Time Between Failures Research Articles

Optimising LPG Bottling Plant With DES Using Flexsim Simulation Tool

Purpose: This study explores the application of Discrete Event Simulation (DES) using FlexSim software to enhance the operational efficiency of a Liquefied Petroleum Gas (LPG) bottling plant. The primary goal is to ensure that the LPG plant can safely and efficiently meet escalating market demands, thereby prioritising all stakeholders' safety. Design/Methodology/Approach: The research design focused on empirical research and experimental simulation modelling. The study began with collecting and analysing one month of LPG plant data, laying the foundation for developing a simulation model. Verification and validation processes ensured the model's accuracy, enabling the investigation of various operational scenarios. The key performance indicators like First Time to Failure (FTTF), Time Between Failures (TBF), and Time to Repair (TTR) were analysed. The availability rates were 77% from actual data and 76% from simulations, showing that the model is suitable for real-world use. Findings: This study's findings underscore the potential impact of proactive maintenance strategies and operational enhancements as practical and applicable approaches to optimising performance. The analysis also revealed significant improvement opportunities through what-if scenarios: increasing MTBF by 100%, reducing MTTR by 50%, and raising conveyor speed by 15%. Research Limitation: The study's dependence on a systematic literature review could restrict its ability to capture the industry's real-time dynamics. Practical Implication: Implementing proactive maintenance strategies and operational enhancements as practical approaches can reduce downtime and costs and promote productivity and safety. Social Implication: Optimising plant operations will help maintain supply chain stability with the growing demand for LPG. Originality/Value: This work contributes valuable insights and recommendations, establishing a foundation for informed decision-making in the LPG bottling sector.

Predictive maintenance of vehicle fleets through hybrid deep learning-based ensemble methods for industrial IoT datasets

Abstract Connected vehicle fleets have formed significant component of industrial internet of things scenarios as part of Industry 4.0 worldwide. The number of vehicles in these fleets has grown at a steady pace. The vehicles monitoring with machine learning algorithms has significantly improved maintenance activities. Predictive maintenance potential has increased where machines are controlled through networked smart devices. Here, benefits are accrued considering uptimes optimization. This has resulted in reduction of associated time and labor costs. It has also provided significant increase in cost benefit ratios. Considering vehicle fault trends in this research predictive maintenance problem is addressed through hybrid deep learning-based ensemble method (HDLEM). The ensemble framework which acts as predictive analytics engine comprises of three deep learning algorithms viz modified cox proportional hazard deep learning (MCoxPHDL), modified deep learning embedded semi supervised learning (MDLeSSL) and merged LSTM (MLSTM) networks. Both sensor as well as historical maintenance data are collected and prepared using benchmarking methods for HDLEM training and testing. Here, times between failures (TBF) modeling and prediction on multi-source data are successfully achieved. The results obtained are compared with stated deep learning models. This ensemble framework offers great potential towards achieving more profitable, efficient and sustainable vehicle fleet management solutions. This helps better telematics data implementation which ensures preventative management towards desired solution. The ensemble method's superiority is highlighted through several experimental results.

Minimizing the expected maximum lateness for a job shop subject to stochastic machine breakdowns

This paper addresses a stochastic job shop scheduling problem with sequence-dependent setup times, aiming to minimize the expected maximum lateness. The stochastic nature is modeled by considering uncertain times between failures (TBF) and uncertain times to repair (TTR). To tackle this problem, a simheuristic approach is proposed, which combines a tabu search (TS) algorithm with Monte Carlo simulation. A total of 320 instances were used to conduct multiple experiments. Instances were generated with two distributions to study the behavior of stochastic TTR and TBF under log-normal and exponential distributions. Firstly, the performance of the simheuristic was evaluated for small instances by comparing it with the simulation of optimal solutions obtained with a mixed-integer linear programming (MILP) model. The simheuristic approach demonstrated an average improvement of around 7% compared to the simulation of MILP model solutions. Secondly, the simheuristic performance was evaluated for medium and large-size instances by comparing it with the simulation of the solutions obtained by the earliest due date (EDD) and process time plus work in the next queue plus negative slack (PT + WINQ + SL) dispatching rules. The results showed an average improvement of around 11% compared to EDD and 14% compared to PT + WINQ + SL. Furthermore, the results highlight that even when the two distributions have the same expected value and coefficient of variation, they can yield different expected maximum lateness values. This emphasizes the importance of precise distribution fitting when solving real cases to achieve effective scheduling performance.

Fault Tree Analysis of a Full-Ocean-Depth Unmanned Submersible

Abstract Due to the rising demands for applications in extreme environments and technological breakthroughs, today's marine equipment system is increasingly intricate and powerful. As a result, the issue of reliability is gaining prominence. One of the most efficient approaches in reliability analysis is Fault Tree Analysis (FTA). The FTA method and the Monte Carlo simulation were used to access the system reliability of a full-ocean-depth unmanned submersible (FODUS). The life distribution function of the system is determined, and the FODUS system's Mean Time Between Failures and the importance index of each basic component are calculated. The results will serve as a reference for future research on the reliability of deep-sea submersibles.

Reliability and maintainability optimization of load haul dump machines using genetic algorithm and particle swarm optimization

PurposeReliability and maintainability estimation of any system depends on the identification of the best-fitted probability distribution of failure and repair rates. The parameters of the best-fitted probability distribution are also contributing significantly to reliability estimation. In this work, a case study of load haul dump (LHD) machines is illustrated that consider the optimization of failure and repair rate parameters using two well established metaheuristic approaches, namely, genetic algorithm (GA) and particle swarm optimization (PSO). This paper aims to analyze the aforementioned points.Design/methodology/approachThe data on time between failures (TBF) and time to repairs (TTR) are collected for a LHD machine. The descriptive statistical analysis of TBF & TTR data is performed, trend and serial correlation tested and using Anderson–Darling (AD) value best-fitted distributions are identified for repair and failure times of various subsystems. The traditional methods of estimation like maximum likelihood estimation, method of moments, least-square estimation method help only in finding the local solution. Here, for finding the global solution two well-known metaheuristic approaches are applied.FindingsThe reliability of the LHD machine after 60 days on the real data set is 28.55%, using GA on 250 generations is 17.64%, and using PSO on 100 generations and 100 iterations is 30.25%. The PSO technique gives the global best value of reliability.Practical implicationsThe present work will be very convenient for reliability engineers, researchers and maintenance managers to understand the failure and repair pattern of LHD machines. The same methodology can be applied in other process industries also.Originality/valueIn this case study, initially likelihood function of the best-fitted distribution is optimized by GA and PSO. Reliability and maintainability of LHD machines evaluated by the traditional approach, GA and PSO are compared. These results will be very helpful for maintenance engineers to plan new maintenance strategies for better functioning of LHD machines.

A machine learning-based approach for product maintenance prediction with reliability information conversion

Predictive maintenance (PdM) cannot only avoid economic losses caused by improper maintenance but also maximize the operation reliability of product. It has become the core of operation management. As an important issue in PdM, the time between failures (TBF) prediction can realize early detection and maintenance of products. The reliability information is the main basis for TBF prediction. Therefore, the main purpose of this paper is to establish an intelligent TBF prediction model for complex mechanical products. The reliability information conversion method is used to solve the problems of reliability information collection difficulty, high collection cost and small data samples in the process of TBF prediction based on reliability information for complex mechanical products. The product reliability information is fully mined and enriched to obtain more reliable and accurate TBF prediction results. Firstly, the Fisher algorithm is employed to convert the reliability information to expand the sample, and the compatibility test is also discussed. Secondly, BP neural network is used to realize the final prediction of TBF, and PSO algorithm is used to optimize the initial weight and threshold of BP neural network to avoid falling into local extreme value and improve the convergence speed. Thirdly, the mean-absolute-percentage-error and the Coefficient of determination are selected to evaluate the performance of the proposed model and method. Finally, a case study of TBF prediction for a remanufactured CNC milling machine tool (XK6032-01) is studied in this paper, and the results show that the feasibility and superiority of the proposed TBF prediction method.

Estudo da manutenção de equipamentos de uma usina de etanol sob a metodologia MCC (manutençâo centrada na confiabilidade)

A manutenção e a sua gestão possuem uma importância substancial na busca de vantagem competitiva para as empresas. Por isso, a análise de confiabilidade de seus equipamentos é parte fundamental para a definição da estratégia de manutenção mais adequada. O objetivo deste estudo é avaliar por meio da metodologia MCC (Manutenção Centrada na Confiabilidade) a curva do ciclo de vida de uma usina de cana de açúcar localizada na região central do Estado de São Paulo e desta forma responder se a atual estratégia é a mais adequada para o gerenciamento da sua manutenção. O método de pesquisa utilizado foi baseado na modelagem estatística aplicada aos tempos entre falhas (TBF) e aos tempos de reparo (TTR) de todos os equipamentos industriais que paralisam a operação da moenda, ou seja, a interrupção na linha de produção. Estes tempos foram modelados por uma distribuição de probabilidade de Weibull e os resultados mostram que dentre as estratégias utilizadas pela usina, o uso intensivo da manutenção preventiva sem um critério definido para vários tipos de manutenção (emergencial, preventiva, corretiva e preditiva) durante a safra não é adequado. Como a análise estatística identificou, historicamente estes equipamentos se encontram na fase de mortalidade infantil do ciclo de vida útil, portanto sugere-se eliminar a causa raiz das falhas utilizando estratégias mais apropriadas e recomendadas pela literatura como as manutenções corretivas e emergenciais.

Strategic formulation of industrial maintenance based on equipment reliability in a sugar and ethanol production plant

Maintenance and management have substantial importance in the search of the company’s competitive advantages. In this direction, a reliability analysis of equipments is very important for the definition of the most suitable maintenance strategy. The main goal of this paper is to assess the reliability-centered maintenance of the industrial reliability curve of a sugar cane department of an industry located in São Paulo State, Brazil. The proposed research method was based on the application of existing statistical modeling for the times between failures (TBF) of all reported equipment failures or the interruption times in the production line. These times were modeled by standard lifetime probability distributions as log-normal and Weibull distributions. The results showed that the company's strategy for preventive maintenance during the off-season is not adequate and the statistical analysis also identified important factors that affect the company's maintenance strategy. These results could be of great interest for the company and for engineering applications in general.

Model based reliability analysis of shovel – dumper system’s mechanical failures used in the surface coal mine: a case study

In a surface coal mine, efficient usage of shovels and dumpers should be most reliable and available to reach their production target. Keeping this in mind, this study was carried out at a surface coal mine, which is located at Open Cast-I, The SCCL, Kothagudem area, Telangana. The mechanical failure data such as time between failures (TBF) and time to repair (TTR) of each sub-system of a shovel-dumper system were collected and analysed statistically and graphically for reliability and availability. Graphical methods such as trend test and series correlation test and Chi-squared test were used to validate the independent, identically distribution of mean time between failures and mean and time to repair for the same a shovel and dumpers. Statistically, the Weibull distribution is selected as it was found the best-fit distribution by the Kolmogorov-Smirnov (K-S) test using Isograph Reliability Workbench. In K-S test, it was found that shovel KS1 has reliability of 0.2682 and dumpers BD1, BD2, KD2 and KD4 had reliability of 0.3731, 0.2850, 0.2897 and 0.2962, respectively for the 1 year of working hours. Also, calculated reliability- based time interval for preventive maintenance to achieve 70, 80 and 90%, respectively of reliability for same shovel-dumper system.

Exact distribution of time between failures under gamma lifetime model

This paper is concerned with the distribution of the time between failures (TBF) under the gamma lifetime model. The exact distribution of TBF is computed in closed form. This allows interested researchers to compute the reliability of certain system models. To better visualize the distributions of TBF, its probability distribution function (pdf) is represented using matrices. Moreover, the moments of TBF is computed and given in closed form. Wolfram Mathematica cods are given in the appendix for fast and easy implementation. Finally, simulation studies for the TBF are performed together with relative tests results.

Exact distribution of time between failures under gamma lifetime model

This paper is concerned with the distribution of the time between failures (TBF) under the gamma lifetime model. The exact distribution of TBF is computed in closed form. This allows interested researchers to compute the reliability of certain system models. To better visualize the distributions of TBF, its probability distribution function (pdf) is represented using matrices. Moreover, the moments of TBF is computed and given in closed form. Wolfram Mathematica cods are given in the appendix for fast and easy implementation. Finally, simulation studies for the TBF are performed together with relative tests results.

Probabilistic Analysis of the Reliability Performance for Power Transformers in Egypt

From reliability, maintainability, and availability (RAM) points of view, the performance of power transformers has a significant impacts on the performance of the entire power network. Their performance has also a significant impacts on the power interruptions at various voltage levels and the consequent customer interruption costs. This paper will discuss the estimated remaining lifetime of power transformers in the voltages subpopulations: 500kV, 220 kV, 132 kV, 66kV and 33 kV of the Egyptian grid as time between failures (TBFs) will be determined and then determine the best fit probability distribution using MATLAB program for repair time, customer interruption costs (CIC) and TBFs, this is performed in order to estimate remaining lifetime of the transformers, from which the best fit probability distribution will be used in this case study which is Weibull distribution. Finally availability of the transformers per different voltage populations is calculated and determined. Different subassemblies (failures) are also undergo the same process of determining TBFs and estimating remaining lifetime. The results will be helpful in the manufacturing process of the transformers and enhancing the maintenance schedule.

Analysis of reliability and maintainability for multiple repairable units (Case study: Sungun copper mine)

The appropriate operating of mining machines is affected by both the executive and environmental factors. Considering the effects and the related risks lead to a better understanding of the failures of such machines. This leads to a proper prediction of the reliability parameters of such machines. In this research work, the reliability and maintainability analysis of the loading and haulage machines in the Sungun Copper Mine, considering the repair condition as multiple repairable units, was performed. For this purpose, the data necessary for the loading and haulage equipment including 2 loaders and 8 dump trucks for a 15-month period was collected and categorized in 10 operational units after the system and sub-systems of the department were determined. Initially, the time between failures (TBFs) and time to repair (TTR) for each unit was calculated. Then 20 sub-systems were developed. Primarily, the Stata software was utilized to carry out the heterogeneity test for all the sub-systems. In consequence, most of the sub-systems were regarded as the heterogeneous ones, except for 7 of them including the dump truck units 1, 2, 3, 4, 5, 7, and 8 in TBFs. Hence, PHM that is a covariate-based model displayed the heterogeneous group. Its reliability function was also estimated. For the next step, the trend tests were done on the non-heterogeneous sub-systems by means of the Minitab software. The homogeneous sub-systems with failure trend were modeled by “NHPP”. Afterwards, the non-trended sub-systems formed the data group. Later, the correlation tests were modeled by “HPP”. Finally, the reliability and maintainability functions were calculated with the 95% confidence level.

Evaluation of Failure and Repair of the Jebba and the Shiroro Hydroelectric Power Stations

Nigeria power generation hovers around 7000 MW as at January 2019; this is far less than the national required electricity per capita value. Jebba Hydroelectric Power Station (JHEPS) and Shiroro Hydroelectric Power Station (SHEPS) are two of the three major hydropower stations in the country in which their maximum average generation values are still less than the rated values of 578.4 MW and 600 MW, respectively. This paper presented some basic concepts of analysis of failure and repair. An algorithm was set up to extract the contiguous set of uptimes and downtimes to determine time-between-failures (TBF) and time-between-repairs (TBR) events; these events were analyzed for the stochastic study. It was observed that the longest uptime was 262 days and seen in turbo-alternator unit five, while the shortest was zero day and seen in the entire units except that of unit two. For JHEPS, the longest downtime was 133 days and seen in the turbo-alternator of unit one, and the least time-between-repairs is a day and common to all the units. The frequency distributions for both TBF and TBR showed that 79% of TBF events and 57% of TBR events are distributed in the first two classes. For SHEPS, unit 3 was found to have the longest TBF with a value of 46 days, while the least was found in unit 1 with a value of 17 days.

Research on TBF distribution model of circular tool magazine and ATC

Tool magazine and automatic tool changer (ATC) is one of the most important function units of a machining centre and its reliability directly influences the reliability of the machining centre. Failure analysis is the basic work in reliability study. So far, there are few studies on failure of the tool magazine and ATC. Based on the failure data obtained from the field tracing of a kind of circular tool magazine and ATC, the probability density function, the distribution function and failure function of time between failures (TBF) of the tool magazine and ATC are calculated. It is preliminarily assumed that the TBF of the circular tool magazine and ATC obey Weibull distribution. Parameter estimation and hypothesis testing of the Weibull distribution model are done. Finally, the effectiveness of the model is verified. The work can provide a reference for failure prediction of circular tool magazine and ATC.

Automobile Maintenance Prediction Using Deep Learning with GIS Data

Predictive maintenance is of importance to various industries. Fleet management can be beneficial if the time-between-failures (TBF) of an automobile can be predicted. Conventionally, the prediction models in predictive maintenance are established using historical maintenance data or sensor data. In the era of big data, the availability of data has been significantly increased. This study aims to introduce geographic information systems data into TBF modelling and research their impact on automobile TBF using deep learning. An experimental study based on real-world maintenance data reveals that the performance of deep neural network improved with the help of GIS data.

Data-Driven Failure Characteristics and Reliability Analysis for Train Control On-Board Subsystem

The train control on-board subsystem (TC-OBS) plays an important role in the safety and efficiency of the high-speed train’s operation. Therefore, there is an urgent demand for the analysis of failure characteristics and the reliability of TC-OBS. In this paper, a specific data model is built for the TC-OBS operational and failure data based on data cubes. This model analyzed the failure distribution characteristics of TC-OBS from the combined angles of System Identification, Time and Operation Attribute through the operations of data cubes. Thus, the representative units and systems can be the research objects of the reliability evaluation. With these representative units and systems, this paper uses Bayesian estimation combined with Markov Chain Monte Carlo (MCMC) to estimate the parameters of the time between failures (TBF) distribution model and the reliability is analyzed. Simulation results show that the data model based on data cubes can offer an efficient and convenient method to analyze the failure characteristics and reliability of TC-OBS.

Análise de confiabilidade para formulação de estratégia de manutenção de equipamentos em uma empresa da indústria alimentícia

A função manutenção tem um importante papel na busca por vantagens competitivas pelas empresas. Nesse sentido, a análise de confiabilidade de equipamentos é parte fundamental para a definição e seleção de estratégias de manutenção mais adequadas. O objetivo deste trabalho é avaliar, por meio de análise de confiabilidade, a coerência da atual estratégia de manutenção de equipamentos de uma empresa da indústria alimentícia localizada no interior do Estado de São Paulo. O método de pesquisa utilizado foi a modelagem estatística aplicada ao tempo de reparo (TTR) e tempo entre falhas (TBF) de um conjunto de 6 máquinas de embalagem de uma das linhas de produção. Estes tempos foram modelados por distribuições de probabilidade Log-normal e Weibull. Os resultados apontam que a atual estratégia de manutenção preventiva adotada para os equipamentos não está adequada, pois os equipamentos se encontram na fase de mortalidade infantil do ciclo de sua vida útil, o que sugere o emprego da estratégia de manutenção corretiva com o objetivo de se identificar e eliminar as falhas de origem.

Penentuan Interval Waktu Pemeliharaan Pencegahan pada Peralatan Sub-Unit RKC 3 di PTX Pabrik Tuban

PT X Tuban Plant has four plants (unit), namely Tuban I, Tuban II, Tuban III and Tuban IV. Each unit plant has three sub units, i.e., Crusher Operations Sub-Unit, Raw Mill, Kiln and Coal Mill (RKC) Sub-Unit and Finish Mill Sub-Unit. RKC 3 Sub-Unit in Tuban III has the highest number of equipment downtime and production loss. Therefore, it was necessary to optimize the time interval of preventive maintenance ( ) and total labor force as part of the company maintenance policy, would also fulfill the required reliability and availability of RKC 3 Sub-Unit. There were two steps in determining Tp optimum. The first step was to obtain the best distribution of the time between failures (TBF) and time to repair (TTR). The next step was to iterate the operating time (Ti) and Tp to determine the minimum preventive maintenance cost rate, reliability and maintainability.This iteration was applied to sub-units of RKC 3 that possesses a series system. Tp at the lowest rate of maintenance costs was the optimum Tp. The optimum Tp for RKC 3 Sub-Unit is 3743,28 hour. The preventive maintenance cost rate for optimum Tp is Rp33.100/hour and the reliability and availability of sub unit are 96,7% and 99,86% respectively.Keywords: reliability, availability, preventive maintenance cost rate, and preventive maintenance

Maintenance strategy based on reliability analytical models for three parallel mechanical transformation machines

The purpose of this article is to formulate a maintenance strategy for three parallel machines in a mechanical manufacture. The research method was the quantitative modeling, based on analytical models. Time between failures (TBF) and time-to-repair (TTR) data since April 2014 to September 2015 were modelled for the three machines. Weibull distribution fitted all the TBF data. Lognormal distribution fitted TTR for machine 3. The availability Av(t) were calculated for each machine, remaining between 97% to 98%. Based on analytical models, it was defined that the best maintenance strategy for the system is the corrective maintenance.