Статті в журналах: "Preventive condition-Based maintenance"

1

Mann, Lawrence, Anuj Saxena, and Gerald M. Knapp. "Statistical‐based or condition‐based preventive maintenance?" Journal of Quality in Maintenance Engineering 1, no. 1 (March 1995): 46–59. http://dx.doi.org/10.1108/13552519510083156.

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2

Su, Hongsheng. "Preventive Maintenance Model Analysis Based on Condition." International Journal of Security and Its Applications 8, no. 4 (July 31, 2014): 353–66. http://dx.doi.org/10.14257/ijsia.2014.8.4.32.

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3

Neugebauer, R., J. Fischer, and M. Praedicow. "Condition-based preventive maintenance of main spindles." Production Engineering 5, no. 1 (September 24, 2010): 95–102. http://dx.doi.org/10.1007/s11740-010-0272-z.

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4

S. Su, H., and Y. Q. Kang. "CONDITION BASED PREVENTIVE MAINTENANCE CONTROL STRATEGY DESIGN." International Journal on Smart Sensing and Intelligent Systems 7, no. 3 (2014): 967–1003. http://dx.doi.org/10.21307/ijssis-2017-690.

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5

Liu, Yao Jun, Shi Zhong Ji, and Hua Pu Chen. "Development of Transmission Line Condition-Based Maintenance System Based on Expert System." Advanced Materials Research 988 (July 2014): 710–14. http://dx.doi.org/10.4028/www.scientific.net/amr.988.710.

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At the moment preventive maintenance is the main approach in maintaining power transmitting lines. While along with the improvement of equipment and management, the drawbacks of preventive maintenance appear more and more prominent. In the file the function and the structure of the system of status maintenance for power transmitting lines based on expert system is presented.

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6

Wei, Shuaichong, Mustapha Nourelfath, and Nabil Nahas. "Condition-based Maintenance Optimization of Degradable Systems." International Journal of Mathematical, Engineering and Management Sciences 7, no. 1 (January 22, 2022): 1–15. http://dx.doi.org/10.33889/ijmems.2022.7.1.001.

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This paper develops a mathematical model for condition-based maintenance optimization of multi-state systems. The majority of the existing literature on maintenance optimization assume that there is no additional cost incurred because of side effects of equipment degradation. Nevertheless, as the operating cost increases with equipment age and degradation, it is important to consider the degradation side effects in the maintenance decision-making process. An important feature of the proposed model lies in the fact that it incorporates side effect of degradation process into condition-based preventive maintenance optimization. We develop a continuous-time discrete-state Markov chain model describing the deterioration stochastic process of a single component. The component is modeled as a multi-state system, where each discrete state is characterized by a degradation level. Numerical examples show the importance of considering such side effect costs when optimizing the choice of maintenance policy. The proposed model is extended to deal with multi-state series systems. Using an example of a series system with two components, it is shown that preventive maintenance and side effect costs should not be optimized for each component individually, but from the perspective of the series system as a whole.

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7

Vale, Cecília, and Isabel M. Ribeiro. "RAILWAY CONDITION-BASED MAINTENANCE MODEL WITH STOCHASTIC DETERIORATION." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 20, no. 5 (October 10, 2014): 686–92. http://dx.doi.org/10.3846/13923730.2013.802711.

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The application of mathematical programming for scheduling preventive maintenance in railways is relatively new. This paper presents a stochastic mathematical model designed to optimize and to predict tamping operations in ballasted tracks as preventive condition-based maintenance. The model is formulated as a mixed 0–1 nonlinear program that considers real technical aspects as constraints: the reduction of the geometrical track quality over time is characterized by the deterioration rate of the standard deviation of the longitudinal level; the track layout; the dependency of the track recovery on its quality at the moment of the maintenance operation; the limits for preventive maintenance that depend on the maximum permissible train speed. In the model application, a railway stretch with 51.2 km of length is analysed for a time period of five years. The deterioration model is stochastic and represents the reduction of the standard deviation of the longitudinal level over time. The deterioration rate of the standard deviation of the longitudinal level is simulated by Monte Carlo techniques, considering the three parameters Dagum probabilistic distribution fitted with real data (Vale, Simões 2012). Two simulations are performed and compared: stochastic simulation in space; stochastic simulation in space and time. The proposed condition-based maintenance model is able to produce optimal schedules within appropriate computational times.

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8

Krit, Makram. "General Independent Competing Risks for Maintenance Analysis." Stochastics and Quality Control 37, no. 2 (November 25, 2022): 117–26. http://dx.doi.org/10.1515/eqc-2022-0029.

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Abstract Repairable systems are submitted to corrective maintenance and condition-based preventive maintenance actions. Condition-based preventive maintenance occurs at times which are determined according to the results of inspections and degradation or operation controls. The generalization of the models suggested makes it possible to integrate the dependence between corrective and preventive maintenances. In order to take into account this dependency and the possibility of imperfect maintenances, generalized competing risks models have been presented in Doyen and Gaudoin (2006). In this study, we revise the general case in which the potential times to next corrective and preventive maintenance are independent conditionally to the past of the maintenance process. We address the identifiability issue and we find a result similar to that of Zhou, Lu, Shi and Cheng (2018) for usual competing risks. We propose realistic models with exponential risks and derive their likelihood functions.

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9

Wang, Jingjing, Qingan Qiu, Huanhuan Wang, and Cong Lin. "Optimal condition-based preventive maintenance policy for balanced systems." Reliability Engineering & System Safety 211 (July 2021): 107606. http://dx.doi.org/10.1016/j.ress.2021.107606.

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10

Galamboš, Róbert, Jana Galambošová, Vladimír Rataj, and Miroslav Kavka. "Design of Condition-Based Decision Support System for Preventive Maintenance." Acta Technologica Agriculturae 20, no. 1 (March 1, 2017): 19–22. http://dx.doi.org/10.1515/ata-2017-0004.

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Abstract Presented paper deals with the topic of preventive maintenance. A decision support system was designed, incorporating historical as well as forecast information to calculate the time remaining to preventive maintenance. The designed system optimizes maintenance costs without any further investment and running costs. An algorithm of the designed system is introduced and a case study of its implementation is described in the paper.

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11

Shahraki Dehsoukhteh, Somayyeh, and Mostafa Razmkhah. "A Condition-based Maintenance Policy in Chance Space." Statistics, Optimization & Information Computing 12, no. 6 (July 26, 2024): 1622–39. http://dx.doi.org/10.19139/soic-2310-5070-2018.

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A condition-based maintenance policy is considered for a deteriorating system including both of preventive and corrective maintenance actions. The gamma process is used to model stochastic degradation in the probability space. Although, the cost of preventive maintenance is considered as an uncertain variable due to incomplete information, and its distribution is estimated based on the opinions of some experts using the Delphi method. The optimal policy is determined by minimizing the expected cost rate function. Since in this function, there are both random variables discussing in a probability space, and an uncertain variable, which is considered in an uncertain space, we have to study the optimal policy in a chance space which is a combination of probability and uncertain spaces. The proposed methodology is explained in an illustrative example. Finally, the results are applied to a real data set.

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12

Ge, En Shun, Qing Min Li, and Ao Lin Huang. "Optimization of Condition-Based Maintenance Policy for Deteriorating System Based on Monte-Carlo Simulation." Advanced Materials Research 544 (June 2012): 44–48. http://dx.doi.org/10.4028/www.scientific.net/amr.544.44.

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Condition-based Maintenance (CBM) can not only efficiently improve the performance of deteriorating system but also guarantee the system operation safety. This paper assumes that the system state is periodically inspected, and a preventive maintenance is performed if the degradation level exceeds a threshold. The effect of maintenance is imperfect, which means that maintenance can restore the system state to somewhere between as good as new and as bad as old. The algorithm is presented to get the solution of long run cost based on Monte-Carlo simulation, and the joint optimization of inspection rate, the threshold value and the number of preventive maintenance activities is investigated for the minimization of long run cost rate. A case study is given to show the procedure of the maintenance model and simulation. Therefore, the correctness and rationality of the model are proved.

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13

Ilyas Mas'udin, M. Eartono,. "Perencanaan Perawatan Mesin dengan Metode Markov Chain Guna Menurunkan Biaya Perawatan." Jurnal Teknik Industri 3, no. 2 (April 26, 2010): 173. http://dx.doi.org/10.22219/jtiumm.vol3.no2.173-184.

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Problem often faced by a fertilizer bagging machine unit is the inegularity of machinemaintenance in which the machine is operated continuously until it damages and can not beoperated any longer. It will result in relatively high down time cost.To solvet his problem we propose a machine maintenance planing based upon data acquired by using Markov Chain Method.There are four types of maintenance we propose they are:l. Maintenance planing with corrective maintenance or high level of damage condition and preventive maintenance or mediurn le vel ofdamage condition.2. Maintenancep laningw ith correctivem aintenancfeo r high and medium level of damageconditiona ndp reventivem aintenancfeo r low levelo fdamagec ondition.3. Maintenance planing with corrective maintenance or high level of damage condition andpreventive maintenance or medium and low level of damage condition4. Maintenance planing with corrective maintenance or high and medium level of damage condition.To determine the best proposal that will be selected we use analysis of preventive maintenance cost and corrective maintenance cost that will result in expected cost for eachmachine.From the four proposals one with the least expected cost is chosen that is planing with corrective maintenance for high level of damage condition and preventive maintenance formedium and low level ofdamage condition.

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14

Yang, S. K. "A condition-based preventive maintenance arrangement for thermal power plants." Electric Power Systems Research 72, no. 1 (November 2004): 49–62. http://dx.doi.org/10.1016/j.epsr.2004.03.007.

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15

Morelos, Joseph. "Lloyd's Register—Cyber CBM (Condition based maintenance)." APPEA Journal 57, no. 2 (2017): 623. http://dx.doi.org/10.1071/aj16223.

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Classification survey requirements are anchored on the concept of a periodic, time-based preventive maintenance regime. The underlying principle of the periodic maintenance philosophy is that parts and components of equipment, machinery, systems and structures eventually wear out, thus the safety and reliability of parts and components – indeed the entire vessel – are directly correlated with time and operational age. While development of techniques and technologies have continuously improved surveys and inspection, the periodic maintenance philosophy has been the de-facto standard in marine and offshore industries for many decades. This paper presents Lloyd Register’s Cyber condition based maintenance (CBM) technique: a scalable evaluation of components, equipment, machinery, systems and structures that detects their emergent faults, and characterises their progression to failure. This is an important step in the evolution of in-service classification:moving away from a periodic, time-based maintenance regime into an intelligent, real-time and predictive vessel health maintenance regime.

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16

Huang, Ao Lin, Qing Min Li, and Tie Bing Li. "Study on Information System with Condition-Based Maintenance Models for a Continuously Degrading System." Advanced Materials Research 703 (June 2013): 227–30. http://dx.doi.org/10.4028/www.scientific.net/amr.703.227.

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This paper considers on-condition maintenance of a continuously degrading information system, in which the inter-maintenance time and the maintenance time depend on the condition of the system at which maintenance is carried out. The durations of preventive maintenance activities are supposed to be exponentially distributed. Assuming deterioration of the system follows a gamma process, models are established to maximize the system average availability when the effectiveness of the preventive maintenance activity becomes weaker and weaker. Optimal solutions on the condition of the system at which maintenance should be performed and the number of times of maintenance action to be carried out are obtained based on Monte-Carlo simulation. A case study is given to show the procedure of the maintenance model and simulation.

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17

Raza, Ahmed, and Vladimir Ulansky. "Optimization of Condition Monitoring Decision Making by the Criterion of Minimum Entropy." Entropy 21, no. 12 (December 4, 2019): 1193. http://dx.doi.org/10.3390/e21121193.

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Condition-based maintenance (CBM) is a promising technique for a wide variety of deteriorating systems. Condition-based maintenance’s effectiveness largely depends on the quality of condition monitoring. The majority of CBM mathematical models consider perfect inspections, in which the system condition is assumed to be determined error-free. This article presents a mathematical model of CBM with imperfect condition monitoring conducted at discrete times. Mathematical expressions were derived for evaluating the probabilities of correct and incorrect decisions when monitoring the system condition at a scheduled time. Further, these probabilities were incorporated into the equation of the Shannon entropy. The problem of determining the optimal preventive maintenance threshold at each inspection time by the criterion of the minimum of Shannon entropy was formulated. For the first time, the article showed that Shannon’s entropy is a convex function of the preventive maintenance threshold for each moment of condition monitoring. It was also shown that the probabilities of correct and incorrect decisions depend on the time and parameters of the degradation model. Numerical calculations show that the proposed approach to determining the optimal preventive maintenance threshold can significantly reduce uncertainty when deciding on the condition of the monitoring object.

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18

Cheng, Guoqing, Binghai Zhou, Faqun Qi, and Ling Li. "Modeling condition-based maintenance and replacement strategies for an imperfect production-inventory system." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 232, no. 10 (November 14, 2016): 1858–71. http://dx.doi.org/10.1177/0954405416675653.

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In this article, we consider an imperfect production-inventory system which produces a single type of product to meet the constant demand. The system deteriorates stochastically with usage and the deterioration process is modeled by a non-stationary gamma process. The production process is imperfect which means that the system produces some non-conforming items and the product quality depends on the degradation level of the production system. To prevent the system from deteriorating worse and improve the product quality, preventive maintenance is performed when the level of the system degradation reaches a certain threshold. However, the preventive maintenance is imperfect which cannot restore the system as good as new. Hence, the aging system will be replaced by a new one after some production cycles. The preventive maintenance cost, the replacement cost, the production cost, the inventory holding cost and the penalty cost of lost sales are considered in this article. The objective is to minimize the total cost per unit item which depends on two decision variables: the preventive maintenance threshold and the time at which the system is replaced. We derive the explicit expression of the total cost per unit item and the optimal joint policy can be obtained numerically. An illustrative example and sensitivity analysis are given to demonstrate the proposed model.

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19

Tlili, L., M. Radhoui, and A. Chelbi. "Condition-Based Maintenance Strategy for Production Systems Generating Environmental Damage." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/494162.

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We consider production systems which generate damage to environment as they get older and degrade. The system is submitted to inspections to assess the generated environmental damage. The inspections can be periodic or nonperiodic. In case an inspection reveals that the environmental degradation level has exceeded the critical levelU, the system is considered in an advanced deterioration state and will have generated significant environmental damage. A corrective maintenance action is then performed to renew the system and clean the environment and a penalty has to be paid. In order to prevent such an undesirable situation, a lower threshold levelLis considered to trigger a preventive maintenance action to bring back the system to a state as good as new at a lower cost and without paying the penalty. Two inspection policies are considered (periodic and nonperiodic). For each one of them, a mathematical model and a numerical procedure are developed to determine simultaneously the preventive maintenance (PM) thresholdL∗and the inspection sequence which minimize the average long-run cost per time unit. Numerical calculations are performed to illustrate the proposed maintenance policies and highlight their main characteristics with respect to relevant input parameters.

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20

Minić, Svetomir, and Jovan Todorović. "Reliability level inspection technical systems preventive on-condition based maintenance model." Vojnotehnicki glasnik 44, no. 6 (1996): 143–48. http://dx.doi.org/10.5937/vojtehg9602143m.

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21

Yang, S. K. "A condition-based failure-prediction and processing-scheme for preventive maintenance." IEEE Transactions on Reliability 52, no. 3 (September 2003): 373–83. http://dx.doi.org/10.1109/tr.2003.816402.

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22

Freeman, Floris, Paul Van Kessel, and Wim Verhagen. "Age and Condition-Based Preventive Replacement Timing for Periodic Aircraft Maintenance Checks." PHM Society European Conference 6, no. 1 (June 29, 2021): 12. http://dx.doi.org/10.36001/phme.2021.v6i1.2865.

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By anticipating impending failures and addressing those with preventive replacements, Condition-Based Maintenance (CBM) can provide several economic benefits to aircraft operators. While Prognostics and Health Monitoring (PHM) methods are widely available, scheduling of tasks originating from those methods is a relatively new challenge. To avoid incurring extra cost and ground-time, these maintenance tasks are typically scheduled during already existing (conventional) maintenance slots such as periodic checks. Following this strategy, an aircraft component would have to be replaced if a failure precursor is detected that is expected to result in failure between the next two checks. The decision following from that detection depends on the chosen decision threshold of the prognostic or diagnostic model, which can be represented by a point on the Receiver Operating Characteristic curve. Selecting the optimal operating point for each maintenance check is challenging, as it depends on the age-dependent reliability of the component, the performance of the prognostic (diagnostic) model, the interval of the periodic check and the cost of (corrective and preventive) maintenance. This paper presents an innovative method for selecting optimal operating points for all periodic checks throughout the lifetime of an aircraft component. This is done by means of a numerical optimization model that finds operating points that minimize the component’s total maintenance cost per flight hour. A case study on a compressor of a wide-body aircraft is presented, which shows that by using this method, additional economic value from existing PHM can be realized without the need for additional investments.

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23

You, Ming-Yi. "A generalized three-type lifetime probabilistic models-based hybrid maintenance policy with a practical switcher for time-based preventive maintenance and condition-based maintenance." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 233, no. 6 (July 15, 2019): 1231–44. http://dx.doi.org/10.1177/0954408919862720.

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This paper proposes a generalized hybrid maintenance policy for maintenance scheduling with the help of both time-based maintenance and condition-based maintenance techniques, in which three-type product lifetime probabilistic models are utilized. A dispersion of lifetime estimates-based switcher is proposed to recommend the choice of time-based maintenance or condition-based predictive maintenance schedules in a real-time manner. Within the condition-based predictive maintenance policy, which is a part of the hybrid maintenance policy, a novel weighted average of the maintenance schedules is proposed to recommend maintenance acts, which are estimated based on two types of product lifetime probabilistic models namely type-II and type-III lifetime probabilistic models. The hybrid maintenance policy includes the classical time-based maintenance policy, the traditional condition-based predictive maintenance policy, and the proposed condition-based predictive maintenance policy as special cases. An extensive numerical investigation for the stochastic linear degradation model verifies the effectiveness of the proposed hybrid maintenance policy, highlighting the existence of a special space among time-based maintenance and condition-based predictive maintenance polices, which provides even better maintenance performance than a solely condition-based predictive maintenance policy.

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24

Raza, Ahmed, and Vladimir Ulansky. "Optimal Preventive Maintenance of Wind Turbine Components with Imperfect Continuous Condition Monitoring." Energies 12, no. 19 (October 8, 2019): 3801. http://dx.doi.org/10.3390/en12193801.

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Among the different maintenance techniques applied to wind turbine (WT) components, online condition monitoring is probably the most promising technique. The maintenance models based on online condition monitoring have been examined in many studies. However, no study has considered preventive maintenance models with incorporated probabilities of correct and incorrect decisions made during continuous condition monitoring. This article presents a mathematical model of preventive maintenance, with imperfect continuous condition monitoring of the WT components. For the first time, the article introduces generalized expressions for calculating the interval probabilities of false positive, true positive, false negative, and true negative when continuously monitoring the condition of a WT component. Mathematical equations that allow for calculating the expected cost of maintenance per unit of time and the average lifetime maintenance cost are derived for an arbitrary distribution of time to degradation failure. A numerical example of WT blades maintenance illustrates that preventive maintenance with online condition monitoring reduces the average lifetime maintenance cost by 11.8 times, as compared to corrective maintenance, and by at least 4.2 and 2.6 times, compared with predetermined preventive maintenance for low and high crack initiation rates, respectively.

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25

HUANGFU, Lanlan, Yifan ZHAO, and Hongsheng SU. "Preventive condition-based maintenance model of EMU components based on stochastic differential equation." Journal of Measurement Science and Instrumentation 13, no. 3 (2022): 345–51. http://dx.doi.org/10.62756/jmsi.1674-8042.2022038.

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26

Toftaker, Håkon, Espen Flo Bødal, and Iver Bakken Sperstad. "Joint optimization of preventive and condition-based maintenance for offshore wind farms." Journal of Physics: Conference Series 2362, no. 1 (November 1, 2022): 012041. http://dx.doi.org/10.1088/1742-6596/2362/1/012041.

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High costs of maintenance and lost production during downtime are a challenge to the offshore wind industry, and there is a great potential to improve cost efficiency by improved maintenance and control strategies utilizing condition monitoring information. As wind farms get older, there is also an increased need to find ways of extending the lifetime of wind turbines allowing continued operation. This may be obtained by de-rating strategies, meaning adjustments of the power production to reduce the fatigue loads on the turbines. This subsequently means wind farm operators are faced with a trade-off between maximizing power production while limiting the degradation of the turbines. To investigate the best trade-off, this paper presents an optimization framework that considers component condition and planned power production to find the best times to perform predetermined preventive and condition-based maintenance on an offshore wind farm. To solve the scheduling problem, it is formulated as a constrained integer linear program, maximizing the net income for the planning horizon. The proposed method considers logistic restrictions, wind and electricity price forecasts, control strategies, component condition and probability of failure. Moreover, the method uses a short time horizon (days) to utilise weather forecasts and a long time horizon (weeks) to better capture the impact of deteriorating condition. The model is presented in a general framework for accounting for component condition in offshore wind farm operation and maintenance. It is illustrated for a specific potential application, considering condition monitoring of main bearings and corrosion of structural elements as examples.

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Gebrehiwet, Lijalem, Yared Negussie, and Ephrem Damtew. "TECHNICAL AND ECONOMICAL BENEFITS OF AVIATION CBM IN MAINTENANCE, REPAIR & OVERHAUL (MRO) - REVIEW." International Journal Of Trendy Research In Engineering And Technology 09, no. 01 (2024): 43–55. https://doi.org/10.54473/ijtret.2024.9106.

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Maintenance along with its technical and management activities during repair periods has impact on a product or an asset’s economic factors. Maintenance plans have been divided into several categories, including condition based maintenance - CBM, preventative maintenance, and breakdown maintenance. While preventative maintenance frequently inspects a product at predetermined intervals to avoid abnormalities, breakdown maintenance is performed once certain issues, such as product breakdowns, are discovered. All these require higher cost values and passive in avoiding defects ahead. The contents of this paper are concerning CBM in technical systems. It has also been shown that the traditional maintenance techniques, corrective and preventive, are very costly. With CBM one utilizes predictive maintenance, i.e. perform maintenance based upon need. One assesses the condition of the assets and makes judgments whether or not maintenance should be performed. This indicates that the maintenance intervals will be decided dynamically. Vibrations, temperature, oil, acoustic analysis etc. are some of the basic parameters that will reveal the condition. There are also just a few areas in the industry where CBM (or possibly even just condition monitoring) have gained acceptance.

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ZHANG, Xiaohong. "Joint Optimization of Condition-based Preventive Maintenance and Spare Parts Provisioning Policy for Equipment Maintenance." Journal of Mechanical Engineering 51, no. 11 (2015): 150. http://dx.doi.org/10.3901/jme.2015.11.150.

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29

AHMADI, REZA. "A NEW APPROACH TO MODELING CONDITION-BASED MAINTENANCE FOR STOCHASTICALLY DETERIORATING SYSTEMS." International Journal of Reliability, Quality and Safety Engineering 21, no. 05 (September 18, 2014): 1450024. http://dx.doi.org/10.1142/s0218539314500247.

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This paper develops the degradation and maintenance modeling technique for repairable systems subject to deterioration due to aging and damage caused by operating environment factors. This provides a relaxed and generalized approach to modeling condition-based maintenance. The approach can deal with both the maintenance scheduling problem and failures preventing the system functioning further. Controlled by a virtual age process and a general class of process called Piecewise-Stochastic (Damage) process (PSP), the proportional intensity model (PIM) is used to describe the system state. The state of the system is monitored at periodic times and maintenance actions are carried out in response to the system state revealed at inspection times. Assuming a threshold-type policy, the approach aims at minimizing the long-run average cost per unit time subject to maintenance parameters; the inspection interval and preventive replacement threshold. To this end, given some assumptions, expressions for the expected cycle length and expected cost per cycle emerging as solutions of the Fredholm integral equations are obtained. The solution technique has been presented for the case when the effect of operating environment is modeled as a Gamma process.

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30

Seo, Min-Young, Se-Yun Hwang, Jang-Hyun Lee, Jae-Gon Kim, and Hong-Bae Jun. "A Study on PF–IFF-Based Diagnosis Model of Plant Equipment Failure." Applied Sciences 12, no. 1 (December 30, 2021): 347. http://dx.doi.org/10.3390/app12010347.

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There are two types of maintenance policies for equipment: breakdown maintenance and preventive maintenance. In the case of applying preventive maintenance, the maintenance is carried out based on time or the condition of the equipment. However, with the development of Information and Communications Technologies (ICT) and the Internet of Things (IoT) technology, the data collected from equipment has rapidly increased and the use of Condition-Based Maintenance (CBM) to perform appropriate maintenance based on the condition of the equipment is increasing. In this study, based on gathered sensor data, we introduce an approach to diagnosing the condition of the equipment by extracting specific data features related to the types of failures that occur with equipment. To this end, we used the K-means clustering method, support vector machine (SVM) classifier, and Pattern Frequency–Inverse Failure mode Frequency (PF–IFF) method with the Term Frequency–Inverse Document Frequency (TF–IDF) method. As a case study, we applied the proposed approach to a centrifugal pump and carried out computational experiments for assessing the performance and validity of the proposed approach.

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31

Suryadi, Muhammad, Fajar Aswin, and Sukanto Sukanto. "Perencanaan Preventive Maintenance Pada Bengkel Mekanik SMKN 2 Pangkalpinang." Jurnal Inovasi Teknologi Terapan 1, no. 2 (August 25, 2023): 405–12. http://dx.doi.org/10.33504/jitt.v1i2.11.

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Preventive maintenance is an activity to maintain the condition of the machine that is carried out in a planned/scheduled manner. Preventive maintenance itself includes scheduling, implementation, and improvement/modification. The purpose of this research is to carry out preventive maintenance on machine tools to maintain optimal engine conditions, ensure machine availability and reduce maintenance costs to a minimum. The preventive maintenance system design method is based on machine condition data collected by observing related machines and interviewing informants at SMKN 2 Pangkalpinang. Data from observations and interviews were used to calculate and determine the value of machine complexity so that a lathe maintenance system was obtained in the form of preventive maintenance design schedule data, annual preventive maintenance schedules, work specifications, and control cards. The results showed that the recommended preventive maintenance system design was acceptable to be implemented on lathes at SMKN 2 Pangkalpinang within 2 years. Further research is needed to increase the effectiveness of this preventive maintenance system. Some things that need to be further developed include a system for managing materials and machine parts inventory and changing engine oil using the oil listed in the lubrication table.

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32

Gunckel, Pablo Viveros, Fredy Kristjanpoller, and René Tapia Peñaloza. "A Generalized Chart-Based Decision-Making Tool for Optimal Preventive Maintenance Time under Perfect Renewal Process Modeling." Mathematical Problems in Engineering 2020 (September 24, 2020): 1–28. http://dx.doi.org/10.1155/2020/3078041.

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Анотація:

The most commonly used probabilistic model in reliability studies is the Perfect Renewal Process (PRP), which is characterized by the condition or type of maintenance represented: once the maintenance activities are executed, the equipment is restored to its original condition, leaving it “as good as new.” It is widely used since it represents an optimistic state when an item is replaced, assuming a perfect operational condition of the item after the maintenance. Some models have been developed for determining optimum preventive maintenance (PM) based on different criteria, and almost all aimed at PRP reliability modeling. The contribution of this paper is to analyze a model for determining the optimal preventive maintenance policy for a long time run under PRP and developing a general and chart-based tool for the problem, making it easier to solve the day-to-day practice and operation of equipment. As a result, a generalized chart was developed to support maintenance decisions through the elaboration of an original isometric table and complemented with a step-by-step methodology to determine the optimum time in which the preventive maintenance activities must be implemented. In most cases, these types of maintenance activities will consider a replacement activity.

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33

Huynh, Khac Tuan, and Antoine Grall. "A condition-based maintenance model with past-dependent imperfect preventive repairs for continuously deteriorating systems." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 234, no. 2 (November 25, 2019): 333–58. http://dx.doi.org/10.1177/1748006x19884210.

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Most condition-based imperfect maintenance models developed over the last few decades are memoryless in the sense that maintenance efficiency is completely [Formula: see text]-independent of previous interventions. However, many maintenance activities exhibit their past dependency in engineering practice, and this significant property should not be ignored in maintenance modeling. In this spirit, our aim is to develop a condition-based maintenance model for continuously deteriorating systems subject to a special kind of past-dependent imperfect repairs. Such a repair can put the system back to a deterioration level better than the one at just before the current repair, but worse than the one reached at the last repair. Besides, inspection and replacement are memoryless actions available for the system. They result in different effects on the system deterioration and incur different costs. To achieve high economic performances in the long term, these actions are coordinated into a control-limit deterioration-based maintenance policy. Its long-run maintenance cost rate is analytically evaluated using the semi-regenerative process theory. Numerous sensitivity studies to maintenance costs and to system characteristics give a thorough understanding about the policy behavior. Furthermore, comparisons with more classical policies justify the importance of incorporating the past dependency in maintenance modeling.

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34

Podtcykin, Nikolay S. "Mathematical model of condition-based preventive maintenance of a complex technical system." 47, no. 47 (September 28, 2020): 53–71. http://dx.doi.org/10.26565/2304-6201-2020-47-05.

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A new approach to mathematical modeling of complex technical systems according to their condition is being developed. Connections between subsystems of a complex system are considered to be arbitrary in terms of reliability. Due to wear, failures of subsystems can happen at random moments of time. Failures of some subsystems can lead to the entire system failure. The purpose of the simulation is to maintain the level of reliability and operability of a complex technical system at an optimal level for an unlimited time interval by means of regular preventive maintenance and repair. Technical instructions and specifications, as well as statistical data, are used in modeling a priori characteristics of subsystems. That information is used to determine the reliability of a complex system and its condition. The mathematical model is built in terms of the Markov decision-making process. The chosen optimization method allows obtaining the best policy for choosing acceptable preventive maintenance policy and repairs at the planned time of inspections and moments of failures.

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35

Xanthopoulos, A. S., D. E. Koulouriotis, and P. N. Botsaris. "Single-stage Kanban system with deterioration failures and condition-based preventive maintenance." Reliability Engineering & System Safety 142 (October 2015): 111–22. http://dx.doi.org/10.1016/j.ress.2015.05.008.

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36

Naga Srinivasa Rao, P., and V. N. Achutha Naikan. "An Algorithm for Simultaneous Optimization of Parameters of Condition-based Preventive Maintenance." Structural Health Monitoring: An International Journal 8, no. 1 (April 2008): 83–94. http://dx.doi.org/10.1177/1475921708094793.

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37

Van, Phuc Do, and Christophe Bérenguer. "Condition-Based Maintenance with Imperfect Preventive Repairs for a Deteriorating Production System." Quality and Reliability Engineering International 28, no. 6 (June 25, 2012): 624–33. http://dx.doi.org/10.1002/qre.1431.

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38

Yang, Baokui, Xiaosheng Si, Hong Pei, Jianxun Zhang, and Huiqing Li. "Joint Optimization Strategy of Condition-Based Maintenance and Spare Parts Ordering for Nonlinear Degraded Equipment under Imperfect Maintenance." Machines 10, no. 11 (November 8, 2022): 1041. http://dx.doi.org/10.3390/machines10111041.

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Анотація:

When existing methods consider the impact of the imperfect maintenance activities on random degraded equipment, they usually assume that the degraded equipment is linear, and set the number of imperfect maintenance activities in advance. However, in engineering practice, most of the degraded equipment is nonlinear, and the number of imperfect maintenance activities has an impact on the development of maintenance strategies. Therefore, this paper proposes a new joint maintenance strategy considering the uncertainty of the number of imperfect maintenance activities for nonlinear degraded equipment. First, nonlinear degradation data are linearized based on Box–Cox transformation (BCT), and the degradation model under the influence of imperfect activities is constructed by the random coefficient regression model. Accordingly, the remaining useful life (RUL) probability distribution can be derived. Secondly, the number of maintenances is calculated by imperfect maintenance level and success probability. Then, the detection cycle, preventive maintenance threshold and maintenance times are taken as decision variables. The optimization objective is to minimize the expected average cost. However, this is restricted by availability and probability of success for imperfect maintenance activities. A multi-objective joint optimization model of condition-based maintenance and spare parts ordering is constructed. Finally, the analysis results based on numerical examples verify the feasibility of the proposed joint optimization strategy.

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39

Goti, Aitor, Aitor Oyarbide-Zubillaga, Ana Sanchez, Tugce Akyazi, and Elisabete Alberdi. "Multi Equipment Condition Based Maintenance Optimization Using Multi-Objective Evolutionary Algorithms." Applied Sciences 9, no. 22 (November 13, 2019): 4849. http://dx.doi.org/10.3390/app9224849.

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Thanks to the digitalization of industry, maintenance is a trending topic. The amount of data available for analyses and optimizations in this field has increased considerably. In addition, there are more and more complex systems to maintain, and to keep all these devices in proper conditions, which requires maintenance management to gain efficiency and effectiveness. Within maintenance, Condition-Based Maintenance (CBM) programs can provide significant advantages, but often these programs are complex to manage and understand. The problem becomes more complex when equipment is analyzed in the context of a plant, where equipment can be more or less saturated, critical regarding quality, etc. Thus, this paper focuses on CBM optimization of a full industrial chain, with the objective of determining its optimal values of preventive intervention limits for equipment under economic criteria. It develops a mathematical plus discrete-event-simulation based model that takes the evolution in quality and production speed into consideration as well as condition based, corrective and preventive maintenance. The optimization process is performed using a Multi-Objective Evolutionary Algorithm. Both the model and the optimization approach are applied to an industrial case, where the data gathered by the IoT (Internet of Things) devices at edge level can detect when some premises of the CBM model are no longer valid and request a new simulation. The simulation performed in a centralized way can thus obtain new optimal values who fit better to the actual system than the existing ones. Finally, these new optimal values can be transferred to the model whenever it is necessary. The approach developed has raised the interest of a partner of the Deusto Digital Industry Chair.

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40

PAPIC, LJUBISA, JOSEPH ARONOV, and MILORAD PANTELIC. "SAFETY BASED MAINTENANCE CONCEPT." International Journal of Reliability, Quality and Safety Engineering 16, no. 06 (December 2009): 533–49. http://dx.doi.org/10.1142/s0218539309003563.

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Effective maintenance concept of the system has been developed and proposed on the basis of universal possible usages analysis of corrective, periodically preventive, predictive, proactive and safety based maintenance concepts in modern conditions. Starting from certain advantages and disadvantages of each concept, effective maintenance concept of the system was reached by balanced approach, i.e. their appropriate combination. However, it has been indicated that traditionally used systems reliability characteristics are not sufficient for complete description of their effectiveness. It was established as a reason that reliability characteristics do not indicate function disturbance level during system operation. It is suggested on new safety assess indicators adoption in system operation stage based on disturbance sequence modeling. It is very important that inappropriate maintenance concept must not put under question safety, i.e. cause system accident condition. Any accident condition would result with staff health and life threats and great economical losses through reengineering and system launch costs. Because of that safety based maintenance concept was consided in this work.

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41

Wang, Kui, Chao Deng, and Lili Ding. "Optimal Condition-Based Maintenance Strategy for Multi-Component Systems under Degradation Failures." Energies 13, no. 17 (August 22, 2020): 4346. http://dx.doi.org/10.3390/en13174346.

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Анотація:

This paper proposes a condition-based maintenance strategy for multi-component systems under degradation failures. The maintenance decision is based on the minimum long-run average cost rate (LACR) and the maximum residual useful lifetime (RUL), respectively. The aim of this paper is to determine the optimal monitoring interval and critical level for multi-component systems under different optimization objectives. A preventive maintenance (PM) is triggered when the degradation of component exceeds the corresponding critical level. Afterwards, the paper discusses the relationship between the critical level and the monitoring interval with regards to the LACR and RUL. Methods are also proposed to determine the optimal monitoring interval and the critical level under two decision models. Finally, the impact of maintenance decision variables on the LACR and RUL is discussed through a case study. A comparison with conventional maintenance policy shows an outstanding performance of the new model.

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42

Li, Hai Qing. "Research on Preventative Maintenance of Key Equipment in Discrete Manufacturing Enterprise MES." Advanced Materials Research 314-316 (August 2011): 2158–61. http://dx.doi.org/10.4028/www.scientific.net/amr.314-316.2158.

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This paper presents preventive maintenance based condition of key equipments in MES and management of job shop scheduling. A comprehensive mathematical model between multi-key-equipments preventive maintenance and job shop scheduling is offered. Assuming key equipment’s failure probability obeys the Weibull distribution and unifying the time estimation and preventive maintenance of the key equipments’ failure and decision–making of job shop scheduling, the goal of achieving multi-key-equipments’ max utilization rate is developed.

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43

Hagmark, Per-Erik. "On Three Competing Maintenance Actions and the Related Condition Control." International Journal of Quality, Statistics, and Reliability 2011 (July 27, 2011): 1–6. http://dx.doi.org/10.1155/2011/637079.

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This paper considers three competing maintenance actions: corrective after failure, condition-based preventive to avoid failure, and scheduled. A stochastic model is set for the time relationship between corrective and preventive maintenance. This model defines a version of the so-called random signs censoring, and it leads to natural goodness measures for condition control. The effect of condition control on important observable and unobservable indicators is studied. The latter half of the paper considers static Weibull models for the time to failure and the effect of condition control; the main contribution is a calculation method that derives the four model parameters from four figures of input data. Corresponding parameter experimentation is demonstrated on maintenance cost analysis and condition control design.

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44

Zhang, Dabo, Wenyuan Li, and Xiaofu Xiong. "Overhead Line Preventive Maintenance Strategy Based on Condition Monitoring and System Reliability Assessment." IEEE Transactions on Power Systems 29, no. 4 (July 2014): 1839–46. http://dx.doi.org/10.1109/tpwrs.2013.2295379.

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45

Su, Hongsheng. "Stochastic Model Analysis and Control Strategy Design on Preventive Maintenance Based on Condition." International Journal of Control and Automation 9, no. 3 (March 31, 2016): 194–214. http://dx.doi.org/10.14257/ijca.2016.9.3.19.

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46

Ge, En Shun, Qing Min Li, and Guang Yu Zhang. "Condition-Based Maintenance Policy under Imperfect Inspection Using Monte-Carlo Simulation." Applied Mechanics and Materials 201-202 (October 2012): 955–58. http://dx.doi.org/10.4028/www.scientific.net/amm.201-202.955.

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Анотація:

Condition-based Maintenance (CBM), which can efficiently improve the performance of the deteriorating system, would be influenced by imperfect inspection in practice. Aiming at this problem, a new CBM model under imperfect inspection is presented for deteriorating system, which described by Gamma process. The system is inspected periodically, and a preventive maintenance is performed if the degradation level exceeds a threhold. The inspection is imperfect, that means the measurements contain errors, and the CBM model should take these measure errors into account. The algorithm is shown to estimate the long run cost rate using Monte-Carlo method. Through numerical example, the influence of mesurement error over long run cost is analyzed. Therefore, the correctness and rationality of the model are proved.

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47

Mahmoudi, Morad, Abdellah El Barkany, and Ahmed El Khalfi. "Toward an Integrated Approach of HV and MV Circuit-Breakers Optimization Maintenance Planning and Reliability Assessment: A Case Study." International Journal of Engineering Research in Africa 29 (March 2017): 133–53. http://dx.doi.org/10.4028/www.scientific.net/jera.29.133.

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This paper investigates technical and organizational tools to improve maintenance planning performances. Indeed, maintaining a high level of reliability and availability of a Medium Voltage electrical network protection system such as the Medium Voltage and High Voltage circuit-breaker and its numerical protection relay at a low operating expenses cost is one of the most critical and challenging tasks for MV electrical distribution network operators. This work has mainly two goals. Firstly, to propose an operating expenses budget function that evaluates the Planned Scheduled Preventive Maintenance Policy combined with a Condition-based maintenance fora real series-parallel multi-assets MV electrical distribution system with active redundancy under the reliability and the maintenance frequency visits of these components. Secondly, to implement an integrated genetic algorithm approach in order to look for the optimal perfect and planned preventive maintenance scheduling policy and condition-based maintenance that minimizes the maximum operating expenses cost of the entire system.The method determines the optimal schedule of preventive maintenance actions based on minimization both reliabilty and operating expenses costs. Conclusions and recommendations for practice are made on the basis of obtained results.

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48

Bankole-Oye, T., I. El-Thalji, and J. Zec. "Combined principal component analysis and proportional hazard model for optimizing condition-based maintenance." IOP Conference Series: Materials Science and Engineering 1201, no. 1 (November 1, 2021): 012088. http://dx.doi.org/10.1088/1757-899x/1201/1/012088.

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Abstract Large companies are investing heavily in digitalization to be more competitive and economically viable. Hence, physical assets and maintenance operations have been digitally transformed to transmit a high volume of data, e.g., condition monitoring data. Such high-volume data can be useful to optimize maintenance operations and minimize maintenance and replacement costs. A tool to optimize maintenance using condition monitoring data is the Proportional hazard model (PHM). However, it is challenging to implement PHM for industrial complex systems that generate big data. Therefore, machine learning algorithms shall support PHM method to handle such a high volume of data. Thus, the purpose of this paper is to explore how to support PHM with Principal Component Analysis (PCA) to maintenance optimization of complex industrial systems. A case study of hydraulic power unit was purposefully selected to apply and validate the proposed analytical approach. The results show that PCA supported PHM optimizes and extends the preventive maintenance interval by 79.27% which might lead to maintenance cost reductions. This model enables PHM to handle complex systems where big data is collected.

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49

Golovan, A., I. Honcharuk, O. Deli, O. Kostenko, and Y. Nykyforov. "System of Water Vehicle Power Plant Remote Condition Monitoring." IOP Conference Series: Materials Science and Engineering 1199, no. 1 (November 1, 2021): 012049. http://dx.doi.org/10.1088/1757-899x/1199/1/012049.

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Abstract Remote condition monitoring of water vehicles plays an important role in preventing potentially very expensive marine incidents and ensuring maximum efficiency of a ship's operation and reliability with minimum maintenance downtime and repair costs. Concept of the condition-based approach to maintenance is today's best practise, and it is becoming increasingly important to move from planned maintenance to condition-based maintenance, to reduce the increasingly high cost of maintaining a modern fleet. Onboard and remote monitoring is now an essential part of condition-based maintenance process to obtain the good quality data, correct analysis, and effective counteractive actions necessary for such an approach, and article presents the water vehicle power plant monitoring model developed by authors. Considered approach, coupled with preventive maintenance, saves shipowners time and money through early diagnosis of component failure or excess wear. Power plant of water vehicle comprises far more than just an engine with its auxiliary equipment but also other main propulsion blocks – in particular, thrusters. The result was the development of the Water Vehicle Condition Monitoring (WVCM) system, which enables to closely examine water vehicle equipment performance. A WVCM system comprises the following installed onboard: accelerometers, pressure and temperature transmitters, oil, fuel and exhaust monitoring units and a torque measurement system.

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50

Doyen, Laurent, and Olivier Gaudoin. "Imperfect maintenance in a generalized competing risks framework." Journal of Applied Probability 43, no. 3 (September 2006): 825–39. http://dx.doi.org/10.1239/jap/1158784949.

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In this paper we present a general framework for the modelling of the process of corrective and condition-based preventive maintenance actions for complex repairable systems. A new class of models is proposed, the generalized virtual age models. On the one hand, these models generalize Kijima's virtual age models to the case where both preventive and corrective maintenances are present. On the other hand, they generalize the usual competing risks models to imperfect maintenance actions which do not renew the system. A generalized virtual age model is defined by both a sequence of effective ages which characterizes the effects of both types of maintenance according to a classical virtual age model, and a usual competing risks model which characterizes the dependency between the two types of maintenance. Several particular cases of the general model are derived.

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