Mohammad Saber Fallah Nezhad, Ali Mostafaeipour,
Volume 25, Issue 1 (2-2014)
Abstract
In order to perform Preventive Maintenance (PM), two approaches have evolved in the literature. The traditional approach is based on the use of statistical and reliability analysis of equipment failure. Under statistical-reliability (S-R)-based PM, the objective of achieving the minimum total cost is pursued by establishing fixed PM intervals, which are statistically optimal, at which to replace or overhaul equipments or components. The second approach involves the use of sensor-based monitoring of equipment condition in order to predict occurrence of machine failure. Under condition-based (C-B) PM, intervals between PM works are no longer fixed, but are performed only “when needed”. It is obvious that Condition Based Maintenance (CBM) needs an on-line inspection and monitoring system that causes CBM to be expensive. Whenever this cost is infeasible, we can develop other methods to improve the performance of traditional (S-R)-based PM method. In this research, the concept of Bayesian inference was used. The time between machine failures was observed, and with combining Bayesian Inference with (S-R)-based PM, it is tried to determine the optimal checkpoints. Therefore, this approach will be effective when it is combined with traditional (S-R)-based PM, even if large number of data is gathered.
Maghsoud Amiri, Mohammadreza Sadeghi, Ali Khatami Firoozabadi, Fattah Mikaeili ,
Volume 25, Issue 1 (2-2014)
Abstract
The main goal in this paper is to propose an optimization model for determining the structure of a series-parallel system. Regarding the previous studies in series-parallel systems, the main contribution of this study is to expand the redundancy allocation parallel to systems that have repairable components. The considered optimization model has two objectives: maximizing the system mean time to first failure and minimizing the total cost of the system. The main constraints of the model are: maximum number of the components in the system, maximum and minimum number of components in each subsystem and total weight of the system. After establishing the optimization model, a multi objective approach of Imperialist Competitive Algorithm is proposed to solve the model.
Mahdi Karbasian, Batool Mohebi, Bijan Khayambashi, Mohsen Chesh Berah, Mehdi Moradi,
Volume 26, Issue 4 (11-2015)
Abstract
The present paper aims to investigate the effects of modularity and the layout of subsystems and parts of a complex system on its maintainability. For this purpose, four objective functions have been considered simultaneously: I) maximizing the level of accordance between system design and optimum modularity design,II) maximizing the level of accessibility and the maintenance space required,III) maximizing the providing of distance requirement and IV) minimizing the layout space. The first objective function has been put forward for the first time in the present paper and in it, the optimum system modularity design was determined using the Design Structure Matrix (DSM) technique.The second objective function is combined with the concept of Level of Repair Analysis (LoRA) and developed. Simultaneous optimization of the above-mentioned objective functions has not been considered in previous studies. The multi objective problem which has been put forward was applied on a laser range finder containing 17 subsystems and the modularity and optimum layout was determined using a multi objective particle swarm optimization (MOPSO) algorithm.
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Ali Salmasnia, Ebrahim Ghasemi, Hadi Mokhtari,
Volume 27, Issue 4 (12-2016)
Abstract
This study aims to select optimal maintenance strategy for components of an electric motor of the National Iranian Oil Refining and Distribution Company. In this regard, a method based on revised multi choice goal programming and analytic hierarchy process (AHP) is presented. Since improving the equipment reliability is an important issue, reliability centered maintenance (RCM) strategies are introduced in this paper. Furthermore, on one hand, we know that maintenance cost consists of a considerable percentage of production cost; on the other hand, the risk of equipment failure is a main factor on personnel’s safety. Consequently, the cost and risk factors are selected as important criteria of maintenance strategies.
Mahdi Karbasian, Ali Eghbali Babadi, Fatemeh Hasani,
Volume 28, Issue 2 (6-2017)
Abstract
Abstract
The reliability and safety of any system is the most important qualitative characteristic of a system. This qualitative characteristic is of particular importance in systems whose functions are under various stresses, such as high temperature, high speed, high pressure, etc. A considerable point, which is rarely taken into account when calculating the reliability and safety of systems, is the presence of dependency among subsystems, and this dependency causes various failures in a system, one of the most important of which is the common cause failure (CCF). Failing to consider common cause failures in the calculation of system reliabilities, leads to optimistic estimations of system reliability rates, which results in too much trust in the system. In this paper, first we deal with identifying the reliability of the input of a dynamic positioning system consisting of different environmental sensors and various positioning systems with the aid of PBS and FFBD techniques. Then, we will calculate and allocate the above-mentioned reliability with the aid of a RBD. The common cause failures of different subsystems were considered in calculating the reliability of the previously mentioned system, with the aid of IEC 61508 standard, and then the degree of the effectiveness of common cause failures on the reliability of the studied system, was obtained. Finally, by considering different assumptions for the system under study, it was proved that the less the amount of the reliability of dependent components is, the higher the effectiveness of common cause failures on the system reliability will be
Hiwa Farughi, Ahmad Hakimi, Reza Kamranrad,
Volume 29, Issue 1 (3-2018)
Abstract
In this paper, one of the most important criterion in public services quality named availability is evaluated by using artificial neural network (ANN). In addition, the availability values are predicted for future periods by using exponential weighted moving average (EWMA) scheme and some time series models (TSM) including autoregressive (AR), moving average (MA) and autoregressive moving average (ARMA). Results based on comparative studies between four methods based on ANN and by considering the several conditions for the effective parameters in ANN show that, the generalized regression method is the best method for predicting the availability. Furthermore, results of the EWMA and three mentioned TSM are also show the better performance of MA model for predicting the availability values in future periods.
Mahdi Karbasian, Maryam Mohammadi, Mohammad Mortazavi,
Volume 29, Issue 2 (6-2018)
Abstract
Reliability allocation has an essential connection to design for reliability and is an important activity in the product design and development process. In determining the reliability of subsystems or components on the basis of goal reliability, attention must be paid to failure effect, failure information, and improvement opportunities based upon real potentials for reliability improvement. In the light of the fact that ignoring dependent failures inflicts irreversible damage on systems, and that redundant systems are vulnerable to Common Cause Failure (CCF) as well as independent failure, attention must be paid not only to components’ independent failure information, but also to CCF information in conducting reliability allocation for such systems. To consider improved failure rate alone cannot ensure the achievement of the goal reliability in question, because if the CCF occurrence exceeds a certain limit, the system’s reliability will certainly fail to match the goal reliability. This paper is an attempt to develop a method for reliability allocation of series-parallel systems by considering CCF, in such a way that potentials and priorities of reliability improvement are taken into consideration. The proposed method consists of four stages: 1) adding a series component to the redundant system in order to investigate CCF, 2) conducting reliability allocation for series components and the redundant system, 3) conducting reliability allocation for redundant system components, and 4) analyzing the failure rate of system components. The proposed method is run for water pumping systems and the results are evaluated. In this method, in addition to the improved failure rate of system components, the improved rate of CCF is computed, too. This proves instrumental and crucial for system designers in feasibility studies and conceptual design.
Ali Vaysi, Abbas Rohani, Mohammad Tabasizadeh, Rasool Khodabakhshian, Farhad Kolahan,
Volume 29, Issue 3 (9-2018)
Abstract
Nowadays, the CNC machining industry uses FMEA approach to improve performance, reduce component failure, and downtime of the machines. FMEA method is one of the most useful approach for the maintenance scheduling and consequently improvement of the reliability. This paper presents an approach to prioritize and assessment the failures of electrical and control components of CNC lathe machine. In this method, the electrical and control components were analyzed independently for every failure mode according to RPN. The results showed that the conventional method by means of a weighted average, generates different RPN values for the subsystems subjected to the study. The best result for Fuzzy FMEA obtained for the 10-scale and centroid defuzzification method. The Fuzzy FMEA sensitivity analysis showed that the subsystem risk level is dependent on O, S, and D indices, respectively. The result of the risk clustering showed that the failure modes can be clustered into three risk groups and a similar maintenance policy can be adopted for all failure modes placed in a cluster. Also, The prioritization of risks could also help the maintenance team to choose corrective actions consciously. In conclusion, the Fuzzy FMEA method was found to be suitably adopted in the CNC machining industry. Finally, this method helped to increase the level of confidence on CNC lathe machine.
Sareh Goli, Mohammadali Asadi,
Volume 30, Issue 2 (6-2019)
Abstract
In the study of the reliability of systems in reliability engineering, it has been defined several measures in the reliability and survival analysis literature. The reliability function, the mean residual lifetime and the hazard rate are helpful tools to analyze the maintenance policies and burn-in. In this paper, we consider a network consisting of n components having the property that the network has two states up and down (connected and disconnected). Suppose that the network is subject to shocks that each may cause the component failures. We further suppose that the number of failures at each shock follows a truncated binomial distribution and the process of shocks is nonhomogeneous Poisson process. This paper investigates the reliability function, the mean residual lifetime and the hazard rate of the network under shock model. An example and illustrative graph is also provided.
Rezvan Rezaei, Gholam Hossein Yari, Zahra Karimi Ezmareh,
Volume 31, Issue 3 (9-2020)
Abstract
In this paper, a new five-parameter distribution is proposed that is called MarshallOlkin Gompertz Makeham distribution(MOGM). This new model is applicable in analysis lifetime data, engineering and actuarial. In this research, some properties of the new model such as mode, moment, Reyni entropy, Tsallis entropy, quantile function and the hazard rate function which is decreasing and unimodal, are studied. The unknown parameters of the MOGM distribution are estimated using the maximum likelihood and Bayes methods. Then these methods are compared using Monte Carlo simulation and the best estimator is proposed. Finally, applications of the proposed model are illustrated to show its usefulness.
Hasan Rasay, Amir-Mohammad Golmohammadi,
Volume 32, Issue 2 (6-2021)
Abstract
The subjects of reliability acceptance sampling plans and failure-censored life tests have usually been investigated from the viewpoint of statistical properties; indeed, few researchers have shed light on the economic aspects of these issues. In this research, a constrained mathematical model is developed to optimally design a reliability sampling plan under failure censoring life testing. Minimizing the expected total cost (ETC) involved in the sampling and life testing is considered as the objective function of the model. Ensuring the producer’s and the consumer’s risks is taken into consideration as the constraint of the model. To minimize the ETC, the model optimally determines three decision variables including the total number of the items put to the life test, the number of the failed items to terminate the test, and a criterion to make decisions about the acceptance or rejection of the lot. Examples are provided and analyses are conducted to gain some insight regarding the model performance.
Badr Dakkak, El Hassan Irhirane, Ahmed Bounit,
Volume 32, Issue 4 (12-2021)
Abstract
Overall Equipment Effectiveness (OEE) is a very powerful indicator for the performance evaluation of a manufacturing organization. However, determining the OEE target remains subjective and it’s usually based on the decision of concerned managers. In this paper, we tested the OEE target determination model based on the measured OEE. Such a method is based on the fuzzy logic principle and on two other decision-making support methods. To do this, we began with a literature review on OEE and its constituents. Then, a detailed description of the research methodology and the proposed model is provided. Thus, a case study in a manufacturing agri-food organization was conducted to test the proposed model and validate the obtained results.
Mehrnaz Piroozbakht, Sedigh Raissi, Meysam Rafei, Shahrooz Bamdad,
Volume 33, Issue 2 (6-2022)
Abstract
In a system, prediction of remaining useful lifetime (RUL) of servicing before reaching to a specified breakdown threshold is a very important practical issue, and research in this field is still regarded as an appreciated research gap. Operational environment of an equipment is not constant and changes regarding to stresses and shocks. These random environmental factors accelerate system deterioration by affecting on the level or rate of degradation path. The present study focuses on providing a practical operational guideline to estimate the RUL of a system with general degradation path after receiving a shock which only affects on the degradation level. Due to exact estimation of the shock arrival times and measuring the magnitudes of future shocks to investigate shock effects on RUL is almost impossible in the real world and laborious in practice, in this research a new procedure based on total defect size monitored in the constant inspection periods and Accelerated Factor (AF) is proposed to analyze RUL of the system. A Micro-Electro-Mechanical system (MEMS) is used as an example and the results show the applicability of the proposed approach.
Hasan Rasay, Mohammad Saber Fallahnezahd, Shakiba Bazeli,
Volume 33, Issue 4 (12-2022)
Abstract
Condition-based maintenance (CBM) is a well-known maintenance cost minimization strategy in which maintenance activities are performed based on the actual state of the system being maintained. The act of combining maintenance activities for different components is called opportunistic maintenance or maintenance clustering, which is known to be cost-effective, especially for multi-component systems with economic dependency. Every operating system is subject to gradual degradation which ultimately leads to system failure. Since each level of degradation can be represented by a state, every system can be modeled as a multi-state structure. The state of a system can be estimated through condition monitoring, albeit with uncertainty. The majority of studies in the field of maintenance planning are focused on preventive perfect maintenance operations such as replacement. But in practice, most of the maintenance operations are imperfect because of time, technology, and resource limitations. In this paper, we present a CBM clustering model that factors in uncertainty in alerting and lifetime distribution and considers the possibility of using the imperfect maintenance approach. This model is developed for a system with three levels of warning (Signal, Alert, Alarm), which combines inspections and condition monitoring to avoid unnecessary inspections and thereby achieve better cost-efficiency. Our analysis and results provide a general view of when and how to cluster maintenance activities to minimize maintenance costs and maximize system availability. Numerical investigations performed with MATLAB show that clustering CBM activities can result in as much as 80% cost saving compared to No clustering.
Fakhri Ikhwanul Alifin, Bermawi Priyatna Iskandar, Nadia Fasa, Fransisca Debora,
Volume 35, Issue 2 (6-2024)
Abstract
This study develops warranty cost models for repairable products subject to Lemon Laws, encompassing Critical and Non-Critical components forming a multi-component system. Failures can arise naturally or be induced by other components (i.e., failure interaction), defining a lemon if recurrent failures reach a threshold (k) during the warranty period. A lemon declaration triggers a refund or replacement by the manufacturer. Four warranty cost models are proposed from the manufacturer's standpoint, considering failure mechanisms. Increasing failure thresholds in the warranty scheme substantially decreases warranty cost rates. For instance, a threshold (k) of 5 in refund and replacement schemes yields the lowest cost rates of 33.7159 and 25.8249, respectively. Failure interactions escalate total warranty costs; for instance, in a refund scheme (k = 5), costs with failure interaction reach 31.0169 compared to 28.7603 without. Similar trends apply to replacement schemes. Moreover, a lower warranty cost rate will extend the period, indicating regulation fulfillment due to a closer warranty period to the Lemon period. Sensitivity analysis also underscores the role of higher reliability in reducing warranty costs and complying with Lemon Laws. Finally, maintenance strategies and product reliability are emphasized to fulfill Lemon Laws with minimal costs, i.e., fewer warranty claims.
Khamiss Cheikh, El Mostapha Boudi, Hamza Mokhliss, Rabi Rabi,
Volume 35, Issue 3 (9-2024)
Abstract
Maintenance plan efficacy traditionally prioritizes long-term predicted maintenance cost rates, emphasizing performance-centric approaches. However, such criteria often neglect the fluctuation in maintenance costs over renewal cycles, posing challenges from a risk management perspective. This study challenges conventional solutions by integrating both performance and robustness considerations to offer more suitable maintenance options.
The study evaluates two representative maintenance approaches: a block replacement strategy and a periodic inspection and replacement strategy. It introduces novel metrics to assess these approaches, including long-term expected maintenance cost rate as a performance metric and variance of maintenance cost per renewal cycle as a robustness metric.
Mathematical models based on the homogeneous Gamma degradation process and probability theory are employed to quantify these strategies. Comparative analysis reveals that while higher-performing strategies may demonstrate cost efficiency over the long term, they also entail greater risk due to potential cost variability across renewal cycles.
The study underscores the necessity for a comprehensive evaluation that balances performance and resilience in maintenance decision-making. By leveraging the Monte Carlo Method, this research offers a critical appraisal of maintenance strategies, aiming to enhance decision-making frameworks with insights that integrate performance and robustness considerations.
