Computer Aided Design for Maintenance Planning and Assessment for a Construction Company

Transcription

1 WebsJournals Epistemics in Epistemics Science, Engineering Science, and Engineering Technology, and Vol. Technology, 1, No. 4., Vol. 2011, 1, No. 4., 2011, Epistemics in Science, Engineering and Technology Abstract Computer Aided Design for Maintenance Planning and Assessment for a Construction Company T. K. Ajiboye and W.B. Yusuf Mechanical Engineering Department,University of Ilorin, Ilorin Nigeria. engrtkajiboye@yahoo.com The needs in the highly challenging environment for organizational competitiveness with respect to cost, quality services and on-time deliveries through optimum machine functionality, guaranteed long-term stability, increases in productivity and system reliability have contributed to new interest in maintenance planning and assessment. The research work is aimed at developing window based design software that combines flexibility with user interface for maintenance planning and assessment. The Computer Aided Design (CAD) model developed assesses and compares the maintenance plans based on three major parameters which are: the periodicity of the maintenance plans, the equivalent time before full resetting for the maintenance plans and the graph of complexities against time for the plans. The impute parameters are: the preventive maintenance frequency (PF), the preventive maintenance level (PL), the repair level (µ), the number of days the machine operates in a week (D), and failure rate (λ). The plan with higher periodicity is preferred. This indicates that it is more capable of reducing the system complexity which makes it more stable and also an indication that the functional requirements will be satisfied. The preventive maintenance (PM) plans adopted by a construction company in Nigeria was used to illustrate the application of the proposed new CAD model developed. Keywords: Computer Aided Design, periodicity, maintenance plans, complexities, maintenance frequency, preventive maintenance level, repair level. 1. Introduction Maintenance is the combination of all technical and associated administrative actions intended to retain an item in, or restore it to, a state in which it can perform its required function, Azlan (2009). Many companies are seeking to gain competitive advantage with respect to cost, quality service and on-time deliveries. The effect of maintenance on these variables has prompted increased attention to the maintenance planning as an integral part of productivity improvement. Maintenance is rapidly evolving into a major contributor to the performance and profitability of manufacturing systems. Six maintenance programs are identified within the maintenance hierarchy, each representing an increased level of sophistication. They are; Reliability-centered maintenance (RCM), Total productive maintenance, Preventive maintenance (PM), Predictive maintenance, Scheduled maintenance and Reactive maintenance Pradhan and Bhol (2006). Maintenance schemes in manufacturing systems are devised to resets the machine functionality in an economic fashion and keeps it within acceptable levels. The classification of various maintenance actions are shown in Figure 1. PREVENTIVE CORRECTIVE TIME BASED PREVENTIVE CORRECTION BASED PREVENTIVE PLANNED CORRECTIVE UNPLANNED CORRECTIVE Figure 1: Classifications of Maintenance Actions Maintenance planning and assessment has been a topic of research for centuries. A smaller fraction of researchers have devoted their time to studying the extent of resetting, the maintenance frequency or the virtual age of the maintained systems using Computer Aided Design with periodicity principle. In the future, engineering systems will become more complicated since the number of the functional requirements (FRs) will 181

2 continue to increase requiring many layers of decomposition, unless fundamental principles for reducing complexity can be devised Suh and Lee (2004). In engineering, the ultimate goal is to reduce the complexity of engineering systems through the use of a rational design approach that is based on fundamental principles so as to increase their reliability, reduce the cost of development and operation, and enhance their performance. The process of reducing complexity in any engineering systems can be considered to be maintenance i.e. satisfying the functional requirements FRs within their specified ranges, i.e., the design range Suh (2005). The deterioration of manufacturing system performance is characterized by a time moving system range and may be considered as a time dependent complexity Suh (1999). Periodicity is important for the long-term sustainable system functionality. However, how often a system should be reset, to what extent the system should be reset, what is a desirable level of periodicity and at what cost, remain un-answered questions. Another important consideration is quantifying the level of periodicity that would result from adopting a certain maintenance regime and comparing it with the level of periodicity required to achieve the desired functionality goals Meselhy (2005). Therefore, a model to quantify the amount of required periodicity is needed to help design new and effective maintenance strategies and evaluate existing ones. 2. Complexity theory In engineering design, we need to satisfy FRs within their specified ranges Suh and Lee (2004), i.e., the design range, as shown in Figure 2. However, the actual performance of the system that is designed to satisfy the FRs may be given by the system range shown in Figure 3. When the system range is not fully inside the design range, we cannot satisfy the FRs at all times. When the FRs cannot be satisfied, the task appears to be complex. Therefore, complexity is defined as a measure of uncertainty in achieving the specified FRs. According to this definition, the relationship between the design range and system range determines complexity Suh (1999, 2005). Probability Density Design Range Functional Requirement (FR) Figure 2: The design range of FR vs. probability density System Range Probability Density Design Range Functional Requirement (FR) Figure 3: The design range of FR vs. probability density. 2.1 Periodicity Periodicity re-initializes the system functionality to a like new state, which assures a high degree of functional certainty Suh and Lee (2004). Hence, introducing periodicity reduces, if not eliminates, uncertainty and consequently decreases the complexity associated with combinatorial complexity. The notion of periodic 182

3 resetting in the functional domain has been defined as a mechanism to reduce complexity and to restore the desired state of operation Suh (2005). 2.2 Single resetting plan periodicity The main focus is placed on the time-based maintenance plans because they are the most commonly used in industry. These two aspects of time-based maintenance plan are illustrated in Figure 4, figure 5 and figure 6. Figure 5 shows the effect of decreasing the resetting frequency by increasing the time between resetting to 2T while maintaining a full resetting level. The resetting plan represented by Figure 5 has less periodicity than the one represented by Figure 4, which leads to a noticeable increase in the average complexity level. The effect of the resetting level is demonstrated in Figure 6 where different resetting levels are shown while keeping the time between resetting at T. Figure 4: Effect of periodicity on complexity with full time resetting at time interval T. Figure 5: Effect of periodicity on complexity with full time resetting at time interval 2T. Figure 6: Complexity against time at time intervals T with different resetting level Meselhy et al (2005). For simplicity, it will be assumed that the complexity increases linearly with a rate ϴ, which will be called complication rate; however, other relationship functions may be used. The complexity change in the presence of a resetting plan, where the time between resetting is T and the resetting extent, is γ, is shown in Figure 7. when there is no periodicity (pr=0, combinatorial complexity case), the complexity continues to increase without resetting as represented by the line of zero periodicity (dashed line).the other theoretical extreme is when the system is being fully reset at infinitesimal time periods, such that its complexity is always zero (pr = ). This case is represented by the line of full periodicity. Therefore, as the periodicity increases, area α increases and area β decreases. 183

4 Figure 7 Complexity versus time with resetting plan Meselhy et al (2005). And complexity ( C ) at any time T i is described by Meselhy et al (2005): i ' Ci C Ci1 T (1) where, C i is the complexity at time T with i th reset, i represent the number of resets, T is the time between ' resetting, is the complication rate and C (1 C). 3. Methodology In this research study, periodicity equations were used to model a computer aided design software for assessing maintenance plans in the functional domain. The developed window based design software combines flexibility with user interface. The computer aided design (CAD) assesses and compares the maintenance plans based on the periodicities of the plans. 3.1 Modelling equations The order of the design calculation techniques is presented below. Preventive maintenance periodicity: Preventive maintenance is the main source of periodicity when the maintenance strategy calls for minimal repair of failures. The machine is reset with each PM, therefore, the resetting rate is the same value as the PM frequency (PF) and the periodicity extent is expressed by the level of PM (PL). Therefore, the periodicity resulting from the PM ( Pr ) of N classes can be described bysuh (1999): 184 PM N PFi Pr PM (2) i1 2 1 PLi Total maintenance plan periodicity:the total maintenance plan periodicity is the sum of the failure repair periodicity and PM periodicity. This can be expressed as Suh (2004):

5 Pr 2 1 PFi PLi N i (3) Therefore, given the maintenance plan parameters, µ, PL i, and PF i and the machine failure rate λ, the maintenance plan periodicity can be calculated. This calculated periodicity (pr) is a measure of the relative ability of the maintenance strategy to reset the machine s functionality. Virtual age model: The new developed CAD software was utilized to predict the long-run future performance of maintained systems. The virtual age ( V ) concept will be utilized to develop this relationship.. Therefore, average the average long-run virtual age (V) would be Finkelstein (2009): 1 V average 2 pr (4) Equation (3) explains the relationship between the system steady state average virtual age and the applied maintenance plan periodicity. The equivalent time before full resetting ( T E ) which is the time it will take the machine to behave as though it was new is given by Finkelstein (2009), T 2 (5) E V average CAD Software model and parameters: The computer aided design software compares the maintenance plans base on three major parameters: Periodicity, equivalent time before full resetting for the maintenance plans and Graphs (plotted data of complexity against time). The impute parameters are: the preventive maintenance frequency (PF), the preventive maintenance level (PL), the repair level (µ) and failure rate (λ). The plan with higher periodicity is preferred. This indicates that it is more capable of reducing the system complexity which makes it more stable and also an indication that it will satisfy it functional requirement. The periodicities of the maintenance plans were computed using equation (2). The preventive maintenance frequencies were computed as; 1 PF (6) D where, = preventive maintenance frequency and D = the number days the machine operates in a week. Flow chart: The computer aided design software was demonstrated in a logical arrangement to show steps by steps on how to determine each of the designed parameters used to compare the maintenance plans. This logical arrangement is called flow chart (Figure 8).. Flow-charting is the rudimental aspect of any computer programming development as it guides on how to accompany the desired objectives in formulating the problem with all the steps involved. 4. Case study of a Construction Company In the following example, the maintenance plan adopted at of one a construction company, here in Nigeria was used to illustrate the application of the proposed new computer aided design software for assessing maintenance plans. The PM plan comprises four classes: weekly, monthly, quarterly and semi- annual. Each PM class has associated courses of action for each machine. This maintenance plan applies to every resource/machine in the plant. One of these resources is an articulated hauler with fleet number EG-198, which experiences random failures with an average of two failures per week. 185

6 Figure 8 Logical arrangement of the designed package. 4.1 The existing maintenance plan The articulated hauler operates 6 days per week. Using the proposed maintenance modeling approach, the plant maintenance strategy can by fully described by the following parameters. Table 2: Existing maintenance plan parameters Type A(Weekly) B(Monthly). C(Quarterly). D(Semi-Annually). PM frequency PF i PM level PL i

7 4.2 Alternative maintenance plan The company is considering an alternative maintenance strategy, the assessment of alternatives under consideration is given below. The tables below described the parameters of the alternative maintenance plans. Table 3: First alternative maintenance plan parameters Type A(Weekly). B(Fortnightly) C(Monthly) D(quarterly) PM frequency PF i PM level PL i Table 4: Second alternative maintenance plan parameters Type A(Weekly). B(Fortnightly). C(Monthly). D(Quarterly). E(Semi- Annually). PM frequency PF i PM level PL i Results and discursion Figure10 below gives the periodicities and equivalent time before full resetting for the plans). The second alternative preventive maintenance plan is preferred as it has the highest periodicity. The periodicity of the second alternative maintenance plan is greater than that of the existing maintenance plan and that of the first preventive maintenance plan. This can be attributed to the fifth class that was introduced to further increase the periodicity. Figure10 Summary of the results obtained for the maintenance plans. Figure 11: Summary of the results obtained for the comparism of maintenance plans. 187

8 The complexities are greater than one for thirty-two weeks out of the forty-eight weeks for the current / existing PM plans. The durations with high complexities are possible failure time David and Jim (1998). The percentage reliability was estimated to be %. This is below average. For the first alternative PM, the percentage reliability and stability was estimated to be 58 % and this is slightly above average. The complexities are greater than one for fifteen weeks out of the forty-eight weeks for the second alternative PM plans. The percentage reliability and stability was estimated to be %. This further adds to the credibility of the second alternative PM plans. The Figure (12) below shows the complexity time curve for the three plans for the case study. This was obtained by plotting the complexities against time for the plans. The curve for the existing PM plans reflects a maximum complexity of while that of the second alternative PM plans reflects a reduced maximum value of This is an indication that it is more stable, reliable and is more capable of satisfying the functional requirement. 5. Conclusion Preventive maintenance introduces periodicity which is required to ensure the system functional stability throughout its life. Computer aided design for quantifying periodicity has been developed and this periodicity measures the relative ability of the maintenance plan to re-initialize the hauler s functionality. It can be said at this juncture that, if the second alternative PM plan with percentage reliability of 68.75% was adopted, the failures recorded in table 1 would have been greatly reduced. The software is capable of computing the design parameters i.e. periodicities of the PM plans, the average long-run virtual age and equivalent time before full resetting for the maintenance plans. The proposed software can be used to quantitatively compare the resetting ability of different maintenance plans, which combined with other performance metrics like cost, availability and product quality, can vastly enhance decision making in selecting appropriate maintenance plans. Also, the software, written in Visual Basic programming language because of its flexibility and ease of its accessibility in formulating the design to suit the design objective, is also capable of plotting the graphs of complexities against time for the PM plans under consideration. The graphs can be used for quick assessment and comparism of the maintenance plans. Finally, it has been shown that the assessment and comparism of preventive maintenance plans, using the proposed computer aided software, is quite simple which can be readily adopted in an industrial maintenance stategy 188

9 References Azlan, S. A Cost decision making in building management maintenance practice in malaysia, journal of facility management, 7(4), David P.F, Jim C A survey of complexity measures, complex systems summer school, Santa Fe Institute. Dekker, R Applications of maintenance optimization models: a review and analysis, Reliability Engineering and System Safety, 51(3), Finkelstein, M Virtual age of non-repairable objects, Reliability Engineering and System Safety, 94/2: Meselhy K. T., W. H. ElMaraghy, E. A. ElMaraghy A periodicity metric for assessing maintenance plans, CIRP Journal of manufacturing science and technology,.2, Pradhan, M. K J. P. Bhol Trends and perspective in industrial maintenance management, Journal of Manufacturing systems, 16(6), Sheu, S.-H., Griffith, W.S., Nakagawa, T Extended optimal replacement model with random minimal repair costs, European Journal of Operational Research, 85(3), Schuh G Release-engineering - an approach to control rising system- complexity, Manufacturing Technology, CIRP Annals, 53(1), 167. Suh, N. P A theory of complexity, periodicity, and design axioms, Research in Engineering Design, 11, Suh, N. P Complexity, theory and applications, MIT, Oxford University Press. Suh, N. P Functional periodicity as the basis for long-term stability of engineering and natural systems and its relationship to physical laws, Research in Engineering Design, 15/1, Suh, N. P Complexity in engineering, CIRP annals manufacturing technology, 2(54), Suh, N. P. and T. Lee Reduction of complexity of manufacturing systems through the creation of timedependent periodic complexity from time-dependent combinatorial complexity, CIRP Journal of Manufacturing Systems, 33, 2. Weule H., D. Spath, S. Riedmiller and M. Schreiber Optimization of production systems using a fuzzy expert system and computer simulation, In: Production Engineering, III(2), Xiao-Gao, L., V. Makis and A. K. S Jardine A replacement model with overhauls and repairs, Naval Research Logistics, 42(7),