the distribution of load from a load history due to the randomness of the magnitude of load at each load application, the statistical parameters involved in the distribution of load are modeled as fuzzy variables. Explosive bolts are chosen as representative examples to illustrate the proposed models. The results show that reliability increases with the increase in the level of cut set. Besides, the dispersion of strength has great influences on the reliability and failure rate of mechanical components.

Relevant drivers of farmers’ decision behavior regarding their adaptation to climate change: a case study of two regions in Côte d’Ivoire

In addition to many other stressors, climate change has emerged as one of the major threats for smallholder farmers from Toumodi in the center and Korhogo in the north of Côte d’Ivoire. This study examined farmers’ adaptation behavior with respect to subjective, socio-demographic, institutional, and physical variables. Focus group discussions with 205 farmers and large-scale surveys involving 800 farmers’ households were conducted in both study areas for data collection. The data were analyzed using a categorical principal component analysis (CATPCA), reliability analyses, and binary logistic regression models to determine relevant influences on farmers’ adaptation behavior. The results revealed that 77 % of farmers perceived high increases in temperature and 75 % perceived strong decreases in rainfall over the last 10 years. Farmers in Toumodi perceived more climate change than their colleagues in Korhogo. In addition, farmers from Korhogo perceived more new pests (81 %) and new weeds (87 %), while those from Toumodi agreed that there were strong changes in flowering and fruiting times (54 %). Furthermore, two main adaptation groups were identified: changes in the sowing management and the technical itinerary. The levels of implemented strategies differed significantly across regions. Factors such as the perceived occurrence of new pests and insects and support from national and international organizations were relevant to farmers’ decision to adapt to climate change. Moreover, the agro-ecological specificities, the types of food crops, and the owners of livestock had also an influence on farmers’ adaptation choices. The appropriate policy response should henceforth integrate these factors to support efficient adaptation processes in response to climate change in Côte d’Ivoire.

Assessment of Patients' Dignity in Cancer Care: Preliminary Psychometrics of the German Version of the Patient Dignity Inventory (PDI-G)

ContextThe Patient Dignity Inventory (PDI) is a valid and reliable instrument designed to measure different sources of dignity-related distress in cancer patients receiving palliative care. ObjectivesWe investigated item characteristics, factor structure, reliability, and concurrent validity of the German version of the PDI (PDI-G) among patients with cancer. MethodsPDI was translated into the German language following state-of-the-art criteria. In a sample of 112 inpatients with mixed tumor types, principal component analysis, reliability analysis (Cronbach's coefficient alpha), and correlation analysis were performed. Concurrent validity was evaluated by validated measures of distress, demoralization, anxiety, depression, hopelessness, quality of life, sense of meaning and purpose, and supportive care needs. ResultsCronbach's coefficient alpha for PDI-G was 0.96; factor analysis resulted in a four-factor solution, accounting for 71% of the overall variance, with factor loadings ranging from 0.49 to 0.86. Factor labels include Loss of Sense of Worth and Meaning, Anxiety and Uncertainty, Physical Symptom Distress and Body Image, and Loss of Autonomy, showing high internal consistencies ranging from Cronbach's α 0.80 to 0.95. Evidence for concurrent validity was established by significant associations between PDI-G scales and concurrent measures of distress. ConclusionAlthough we were unable to replicate the five-factor structure provided by Chochinov, this study gave strong support to an alternative four-factor solution of PDI-G, capturing all 25 items. We conclude that PDI-G is a psychometrically sound instrument assessing a broad range of dignity-related distress issues in patients with cancer.