Mean Time To Failure Research Articles

Reliability Prediction and FMEA of Loading and Unloading Truss Robot for CNC Punch

Loading and unloading truss robot for computer numerical control (CNC) punch is widely used in the production of sheet metal parts, as its reliability level is directly related to the quality of sheet metal parts. Especially for the automatic sheet metal production line, it is urgent to predict the reliability of the loading and unloading truss robot for CNC punch. In this paper, a new method for the reliability prediction of the loading and unloading truss robot for CNC punch is proposed. The method uses the component counting method to predict the failure rate and mean time between failures (MTBF) of the electrical control system. Then, according to the MTBF value of the electrical control system, the MTBF value and failure rate of the mechanical system and pneumatic system are calculated by expert scoring method based on fuzzy theory. The MTBF value and failure rate of the loading and unloading truss robot for CNC punch are estimated, and the weak link of the loading and unloading truss robot for CNC punch is obtained. Finally, through the collected fault maintenance data records, the failure modes and effects analysis (FMEA) of the loading and unloading truss robot for CNC punch is carried out, and the reliability prediction method proposed in this paper is validated by the fault location analysis results.

Implementation of the Mean Time to Failure Indicator in the Control of the Logistical Support of the Operation Process

The focus of this paper is to identify a method for defining the needs of logistical operational support based on the mean time to failure (MTTF) factor. The research was based on a helicopter intended for flight training. The MTTF indicator for selected equipment was determined based on failure data from previous flight operations. As the basic operational data for the developed method, the time from the beginning of the operation or the flight time from the last damage and the method of restoring airworthiness were selected. The MTTF and replacement index for the device were determined. The next step was to determine the index, based on selected probability distributions. The results were analyzed and presented in graphical form, and conclusions were drawn. Based on the MTTF index and replacement index, the logistics needs of selected devices were determined. The obtained results were compared with the actual exchanges of devices made in the year in question. The research proved that the MTTF reliability factor and the analysis of trends in value changes could be used to determine the needs for the logistical security of the operation process, particularly in relation to the equipment subject to accidental failures. This is important for maintaining high availability of an aircraft or other technical objects.

Automated design and usage of the Fault-Tolerant dynamic partial reconfiguration controller for FPGAs

This article presents a new design automation method for Fault-Tolerant (FT) systems implemented on dynamically reconfigurable Field Programmable Gate Arrays (FPGAs). The method aims at minimizing the human interactions needed to incorporate FT mechanisms into an existing system. It starts with a source code of an original unhardened circuit. It continues by automated manipulation of the source code, algorithmic strategic selection of suitable FT techniques, design space exploration of candidate FT implementations, and selection of the resulting implementation. The method also includes efficient evaluation of achieved FT parameters performed on the target HW. As a novel approach working on the level of HW description languages is employed, the code modification is separated, which differentiates our method from others. The case study utilizing this method targets the design of an experimental FT dynamic partial reconfiguration controller for an FPGA. This controller is helpful for the restoration of faulty components due to a single-event upset on an FPGA. We used the method to generate a set of Pareto-optimal controllers concerning the design’s Mean Time to Failure (MTTF) parameter, power consumption, and size. Then, the FT controller is connected to several benchmark circuits, and the reliability parameters are evaluated at the entire system level. Our results show that by replacing the standard reconfigurable controller with our automatically-designed FT controller for one specific benchmark, the design size increased by 20.1%, and MTTF increased by 11.7%. However, the efficiency is highly dependent on the target system size, MTTF, and circuit functionality. We also estimate that a complex system defined by half a million configuration bits would improve MTTF by more than 50%.

A Markov chain-based genetic algorithm for solving a redundancy allocation problem for a system with repairable warm standby components

Many studies have been conducted on designing systems based on the redundancy allocation problem (RAP). However, considering repairable warm-standby components (which are subject to failure even in an idle state) is somewhat neglected by researchers due to the complex mathematical models. One of the crucial aspects of these systems is considering the probability of failure when switching to a standby component or subsystem. This study tries to highlight these imperfect switching and switch selection strategies in the redundancy allocation designs. In this regard, this article is dedicated to developing two RAP models (a single objective and a bi-objective) with warm standby repairable components by proposing a solving approach based on the genetic algorithm (GA) and Markov chains. Since the model’s objective functions minimize the system’s mean time to failure (MTTF) and cost, we discussed how imperfect switches affect the total system’s cost and mean time to failure for the proposed RAPs. Finally, we adopted a GA and a non-dominated sorting genetic algorithm (NSGA-II) to solve the proposed models due to the models’ complexity. Solving these models clearly indicates the critical role of selecting an appropriate switching strategy on the system’s costs and reliability.

Assessing repair and maintenance efficiency for water suppliers: a novel hybrid USBM-FIS framework

A metropolitan area's water supply is imperative to the city's development. One of the main goals of the water supply utilities is to ensure the availability of water, as a lack of water would cause many social, political, or health problems. Therefore, water supply facilities must be in good condition, efficient preventive maintenance plans must be implemented, and the performance of the maintenance team monitored. In this paper, efficiency indices of Tehran water utility maintenance teams are investigated using different Data Envelopment Analysis (DEA) models. The final scores were then used as inputs to a Fuzzy Inference System (FIS) to assess the efficiency of these maintenance units. Two performance indicators based on DEA, "Availability efficiency" and "Repair time efficiency" are introduced for performance assessment. The Mean Time Between Failure (MTBF) and the Ready To Operate (RTO) are two desirable outputs that are considered in addition to one undesirable output: the Mean Time To Repair (MTTR). In addition, we suggest a new index named MRRW by combining the DEA efficiency with the RRW index. We introduce a novel approach based on DEA combined with FIS methods and the new factors for evaluating water supply maintenance systems, while most previous studies on measuring the efficiency of maintenance teams consider only limited aspects of performance measurement. Based on the results of our study, it became clear that the MRRW measures efficiency better than the traditional RRW measures. We present future improvement strategies based on the output of the FIS.

Linear Consecutive-k-out-of-n: G System Reliability Analysis

The concerned study pertains to the development of a new stochastic model for the reliability analysis of linear consecutive-k-out-of-n: G system, where k>n2. In the developed model, system may collapse as a result of common cause failure or hardware failure in its units. The system has exponentially distributed failure rates, and in case of breakdown, it is repaired with the copula method. The developed model has been examined through supplementary variable technique (SVT) along with Laplace transform. The current paper has specifically studied consecutive-(n-1)-out-of-n: G system. The performance of such system having ten components is explored and its various reliability measures have been obtained and discussed with the help of graphs. The originality of this work lies in incorporating common cause failure in conjunction with copula repair in the reliability modeling of consecutive systems through the SVT. The study confirms that an increase in failure rates and the number of components of the concerned system decreases mean time to failure (MTTF). The profit of linear consecutive-9-out-of-10: G system is examined with the help of a numerical example.

Reliability-Based Analysis of Main Propulsion Fuel Oil System Maintenance for Tugboats with Qualitative and Quantitative Methods

This study aims to analyze the maintenance of the main propulsion fuel oil system for tugboats using both qualitative and quantitative methods. The focus is on evaluating the reliability of the system and identifying potential areas for improvement. The research employs a combination of expert interviews, industry standards, and statistical analysis to provide a comprehensive overview of the maintenance practices in place. The goal is to enhance the overall performance and longevity of the main propulsion fuel oil system in tugboats. The study uses data on the operational time, failure time and frequency, the number of vessels served, and fuel system diagrams to analyze the system's reliability. Qualitative methods such as Failure Mode and Effect Analysis (FMEA) and Fault Tree Analysis (FTA) were used, as well as quantitative methods such as Overall Equipment Effectiveness (OEE), Markovian Decision Process (MDP), and reliability methods. The study found that the FO Purifier component was the critical component with a Risk Priority Number (RPN) of 294. The average value of OEE was 47%, lower than the standard of 85%. The MDP analysis showed a probability of 0.08 for mild damage and 0.46 for moderate to severe damage under steady-state conditions. The FO Purifier component had the lowest Mean Time to Failure (MTTF) value of 1658.50 hours. The study provides a graph of the reliability function against time, and recommends maintenance actions based on the MTTF and MDP.

Reliability Testing of Wind Farm Devices Based on the Mean Time between Failures (MTBF)

Among the most valuable types of renewable energy available today is wind energy. The reliability of WF systems must be regularly evaluated at every stage of their “life,” from design to operation, if a wind farm energy system is to be effective and function damage-free. Three key goals are presented in the article. The theory of fundamental quantities in reliability and maintenance analysis should be derived and explained first. Second, as a consequence of maintainability, theoretical correlations between reliability and mean time between failures (MTBF) are provided. The three-state theory of the WF procedure for operation presented in the research serves as the foundation for the analytical analysis of WF reliability. The time between failures is investigated as a function of maintainability, and the dependability of the WF under examination is assessed as a function of service life. The WF owner can make the best decisions to renew the WF and increase its reliability, energy, financial efficiency, etc. by being aware of the existing reliability of the WF system in use.

A lifetime prediction approach for LED packages in paralleled under thermal-electronic coupling effects

For LED packages in parallel, the driving current depends on the catastrophic failure history, so LEDs break down faster than that with the constant current hypothesis. This work proposes a hybrid reliability assessment approach that is based on the combination of the symbol sequence with Physics of Failure (PoF) modelling for parallel LEDs. A symbol sequence is proposed to abstract lumen maintenance curves into several failure modes. According to the monotonicity of the proposed symbol sequence, the boundary lifetime and probability is required to obtain the reliability of the light source in terms of mean time to failure (MTTF). The PoF simulations are utilized to calculate lifetime, temperature and thus probability of each failure mode with consideration of interaction between the lumen decay and open circuit. A temperature- and time-dependent model is applied to describe the LED lumen depreciation. LED's open circuit probability is considered as functions of temperature. The parameters of the proposed methodology are extracted by experiments which select a commercial type of LED. With the combined effect of the lumen depreciation and open circuit failuires, the time of open circuit failure determines the failure mode of the light source. By finding 3 boundary points among failure modes, the proposed approach obtains accurate survival rates as function of lifetime and MTTF of LED packages in parallel than conventional approaches.

Process modeling for decomposition unit of a UFP for reliability indices subject to fail-back mode and degradation

PurposeEnsuring safe operation of a urea fertilizer plant (UFP) is a vital aspect for its functioning and production. Clearly the safe operation of such systems can only be archived with proper and effective maintenance scheduling and through controlling its failures as well as repairs of the components. Also for this, the concern plant management must have the information regarding the failures that affects the system's performance most/least. The objective of this study is to analyze mathematically the factors that are responsible for the failure/degradation of the decomposition unit of UFP.Design/methodology/approachThe considered system has been modeled by the aid of Markov's birth–death process with two types of failures for its components: variable (which are very similar in practical situations) and constant. The mathematical model is solved by the help of Laplace transform and supplementary variable technique.FindingsIn the present paper, the availability, reliability and mean time to failure (MTTF) are computed for the decomposition unit of the UFP. The critical components that affect the reliability and MTTF of the decomposition unit are identified through sensitivity analysis.Originality/valueIn this paper, a mathematical model based on the working of the decomposition unit of a UFP has been developed by considering two types of failure, namely, variable failures rates and constant failure rates (which has not been done in the literature for the decomposition unit). Conclusions in this paper are good references for the improvement of the same.

A stochastic Petri Net‐based approach for operational performance estimation of quay cranes

AbstractReliability, availability, and maintainability (RAM) of quay cranes (QCs) are essential for an effective port operation. This study estimates operational RAM of QCs using the Stochastic Petri Net (SPN) modelling. Asset Performance Assessment (APA) was performed to conduct the study by a SPN model. Based on the operation and maintenance data, probability distributions of Mean Time Between Failure (MTBF) and Mean Time to Repair (MTTR) is determined by Goodness of Fit Test of Anderson Darling. Subsequently, these distributions are applied in APA to determine the frequencies of failure/breakdowns, duration of downtimes, availability, and reliability with the assistance of the SPN model and Monte Carlo Simulation. The availability of the analysed QC is 0.97 or 97%. The results of this work are verified by the comparison with historical data. The outcomes of this paper contribute to reliability, availability, maintainability, and risk management of QCs.

THD Minimization and Reliability Analysis of Cascaded Multilevel Inverter

Inverters are DC to AC power converters that are widely used in AC motor drives and distributed energy generation systems. Multi-Level Inverters (MLIs) have emerged as the preferred inverter technology because of their advantages of reduced switching losses and better harmonic profile. This paper deals with the Minimization of Total Harmonic Distortion (MTHD) of Symmetric Cascaded H-bridge Multi-Level Inverter (SCMLI) and Asymmetric Cascaded H-bridge Multi-Level Inverter (ACMLI). A hybrid memetic algorithm composed of heuristic PSO (Particle Swarm Optimization) algorithm and traditional MAS (Mesh Adaptive direct Search) is proposed to optimize the switching angles. In Photo Voltaic (PV) system, the input DC voltage generally varies from its nominal value due to the change in temperature and irradiance. Thus, the sensitivity analysis of THD and harmonics is also carried out considering the variations with non-integer magnitudes of input DC sources. The experimental prototype of SCMLI and ACMLI topology is developed and validated with simulation results. The reliability and Mean Time to Failure (MTTF) of SCMLI and ACMLI are investigated based on power losses of the MOSFET and thermal parameters.

A design strategy for performance improvement of capacitive sensors for in-flight oil-level monitoring aboard helicopters

Cylindrical capacitors are largely employed in avionic industry as contact submerged probes of level sensors for in-flight oil-level monitoring for many reasons: their high robustness, their long MTBF (Mean Time Between Failures), maintenance-free character, virtually infinite resolution readings and last, relatively low cost. However, the cylindrical capacitors suffer from low sensitivity mainly due to small oil permittivity. This is clearly a disadvantage which collides with the ever-increasing demand for higher static and dynamic performances. To improve that, the approach adopted here consists in tweaking the conventional design of this kind of sensors guided by the study of their limitations in terms of static errors and poor responsiveness caused by phenomena of capillarity, high viscosity, and vibrations. Indeed, sensitivity doubling is proved to be achieved without compromising the indispensable former qualities. This is shown by the results of electrostatic, fluid mechanics, and structural dynamics analyses presented with enough details. Numerical simulations have been carried out and are here presented to confirm results. To summarize, with respect to the conventional capacitive level sensors currently available on the market, the achievements of the proposed design are i) an improved sensitivity that, for engine oils, is greater than 700pF/m, ii) a lower cost even though extra-costs for surface perforation must be accounted for, iii) cancellation of systematic error due to capillary phenomena and iv) improved dynamic response. Also, accurate experimental verifications are being carried out and will be shared in a future paper.

Weibull model for RUL estimation at RSG-GAS reactor implemented on PA01-AP01 secondary pump

Abstract Remaining Useful Life (RUL) estimation has been extensively explored in recent years. RUL could be used in deciding the maintenance timeline or inspection interval for the Reaktor Serba Guna – G. A. Siwabessy (RSG-GAS reactor). RSG-GAS reactor is a pool-type research reactor (built by the Interatom Internationale of Germany) and has been operating for more than 30 years to date. This study aimed to propose a Weibull model to find the RUL estimation value of the distribution parameters of the mean time to failure (MTTF). Therefore, the RSG-GAS reactor would be higher safety, longer lifetime and higher reliability with a smaller failure rate including for the PA01-AP01 secondary pump. The research methodology is processing data collection and estimating the parameters of the Weibull model to determine maintenance timeline or inspection intervals based on the MTTF value in case the reliability has reached the targeted percentage. Results show that the RUL estimation has been obtained for the RSG-GAS reactor. In the implemented study, a maintenance timeline has been stipulated for the PA01-AP01 secondary pump (with the model of KSB and type of CPK-S350-400) for the reliability of 90% and RUL estimation of circa 29 days.

Reliability modeling and analysis of cycloid gear grinding machines based on the bootstrap-bayes method

To increase the reliability of cycloidal wheel grinding machines, reduce the failure rate of machine tools, and shorten maintenance times, a reliability modeling method for small-sample fault data is proposed based on the Bootstrap-Bayes method. The mean time between failures (MTBF) of a machine tool generally conforms to a Weibull distribution. Based on the historical fault information and similar fault information, the distribution function for the mean time between failures of a machine tool is determined. The fault information is expanded by the self-help method, and the two-parameter distribution interval of the distribution function is calculated by the Bayesian formula. An example reliability calculation for a cycloid gear grinding machine is given, including the failure analysis and maintenance methods of the gear grinding machine. This approach can also be used to simulate and analyze the reliability of other computer numerical control (CNC) machine tools.

Measuring Reliability of A Web Portal Based on Testing Profile

Conventionally, the reliability of a web portal is validated with generalized conventional methods, but they fail to provide the desired results. Therefore, we need to include other quality factors that affect reliability such as usability for improving the reliability in addition to the conventional reliability testing. Actually, the primary objectives of web portals are to provide interactive integration of multiple functions confirming diverse requirements in an efficient way. In this paper, we employ testing profiles to measure the reliability through software operational profile, input space profile and usability profile along with qualitative measures of reliability and usability. Moreover, the case study used for verification is based on a web application that facilitates information and knowledge sharing among its online members.The proposed scheme is compared with the conventional reliability improvement method in terms of failure detection and reliability. The final results unveil that the computation of reliability by using the traditional method (utilizing failure points with the assistance of Mean Time Between Failures (MTBF) and Mean Time To Failure (MTTF) becomes ineffective under certain situations. Under such situations, the proposed scheme helps to compute the reliability in an effective way. Moreover, the outcomes of the study provide insight recommendations about the testing and measurement of reliability for Web based software or applications.

Manufacturing Reliability and Cost Improvements through Data Analytics: An Industry Case Study

Industry has entered a new age of industrial change which is revolutionising how products are manufactured. Driving this industry change are newly developing digital technologies such as robotics, artificial intelligence, advanced analytics and the industrial internet. This convergence of technology and manufacturing is driving the digital transformation of every industrial segment from operations to logistics, and from aeronautics to retail product manufacture. The technology advances evident from Industry 4.0 will result in industry disruption and build competitive advantages for the organisations who can master the technologies in their manufacturing processes. However, organisations are struggling to develop a tactical methodology to introduce these technologies while simultaneously transforming their organisation cultures and organisations and maximising the benefits. One of the key reasons for the Fourth Industrial Revolution called Industry 4.0 is the need to strengthen the competitiveness of Western European economies, which as a result of the progressing globalization process and rising labor and business costs [1]. A key benefit of the introduction of Industry 4.0 initiatives in a manufacturing plant is to help overcome current operations management limitations, such as, the lack of knowledge on how the process is performing at a specific point in time. This paper will investigate the impact of deploying a range of Industry 4.0 technologies to access machine and operations performance data, to improve equipment reliability and to reduce the costs associated with the maintenance of equipment. With the introduction of the operational data analytics as a result of data extraction from manufacturing equipment, along with integrating other data sources, the business has realised improvements in key metrics like OTIF (On Time in Full), OEE (Overall Equipment Effectiveness), MTBF (Mean Time Between Failure), MTTR (Mean Time to Return), CuC (Consumable Unit Cost), and Lead Time. This paper discusses this project and the results obtained at Zimmer Biomet while also discussing the research carried out for ZOML to begin its journey on manufacturing digitalisation.The paper starts with a background in Industry 4.0. The next section provides a background on the company where the problem statement and project have come from with some detail on their digitalisation journey. The following section details the problem and the project that the research is derived from and the final section is in relation to the results of the research and the project.

Research on Reliability Growth of Chain Transmission System in Rapid Secure Device of Shipborne Helicopter

Aiming the chain transmission system in the rapid secure device (RSD) of shipborne helicopter, an individual fault was that the chain was broken, and then the system gave an alarm. According to the above question, the RSD reliability growth test bench was built to conduct a reliability growth test study on the chain transmission system in a single RSD test prototype. After the cause of the fault was found, a part of the chain in the test prototype was tested by the chain pulling test machine. According to the test results, the reliability optimization design of the chain transmission system was carried out, and the optimization design scheme was determined. A suitable reliability growth test plan for the chain transmission system was formulated using the Duane model analysis method. Through the reliability growth test, the Class B failure statistics of the chain transmission system were obtained, and then the reliability growth Duane model of the total test time of the chain transmission system was established. Using the army materiel systems analysis activity (AMSAA) model analysis method, the approximate unbiased estimation of the instantaneous mean time between failures (MTBF) of the chain transmission system was carried out, the trend test of the reliability growth of the chain transmission system was carried out, and the confidence interval of the instantaneous MTBF at the end of the test time was obtained to verify that the reliability optimization design meets the planning requirements and provide a reference for the reliability growth research of the whole RSD equipment.

Fault-Tolerant Spiking Neural Network Mapping Algorithm and Architecture to 3D-NoC-Based Neuromorphic Systems

Neuromorphic computing uses spiking neuron network models to solve machine learning problems in a more energy-efficient way when compared to conventional artificial neural networks. However, mapping the various network components to the neuromorphic hardware is not trivial to realize the desired model for an actual simulation. Moreover, neurons and synapses could be affected by noise due to external interference or random actions of other components (i.e., neurons), which eventually lead to unreliable results. This work proposes a fault-tolerant spiking neural network mapping algorithm and architecture to a 3D network-on-chip (NoC)-based neuromorphic system (R-NASH-II) based on a rank and selection mapping mechanism (RSM). The RSM allows the ranking and rapid selection of neurons for fault-tolerant mapping. Evaluation results show that with our proposed mechanism, we could maintain a mapping efficiency of 100% with 20% spare rate and a fault rate (40%) more than in the previous mapping framework. The Monte Carlo simulation evaluation of reliability shows that the RSM mechanism has increased the mean time to failure (MTTF) of the previous mapping technique by 43% on average. Furthermore, the operational availability of the RSM for mapping to a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\times 4\times 4$ </tex-math></inline-formula> (smallest) and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6\times 6\times 6$ </tex-math></inline-formula> (largest) NoC is 88% and 67% respectively.

Incorporating deep learning data analytics techniques in the optimisation of capacitated planned maintenance

Manufacturing systems must be supported by the availability of materials, a streamlined production process and a prepared production line to achieve the production target. In a mass customization manufacturing system, the number of machines required for customization is relatively small. Conse-quently, maintenance on critical machines will impact this manufacturing system the most. Two types of maintenance strategies are implemented: corrective and preventive maintenance. The corrective maintenance requires more resources since the time and cost to repair the breakdown machine will be higher due to fatal failure. For the management to consider preventive maintenance while the binding machines are still operational, it must be equipped with a deep analysis demonstrating that fewer resources will be required. This paper discusses two deep analyses: accurate prediction of the binding machines' breakdown based on Mean Time Between Failure (MTBF) data using a deep learning data analytics technique and optimizing the maintenance total cost in the available capacitated time. The findings and results of this paper show that the proposed deep learning data analytics technique can increase the MTBF prediction accuracy by up to 66.12% and reduce the total maintenance cost by up to 4% compared with the original model.