Maintenance modelling of shipboard machinery by delay time analysis

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This paper attempts to develop a numerical maintenance model to determine the optimal inspection regime to be followed for shipboard machinery. The maintenance modelling is carried out by Delay Time Analysis using a down time estimation model.

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Nội dung Text: Maintenance modelling of shipboard machinery by delay time analysis

International Journal of Management (IJM) Volume 8, Issue 4, July– August 2017, pp.16–22, Article ID: IJM_08_04_003 Available online at http://www.iaeme.com/ijm/issues.asp?JType=IJM&VType=8&IType=4 Journal Impact Factor (2016): 8.1920 (Calculated by GISI) www.jifactor.com ISSN Print: 0976-6502 and ISSN Online: 0976-6510 © IAEME Publication MAINTENANCE MODELLING OF SHIPBOARD MACHINERY BY DELAY TIME ANALYSIS Rahul Ramachandran KunjaliMarakkar School of Marine Engineering, Cochin University of Science and Technology, Cochin, India Dr. K. Sunil Kumar Model Engineering College, Institute of Human Resource Development, Cochin, India Roy V. Paul KunjaliMarakkar School of Marine Engineering, Cochin University of Science and Technology, Cochin, India ABSTRACT Shipping Companies are looking for an optimised Maintenance Model to be incorporated into the Safety Management System of the Ships operated by them. Coming into terms with what the best maintenance practise for each of the shipboard machinery is the need of the hour. Optimisation of Maintenance actions will enhance the safety and reliability of the machinery. It is believed that both over maintenance and under maintenance are to be avoided to enhance the efficiency of the concerned machinery. This paper attempts to develop a numerical maintenance model to determine the optimal inspection regime to be followed for shipboard machinery. The maintenance modelling is carried out by Delay Time Analysis using a down time estimation model. The model is validated using operational data, original equipment manufacturer recommendations and historical failure data collected from Wartsila A6L20C auxpac system. Key words: Maintenance Models For Engines In Ships; Safety Management System; Delay Time Analysis Cite this Article: Rahul Ramachandran, Dr. K. Sunil Kumar and Roy V. Paul, Maintenance Modelling of Shipboard Machinery by Delay Time Analysis. International Journal of Management, 8 (4), 2017, pp. 16–22. http://www.iaeme.com/ijm/issues.asp?JType=IJM&VType=8&IType=4 http://www.iaeme.com/IJM/index.as 16 editor@iaeme.com
Rahul Ramachandran, Dr. K. Sunil Kumar and Roy V. Paul 1. INTRODUCTION In the marine shipping industry, maintenance planning is very significant due to its complexity and the obligations on shipping organisations to comply with certain regulations and requirements. Moreover, improper planning can reduce the ship‘s availability, which may in turn, be reflected in the revenue of the company. Every hour of intermission brings high expenses to the ship-owner and the maintenance expert’s task is to do their best to avoid the unplanned intermission or to reduce its duration (Buksa, et al., 2005). In shipping industry, most of the maintenance actions are performed based on the operation manual provided by the original equipment manufacturer (OEM). However studies have revealed that maintenance activities should be based on the current state of the machine because each machine may operate in a different environment and failure of the machine (due to component failure) may not have similar occurrence as OEM predicted. Inspections can be carried out on the machinery at any interval of time which will help identify the faults that have cropped up during operation of the machinery. Each inspection carried out on the machinery is associated with a certain amount of down time. Carrying out inspections at very small intervals of time is considered to be a case of over maintenance which is not a favourable situation both in terms of the time spent for inspection and also the cost incurred as part of the inspection process. Similarly, when machinery inspections are scheduled at very lengthy intervals; faults may arise which may lead to a machinery failure prior to the inspection being carried out thereby reducing the effectiveness of the inspection carried out. Alhouli, 2011 in his PhD thesis has developed a new methodology to measure the maintenance performance in marine shipping organisations using Ship Maintenance Performance Measurement (SMPM) Framework. Pillay et al. (2001) studied the maintenance of fishing vessels ‘equipment by using time-delay analysis. In the study, a model was proposed to optimise the inspection period of the vessels ‘equipment. The IMO ISM Code states that “development, implementation and maintenance of all instructions and procedures to ensure safe operation of the ship and protection of the environment in compliance with relevant international and Flag state legislation shall be a part of the ship’s safety management system (SMS)” (ISM Code Section 1.4). Furthermore, it states that the ship owner is responsible for “establishing procedures to ensure that the ship is maintained in conformity of the provisions of the relevant rules and regulations and with any additional requirements which may be established by the company” (ISM Code Section 10). The Significance of Planning and control of maintenance systems including the role of modelling and validation has been recognised by several workers (White. 1973; White 1975 and Duffuaa, et al., 1999). Lee, 2013; in his thesis demonstrates the application of predictive analytics to ship machinery maintenance to aid in the reduction of operational downtime and increase the overall effectiveness of a ship maintenance programme. This paper aims to develop a framework that can help the decision maker to identify and choose optimum decisions regarding ship maintenance. The idea is to construct an optimised maintenance model for the routine maintenance of the ship under study, by using a Delay time estimation model which will help determine the optimal time/interval to carry out inspections on the machinery under study with a view to maximise the ship‘s availability within the company fleet. The model is to be defined by the down time that is associated with a failure mode. http://www.iaeme.com/IJM/index.as 17 editor@iaeme.com
Maintenance Modelling of Shipboard Machinery by Delay Time Analysis 2. MODEL FORMULATION Figure 1 Breakdown and Inspection Maintenance As can be seen from figure 1, a fault arising within a time period (0,t) is associated with a delay time z, the probability of occurrence of the event being ∆ . is the probability density function of the delay time z. A fault arising within the time duration (0, t-z) will undergo a breakdown maintenance whereas a fault arising within the time duration (t-z, t) will undergo an inspection maintenance. Summing up all possible values of z, the probability of a breakdown defect occurring, b(t) can be expressed as follows; Eqn 1 Assuming that the inspections are carried out at fixed intervals of t hours and the duration of each inspection is constant. That implies; Eqn 2 1 It is to be noted here that the probability of a breakdown defect occurring is independent of the arrival rate of the defect per unit time but dependant on the delay time. The delay time can be estimated only when a fault has occurred and it has led to a breakdown failure. Hence if a breakdown failure can exist when a fault has arisen, then it can be said that the probability of the failure being a breakdown failure, b (t) is a conditional probability (excluding the case of a sudden failure without any delay time). 2.1. Assumptions made in downtime model formation • A fault arising within (0,t) has a delay time z • Probability of occurrence of this event is f(z)dz • f(z) is the pdf of z • Inspections are carried out at fixed intervals of t hours. • The duration of each inspection is constant. • System downtime during inspection is j hours • Downtime for carrying out breakdown maintenance is J hours • Inspection period is of t hours http://www.iaeme.com/IJM/index.as 18 editor@iaeme.com
Rahul Ramachandran, Dr. K. Sunil Kumar and Roy V. Paul • Q is the frequency of arrival of defects • Machinery faults are assumed to be repaired immediately; J
Maintenance Modelling of Shipboard Machinery by Delay Time Analysis That implies; Eqn 7 ≥0 From Eqn. 7 it is very evident that there exists a positive chance for the observation depicted in Eqn6 being a negative value. This is not considered to be a favourable observation. Hence it is assumed that the probability density function of delay time follows a truncated standard normal distribution, truncated at 0 with mean of the delay times, 8 0 and standard deviation of the delay time, 9 : 1 Thus the probability density function of delay time is as follows; Eqn 8 2 2 36 6 √20 Substituting the value of probability density function obtained from Eqn. 8 into the equation for the expected down time per unit time, Eqn. 5; That implies; Eqn 9 436 , : (+)∗ ∗{ - 6 }* √:= [ ] +( Eqn. 9 depicts the estimated down time per unit time of the equipment. This is the final form of the down time model that we have formulated. 2.2. Model Validation As an example, the maintenance requirements of fuel valves used in Wartsila A6L20C are considered. It should be noted here that the following information was gathered from logged historical records, real time operating data and are complimented by expert judgements where data was not readily available. The down time due to inspection, j = 30 minutes = 0.5 hours Down time for breakdown maintenance, J = 12 hours Arrival rate of defects, Q = 0.00048 per hour From Eqn. 9, 436 , : (+)∗ ∗{ - 6 }* √:= [ ] +( Substituting the values of j, J and Q into Eqn9, Eqn. 10, 436 , : 0.5 + 0.00048 ∗ ∗ { - B := 6 C ∗ 12 √ [ ] + 0.5 http://www.iaeme.com/IJM/index.as 20 editor@iaeme.com
Rahul Ramachandran, Dr. K. Sunil Kumar and Roy V. Paul That implies, Eqn. 11 436 0.5 + 0.00046 ∗ {- 6 } [ ] + 0.5 That implies, Eqn. 12 F G 4H6 0.5 + 0.00046 ∗ E 2 J+ I + 0.5 When the above equation is solved using Matlab, the output obtained will be of the form shown in Figure 2. Figure 2 Sample D (t) – t plot From the graphical plot obtained, the optimal inspection period, t can be determined by choosing a point on the graph where the down time is minimised. 3. FUTURE WORK The numerical down time model that has been formulated is a model purely based on minimisation of the down time of the machinery. In order to further iterate the numerical model used in the maintenance modelling by delay time analysis of shipboard machinery for optimisation of the inspection regime the cost consideration of the spare parts, consumables, cost of repair etc. can be considered. The formation of a cost model can be carried out similar to the down time model that has been formed. Further dimensions like reliability, safety etc. can be considered in model formulation for the delay time analysis. It has to be understood that with the addition of an additional dimension to the delay time analysis, the accuracy of the result will be enhanced. Also the effectiveness of the effectiveness of the delay time analysis method mentioned here can be further improved if sufficient data is available in order to generate a more apt distribution for the probability density function of delay time, f(z). http://www.iaeme.com/IJM/index.as 21 editor@iaeme.com
Maintenance Modelling of Shipboard Machinery by Delay Time Analysis CONCLUSION Though the determination of optimal inspection regime by numerical analysis is complex, it can be incorporated into a computer interface which would require the machinery operators to input the necessary data regarding equipment failure. The maintenance model can be easily synced with the existing planned maintenance system described in the Safety Management System manual of the respective ship. Another advantage that the maintenance modelling approach mentioned here has is that it does not require any expensive condition monitoring equipment to be installed. Thus it will in all sense be a cost effective means for maintenance modelling of the machinery under study. The method mentioned in the study for the Maintenance modelling of shipboard machinery by delay time analysis using a downtime model is thus proved to be an effective and efficient means to maximise the ship’s operational readiness. Also it is capable of optimising the expenditure incurred during and as a result of maintenance. REFERENCES [1] Alhouli Yusuf. 2011. Development of Ship Maintenance Performance Measurement Frame Work to Assess the Decision Making Process to Optimise the Ship Maintenance Planning, PhD Thesis, University of Manchester, UK. 225 p. [2] Bukša, Ante; Tudor, Mato; Martinović, Dragan, 2005. Research of the Failure Incidences in the Diesel-engine Propulsion System, In Zanne, Marina ; Fabjan, Daša ; Jenček, Peter, editor(s), Proceedings of the 9th International Conference on Traffic Science (ICTS 2005) Transportation in science and practice, Portorož : Fakultetazapomorstvo in promet, Portorož, Slovenija, Retrieved on 10-2-2016, http://bib.irb.hr/prikazi- rad?&lang=en3&rad=291496 [3] Duffuaa, S., et al., 1999. Planning and control of maintenance systems: modeling and analysis. John Wiley & Sons Inc., New York. [4] IMO ISM Code Section 1.4 [5] IMO ISM Code Section 10 [6] Lee, G.H. 2013. A Study on Predictive Analytic Application to Ship Machinery Maintenance, M.S, Thesis, Naval Post Graduate School, Monterey, California. [7] Lee. 1999. Applications of delay time theory to maintenance practice of complex plant. PhD work. T.I.M.E. Research Institute. University of Salford, Salford, UK. [8] Pillay, A, Wang,J., Wall,A.D. and Ruxton, T. 2001. A maintenance study of fishing vessel equipment using delay-time analysis, Journal of Quality in Maintenance Engineering, vol. 7, no. 2, pp. 118–128. [9] Manoj Kumar Kar, Implementation of Planned Maintenance Using TPM Methodology For A Bi-Cycle Manufacturing Industry. International Journal of Mechanical Engineering and Technology, 7(6), 2016, pp. 253–270. [10] Dr .S. Ravichandran, Condition Based Maintenance Power Plant Machineries Through Non Destructive Testing And Emerging Techniques, Volume 1, Issue 2006, Jan–Dec 2006, pp. 5–14, International Journal of Mechanical Engineering and Technology (IJMET) [11] Dr. R. Dillibabu and Suresh. K, Development of Multi-Agent Systems Based Intelligent Fleet Maintenance Scheduling, 1 (1), May - June (2010), pp. 115-126, International Journal of Industrial Engineering Research and Development (IJIERD) [12] White, E. N. 1973. Maintenance Planning – Control and Documentation. London, Gower Press. [13] White, E.N. 1973. Maintenance Planning, Control & Documentation, Gower Press Limited, Essex. [14] White. E.N. 1975. Introduction to Maintenance Planning in manufacturing establishments, United Nations Publication, New York. http://www.iaeme.com/IJM/index.as 22 editor@iaeme.com