Field Failure Data Research Articles

A method for evaluating safety reliability of descend/ascend drop-weight system and mission abort decision for deep-ocean scientific human submersibles

Descend and ascend of deep-water human-occupied submersibles based on buoyancy changes are enabled using a denomination of dispensable drop-weights, and loss of buoyancy are managed using emergency drop-weights and jettisoning identified systems. Failing to release the drop weights or jettison results in submersible stranding on the sea floor, leads to emergency recovery, and hence the drop-weight configuration has to be highly reliable. The paper, for the first time, based on hydrostatic stability, descend/ascend velocity needs, ocean salinity profile and loss-of-buoyancy situations, proposes a novel on-demand reliability based methodology for determining the safe drop-weight configuration and degradation-based mission abort strategy for deep-ocean human submersibles. Probabilistic on-demand reliability analysis based on IEC61508 standards for safety–critical systems using component field-failure data is carried out and the drop-weight configuration essential to meet the human-rated safety integrity level 3 during all stages of the subsea mission is identified for the deep-ocean human scientific submersible Matsya6000, based on which a mission abort protocol is evolved.

Intelligent mining methodology of product field failure data by fusing deep learning and association rules for after-sales service text

The after-sales service text contains wealthy field failure data, which can be used to estimate the field reliability of product and its components. However, human-based data mining suffers from low efficiency and unstable quality. This study proposes an innovative intelligent mining methodology that combines the product failure dictionary (PFD), gate recurrent unit (GRU) and association rules (AR) to solve the problem. Firstly, the text is studied, and the segmented mining process is developed. Secondly, the PFD is edited according to the analysis results of a large product failure sample, and combined with GRU to construct the PFD-GRU efficient mining model. Thirdly, based on the failure determination criteria and the dependency syntax analysis results of the text, the AR-based mining method is formulated for re-mining the text that does not satisfy the determination threshold of the PFD-GRU model to establish the PFD-GRU-AR intelligent mining methodology. The case study results of the after-sales service text mining for four types of air-conditioner show that the segmented mining pattern can obtain better quality mining results than the one-stage mining pattern, and the P,R, and F1 of the PFD-GRU model have increased by at least 1.48%, 1.72% and 1.6% respectively compared to the corresponding indicator values of the GRU model. With the increase of the threshold value, the quality and robustness are improved and the mining results tend to be stable. As threshold value is 0.95, the PFD-GRU-AR methodology obtains minimum value for P,R, and F1 as 98.54%, 99.09% and 98.81% respectively.

Reliability Analysis and Life Cycle Cost Optimization of Hydraulic Excavator

This study focuses on conducting a reliability analysis of an excavator from its field failure data and improve its reliability cost effectively. The aim of the research is to perform reliability estimation of systems and identify the critical subsystem with significant contributions to system unreliability. The reliability analysis was performed using repairable system data analysis approaches. Life cycle cost (LCC) was estimated for critical subsystems, and it was optimized to select cost effective reliability improvement strategy. The results of the study provide valuable insights into the performance and cost-effectiveness of the excavator and its subsystems, which can assist manufacturers and operators in optimizing their equipment’s reliability, availability while considering the cost implications over the life cycle of the equipment. The results show that the undercarriage has critical contributions to system unreliability. This study attempts deep down reliability analysis of critical undercarriage components for optimal selection of improvement method among feasible alternatives. LCC analysis and its optimization performed on the critical sub-system is expected to help OEM save approx. 15% of LCC. The empirical data used in the paper is based on field data gathered during the operational life of the hydraulic excavator over approx. six years in its Indian operations.

Reliability analysis of wind turbine subassemblies based on the 3-P Weibull model via an ergodic artificial bee colony algorithm

The Weibull distribution is the most widely used model for the reliability evaluation of wind turbine subassemblies. Considering the important role of the location parameter in the three-parameter (3-P) Weibull model and its rare application in wind turbines, this study conducted a reliability analysis of wind turbine subassemblies based on field data that obeyed the 3-P Weibull distribution model via maximum likelihood estimation (MLE). An improved ergodic artificial bee colony algorithm (ErgoABC) was proposed by introducing the chaos search theory, global best solution, and Lévy flights strategy into the classical artificial bee colony (ABC) algorithm to determine the maximum likelihood estimates of the Weibull distribution parameters. This was validated against simulation calculations and proved to be efficient for high-dimensional function optimization and parameter estimation of the 3-P Weibull distribution. Finally, reliability analyses of the wind turbine subassemblies based on different types of field failure data were conducted using ErgoABC. The results show that the 3-P Weibull model can reasonably evaluate the lifetime distribution of critical wind turbine subassemblies, such as generator slip rings and main shafts, on which the location parameter has a significant effect.

How extreme failure‐data filtering leads to poor process safety

AbstractThis paper describes both appropriate and inappropriate practices of filtering field failure data (FFD) prior to using the resulting failure count from the failure data to estimate failure rates. The underlying causes of inappropriate (extreme) filtering of FFD are examined, and the consequences (poor process safety) are discussed. An example is presented illustrating the benefits of using all real failures discovered in the field in the estimation of failure rates based on FFD.

Statistical concepts and data requirements for valid failure rate estimation

AbstractThe distinction between point estimation and confidence interval estimation for failure rates is described. Why the upper bounds of failure rate confidence intervals are prescribed by safety standards for use in safety metric computations is explained. Statistical formulas for the failure rate upper bounds are provided. The requirements that the field failure data (FFD) must meet to ensure the validity of the failure rate estimations are discussed from both mathematical and engineering perspectives. The need to understand the underlying causes of failure in determining when FFD can be aggregated is explained. Examples illustrating the impacts on failure rate estimation when the data requirements are and are not met are presented.

Application of the reliability analysis for modifications in maintenance scheduling

The preliminary examination of the reliability of specific workstations of milk powder and curd production lines in a dairy plant was conducted to improve its availability under actual working conditions. The current study makes use of field failure and repair data, as well as Weibull++ software and the finest fitness indexes. The time between failure (TBF) and time to repair (TTR) data are given as input to the software. The reliability and availability of subsystems at different intervals of time have been estimated. The maintenance schedule can be optimized based on reliability analysis and can be revised for the subsystem having more failures. Recommended maintenance schedule appears to be beneficial from the performance point of view as well as to maintain the quality of dairy products. The present work's reliability analysis indices are beneficial for identifying the essential subsystems of milk powder and curd production lines, as well as their influence on the system's performance and product quality. On the basis of analysis, maintenance policies can be decided for various subsystems.

Assessment of the Technological Maturity of Marine Autonomous Surface Ships

Abstract Marine Autonomous Surface Ships (MASS) are gaining increasing attention due to the potential benefits of improving safety and efficiency. The paper presents the trends in the development of autonomous ships and reliability modeling carried out to assess the maturity of Guidance, Navigation, Control, Power and Propulsion (GNCPP) subsystems that are critical for safe and reliable operation of MASS. Based on the simulations with field-failure data as inputs, it is identified that a typical vessel GNCPP system can have a mean time to fail period of 1.2 years. The emergency shore support systems shall require a proof test interval of 22 days to comply with IEC61508 HSE Safety Integrity Level 3 of on-demand availability. The results could help in efficient maintenance planning and integrity management of the autonomous ship systems. The efforts required to realize situation-aware autonomous navigation technologies complying with stringent operational regulatory frameworks are also discussed.

Left‐truncated and right‐censored field failure data: Review of parametric analysis for reliability

AbstractIn field reliability analyses, a data collection period is given to monitor the failure events from the field. Left‐truncation arises due to early failures occurring before the data collection period, and right‐censoring arises for late failures occurring beyond the monitoring period. Naïve analyses of left‐truncated and right‐censored data lead to biased estimation of the population lifetime of interest. A variety of models and methods have been developed to analyze the left‐truncated and right‐censored data for field reliability analyses. The goal of the paper is to review the existing models and methods for fitting left‐truncated and right‐censored data. Our review includes the existing statistical models, such as the exponential, Weibull, lognormal, gamma, Gompertz, Lomax, and spline models. We comprehensively review the statistical issues of maximum likelihood estimation, model selection, residual lifetime prediction, and Bayesian methods. Some of these methods are illustrated through the field reliability analysis of the electric power transformer dataset.

Availability optimization of biological and chemical processing unit using genetic algorithm and particle swarm optimization

PurposeThe demand of sewage treatment plants is increasing day by day, especially in the countries like India. Biological and chemical unit of such sewage treatment plants are critical and needs to be designed and developed to achieve desired level of reliability, maintainability and availability.Design/methodology/approachThis paper investigates and optimizes the availability of biological and chemical unit of a sewage treatment plant. A novel mathematical model for this unit is developed using the Markovian birth-death process. A set of Chapman–Kolmogorov differential equations are derived for the model and a generalized solution is discovered using soft computing techniques namely genetic algorithm (GA) and particle swarm optimization (PSO).FindingsNature-inspired optimization techniques results of availability function depicted that PSO outperforms GA. The optimum value of the availability of biological and chemical processing unit is 0.9324 corresponding to population size 100, the number of evolutions 300, mutation 0.6 and crossover 0.85 achieved using GA while PSO results reflect that optimum achieved availability is 0.936240 after 45 iterations. Finally, it is revealed that PSO outperforms than GA.Research limitations/implicationsThis paper investigates and optimizes the availability of biological and chemical units of a sewage treatment plant. A novel mathematical model for this unit is developed using the Markovian birth-death process.Originality/valueAvailability model of biological and chemical units of a sewage treatment is developed using field failure data and judgments collected from the experts. Furthermore, availability of the system has been optimized to achieve desired level of reliability and maintainability.

베이지안 네트워크를 이용한 출입문 장치의 확률론적 고장 분석

Unlike other products, these electric vehicles have undergone a period of reliability growth by manufacturer and operating company for a long time. Through many years, reliable field failure data is continuously stored in specific program. When uncertain failure of particular system is detected during this period, we suggest probabilistic method of reasoning that can show dependent relationship with subparts and estimate the reliability of system. In this study, failure mode effects and criticality analysis (FMECA) was conducted to extract the major failure and degradation factors of passenger door system (Centered on new manufactured vehicle line 2). In addition, Failure rates of sub parts are estimated by means of NSWC handbook and Field failure data. To show structural failure mode, we used fault tree analysis (FTA). Based on the fault states and failure rates, we estimated reliability of Passenger Door System using Bayesian network (BN). The goal is to execute dependability analysis for system reliability.

베이지안 기법을 이용한 품목 수준의 개발 신뢰도 최신화 방안 연구

Purpose: This study proposes the Bayesian model that can update the development phase reliability using the small field failure data.<BR>Methods: A Gaussian distribution with the development phase reliability as one parameter, μ, was used as the prior distribution of reliability. Moreover, the posterior distribution was estimated by Markov Chain Monte Carlo. The standard deviation was assumed to be proportional to the development phase reliability. That optimal proportional value, m, was derived from the simulation.<BR>Results: The effectiveness was verified by applying the proposed method to real cases. The proposed method involves only moderate fluctuations depending on the amount of data considering that only a fraction of data was used. Comparatively, the results of the existing methods showed large fluctuations.<BR>Conclusion: We expand the reuse area of the development phase reliability, where a high cost and effort was invested, Moreover, we propse a procedure to calculate meaningful reliability with a small amount of data. Moreover, an intuitive and familiar Gaussian distribution can double its effectiveness.

An Assessment of Validity of the Bathtub Model Hazard Rate Trends in Electronics

The bathtub model has often been cited as a hazard rate profile that predicts not only human mortality but also the hazard rate of manufactured products. This article examines the inapplicability of the bathtub model to predict the hazard rate of electronic components, products, and systems. The article reviews various literature and uses numerous experiments and field failure data to support its argument. The factors affecting the hazard rate of electronics are then discussed, common hazard rate trends for electronics are identified, and recommendations for the assessment of hazard rates are given. This article recommends that a preconceived model of hazard rate over time should not be used.

Risk-Based Threshold on Intensity Function of Repairable Systems: A Case Study on Aero Engines

Maintenance, repair and overhaul (MRO) facilities deal with situations where repairable systems and its components are required to be designated as high failure rate components (HFRCs). The shortlisted HFRCs are then selected for reliability improvement. The procedure of short listing components as HFRCs is commonly based on experts’ field experience or number of failures. In case of organizations dealing with complex and critical repairable systems like military aviation (MA) and nuclear industries, the subjectivity in the short listing of HFRCs can lead to prolonged unavailability of equipment and may incur financial loss. Thus, a scientific methodology is required to be developed for HFRC designation. The paper develops a methodology for HFRC designation through risk-based threshold on intensity function by considering combat aircraft engines as a case. To develop the threshold methodology, the paper uses generalized renewal process (GRP) for multiple repairable systems (MRS) considering both corrective and preventive maintenance as imperfect. The proposed methodology is duly validated with the help of field failure data of two variants of the same aero engine of a particular combat aircraft. The developed methodology in this paper is highly inspired by the problems faced by the various industries while operating the repairable systems and can be extended for systems which undergo periodic maintenance, repair and overhaul.

A rapid life-prediction approach for solder joints based on modified Engelmaier fatigue model

Accelerated life test (ALT) is widely used to predict life of solder joints nowadays, and how to shorten the test time and improve the precise of evaluation is one of the most important issues. Based on statistical results of field failure for solder joints, improved ALT under combined thermal cycling and vibration loading conditions was designed and carried out. This paper presents a modified Engelmaier fatigue model to predict life of solder joints, by taking into account field failure data and ALT testing data. The results show that test time of improved ALT is more shorter than conventional one, and the result of remaining life prediction based on modified Engelmaier fatigue model becomes more precise due to improving life model and increasing information.

Age Based Overhaul Policy for Multiple Repairable Systems with Imperfect Maintenance: Case Study of Aero Engines

Reliability analysis of complex multiple repairable systems (MRS) such as aero engines, rolling stocks and nuclear power plants has always been an area of interest for the research fraternity. An appropriate age based overhaul maintenance policy for such systems can provide impetus to the operations. The paper proposes two different age based maintenance policies; Policy-I and Policy-II, to evaluate the overhaul time of an aero engine, where Policy-I considers MRS with imperfect corrective maintenance (CM), whereas Policy-II examines MRS with both imperfect CM and preventive maintenance (PM). The paper then provides a spare parts estimation model for both the policies. The developed policies and spares parts model are validated by considering field failure data of aero engines as a case and the obtained results are compared with the existing time based maintenance policy used for aero engines. The paper recommends the best policy to be used for MRS in general and the considered case in particular.

A Study on the Prediction of Failure Rate of Airforce OO Guided Missile Based on Field Failure Data

A Study on the Prediction of Failure Rate of Airforce OO Guided Missile Based on Field Failure Data

Model Selection Methods for Reliability Assessment Based on Interval-Censored Field Failure Samples

Incomplete field failure data from automated production are often applied for evaluating the system reliability. But the evaluation could be impacted by the uncertainty of the product’s lifetime distribution, which is usually predetermined but may be misspecified. In this paper, we assume that the system lifetime distribution follows a location-scale family with several candidates instead of a certain distribution. Two model selection procedures are proposed to assign the most likely candidate distribution from a pool of the location-scale distributions based on interval-censored field failure samples. The maximum likelihood estimates (MLE) of parameters of the candidate distribution are estimated by using the Newton–Raphson method and the MLE of a quartile is assigned as the reliability measure for assessing the reliability of systems. To illustrate the applications of the proposed model selection procedures, an example of high-speed motor with interval-censored field failure data is given. Monte Carlo simulations are carried out to evaluate the performance of the proposed model selection procedures. Simulation results show that the proposed methods are efficient for model identification and can provide reliable reliability assessment.

A Study on Reliability Growth of P-3 Essential Avionic Equipments and Operational Availability Simulation

Failure of essential avionic equipments have a significant impact on the operations and safety of P-3 maritime patrol aircraft. Therefore, avionic equipments of P-3 are required to have higher reliability. Based on the field failure data, this paper studies the reliability growth of essential avionic equipments in P-3 using Duane model. Additionally, a simulation model is built and implemented for identifying the operational availability according to the field failure data of avionic equipments. Keywords: Reliability(ì‹ ë¢°ë„), Reliability Growth(ì‹ ë¢°ë„ 성장), Mean Time Between Failure(ê³ ìž¥ 간 í‰ê· ì‹œê°„), Simulation(ì‹œë®¬ë ˆì´ì ˜), Operational Availability(운용가용도)

On the Criticality Analysis of Computer Numerical Control Lathe Subsystems for Predictive Maintenance

Predictive maintenance of machine tools is gaining wide attention in the manufacturing sector for achieving higher production rates and closer tolerance of machined parts. However, the implementation of predictive maintenance requires additional instrumentation and computation software, causing huge installation costs. Hence, the application of predictive maintenance is limited to the most critical subsystems of the machinery. The present investigation puts forward a scientific methodology to identify the subsystems of a computer numerical control (CNC) lathe that must be considered for predictive maintenance. The field failure data for CNC lathe collected from various industries are subjected to fuzzy modified failure mode, effects and criticality analysis (fuzzy FMECA). A unique fuzzy risk priority number (fuzzy RPN) is assigned to every subsystem of the CNC lathe. This fuzzy RPN is used to prepare a maintenance priority ranking of the CNC lathe subsystems. The subsystems with higher fuzzy RPN are considered for predictive maintenance, and the subsystems with lower fuzzy RPN are considered for preventive or reactive maintenance. It is observed that the spindle unit has the highest fuzzy RPN 848.2, followed by the turret and chuck with fuzzy RPNs 527.5 and 475.7, respectively. FMECA also identifies potential failure modes associated with critical subsystems. This information is necessary for sensor selection in predictive maintenance. Further, the improvement of fuzzy FMECA over conventional FMECA is discussed. This study paves a path for identifying and prioritizing the critical subsystems of a machine tool for predictive maintenance.