Maintenance Decisions Making Method for Repairable System by using

Output-based Maintenance Technique: A Case Study at Pulp Manufacturing Industry

Rosmaini Ahmad School of Mechanical Engineering

Engineering Campus Universiti Sains Malaysia

Nibong Tebal, Penang, Malaysia rosmainibinahmad@yahoo.com

Abstract-This paper presents a maintenance decision making

method for the case of repairable system by using output-based

maintenance (OBM) technique, which is an alternative

technique under condition-based maintenance (CBM)

approach. Under OBM technique, machine output measure

such as product quality characteristic become the main

monitoring parameter to indicate the deteriorating process and

failure limit of the system. A simple decision algorithm model

for the case of single-repairable system is proposed in order to

suggest not only what the best maintenance actions to be taken

but also when the right time to be performed. A case study at a

pulp manufacturing industry has been presented to validate

the maintenance decision making method. Results from the

case study show that the proposed method is beneficial in

making the maintenance decision for real industry case. The

application of decision algorithm model through monitoring

and future trend forecasting processes is able to suggest the

right maintenance at the right time. This paper concludes with

some uniqueness features of the proposed maintenance

decision making method, contributions and the future works.

Keywords-component; maintenance decisions making; condition-based maintenance; output-based maintenance; repairable system; industry application; case study.

I. INTRODUCTION

Maintenance engineering has been a research topic since the basic strategy of maintenance practise has changed from corrective maintenance (CM) to preventive maintenance (PM) strategies. The principle of PM strategy is to perform the maintenance before the failure occurs, thus the effects of unexpected failures can be avoided or at least can be minimised. Research in maintenance engineering towards PM strategy is concern with maintenance decision making process to address the questions such as when and where to do maintenance and what the best maintenance activities (e.g. repair or replace) that should be carried out.

One of the challenges in maintenance engineering research is to solve the maintenance problem for the case of repairable equipment or system. According to reference [I], repairable refers to the equipment or system that can be repaired to recover its functions after each failure rather than be discarded. The basic idea in solving maintenance problem for repairable system is to decide when to do repair and

978-1-61284-486-2/111$26.00 ©2011 IEEE

15

Shahrul Kamaruddin School of Mechanical Engineering

Engineering Campus Universiti Sains Malaysia

Nibong Tebal, Penang, Malaysia meshah@eng.usm.my

when to replace/overhaul the system. The rational of making these decisions is that the costs and the effects of each decision are varied. In most cases, the cost of repair is less than the costs of replace/overhaul, while, the effect of replacement/overhaul action is better than repair [2]. In other words, at a certain age (lifetime) or condition of the system, it becomes more economical to replace/overhaul the system than to do repair.

In the literature, maintenance problems for the case of repairable system are widely discussed under time-based maintenance (TBM) approach, which it is named as minimal repair policy. Minimal repair policy under TBM approach recommends replacing/overhaul the system when it reaches at certain age, t or at certain failure limit or costs and any failure occurring before it, the repair or minor repair is carried out. The application of minimal repair policy in solving maintenance problem on repairable system is presented by many researchers. For instance, reference [3] studied the minimal repair policy based on a cumulative repair-cost limit, where the concept uses the information of all repair costs to decide whether the system is repaired or replaced. The paper formulated a model of long-run expected cost per unit time by incorporating costs due to replacement and minimal repair is derived. Reference [4] also applied the minimal repair policy based on a cumulative repair-cost limit. An optimisation model was presented to find the optimal number of minimal repairs before replacement that minimizes the long-run expected cost per unit time. Another minimal repair policy application based on cumulative repair-cost limit subjected to shocks is presented by [5]. The other current studied of minimal repair policy is given by [6-8].

However, the application of TBM approach in solving maintenance problem including repairable case has many disadvantages and limitations towards practical point of view [9]. Therefore, condition-based maintenance (CBM) approaches has become an alternative. The heart of CBM is the condition monitoring process, where signal that indicates the system condition is continuously monitored by using certain type of sensor or other appropriate indicator [10]. The motivation of CBM is that 99 per cent of the component failures are preceded by certain signs, conditions, or indications that a failure was going to occur [11]. Nevertheless, the application of CBM approach in solving

maintenance problem of repairable case is very limited. It is because the current research of CBM is more focusing on condition monitoring process and future deteriorating trend prediction rather than maintenance repairable decisions [12, 13].

Therefore in this paper, a maintenance decision making method for the case of repairable system is proposed by using condition-based maintenance (CBM) approach. An alternative maintenance technique called output-based maintenance (OBM) is used as the main structure of the method. Simple decision algorithm is introduced to show how the whole process of maintenance decision making works.

II. OUTPUT- BASED MAINTENANCE TECHNIQUE

Output-based maintenance (OBM) is one of the techniques under condition-based maintenance (CBM) approach. The rational and motivation of the used of OBM technique in maintenance decision making process can be presented by the production machine process scenario.

Production machine can be classified as a complex system, where many mechanical/electrical/electronic components are connected and worked together to perform one or more machining processes such milling, rewinding, cutting, perforating etc. In addition, these machining processes can be referred as sub-systems of the production machine. From the production processes point of view, the output of any machining process plays an important part to produce the product with required dimension or quality. In most cases, the machining process mechanism is considered failed when the output measure (e.g. product quality characteristic) of the product is out of specification. This is supported by [14], where there is a close relationship between machine maintenance and its output (e.g. product quality), as its output depends on the machine system condition.

Therefore, the basic idea of OBM technique is to use the output machining process measure (e.g. product quality characteristic) as the main condition monitoring (CM) parameter not only to observe the health (deterioration) of the machining process mechanism but also it can be referred as the failure limit indicator. The general process of maintenance decision making by applying the OBM technique can be shown in Figure 1.

Referring to Figure I, the data from the output machining measure such as cutting quality of the product is continuously monitor and compared with the quality specification limit. If the current monitoring indicates the quality of the product is out of specification, the appropriate action (do-something) towards maintenance is carried out. Otherwise, the action of do-nothing is considered and data monitoring process is continued. Even the principle idea and the application of OBM technique in making maintenance decisions is mentioned by [15, 16], but how this technique can be extended for the case of repairable system is still absent. Therefore, the following section presents the detail maintenance decision making process for the case of repairable system by applying OBM technique.

16

Figure I. General maintenance decision making process based on O BM technique.

III. REPAIR-REPLACEMENT/OVERHAUL DECISION

ALGORITHM MODEL

Figure 2 shows the structure of the maintenance decision algorithm model for the case of single-repairable system. The model is designed based on a tree diagram approach and three possible decisions are involved; do-nothing, repair (minor repair) and replace/overhaul. Referring to Figure 2, there are two main processes that relate to each other; deterioration monitoring process and maintenance decision analysis.

The purpose of deterioration monitoring process is to monitor and predict the future system condition based on its output measure (e.g. cutting appearance quality). This process can be carried out based on time series analysis towards one-step-ahead-based prediction approach. One of the prediction models that can be applied in this process is double exponential smoothing (DES) models. The equations of DES model are as follow:

t = Lr-1 + Tr-1

(1)

(2)

(3)

Where, L{ is the level at time t, a is the weight for the level, Tr is the trend at time t, ris the weight for the trend, 1', is the data value at time t, and Y{ is the fitted value, or one­step-ahead forecast, at time t. Then, the value of Y, will be compared with predetermined failure limit. If t still do not reach or exceeds the failure limit, the decision of "do­nothing" is preferred and the process of monitoring and predicting system condition is continuous. Otherwise, if t is

Figure 2. Example of a TWO-COLUMN figure caption: (a) this is the format for referencing parts of a figure.

reach or exceeds the failure limit, the analysis of repair limit policy is considered.

In the proposed maintenance decision algorithm model, repair limit policy is concerned with maintenance decisions analysis to decide either to do minor repair or replace/overhaul (major maintenance) based on economic point of view. In this paper, repair limit policy is the calculation of total maintenance cost per lifecycle, TrDeosl of the system by considering the number and total costs of minor repair performed along its lifecycle. Under this policy, the decision of "minor repair" is suggest if the total maintenance cost per lifecycle, Trc-cos, is less then the total unexpected failure cost, TF-cost and otherwise the decision of "replacementloverhauf' is done. The total maintenance cost per lifecycle, Teos' and the total unexpected failure costs can be calculated as follow;

(4)

TF-cos{ = CProRej + C[)lfnTi + CMain + CWorOve (5) Maintenance decisions rule (predetermined cost limit)

under repair limit policy is defined as follow; If, TrC-eos{ < TF-cosl the decision of "minor repair" is

preferred

If, TLc-cost =1> TrccoI·t the decision of "replacementloverhauf' is preferred Notation,

TH'-cost = Total maintenance cost per lifecycle

Trccolt = Total unexpected failure cost

CpR 0 = Cost of preventive replacement/overhaul

Cpr = Cost of preventive repair

CProRe! = Cost of product reject (e.g. reworks)

C[)lfnTi = Cost of production machine downtime

17

CMain = Cost of maintenance (e.g. minimal repair or major repair)

CWorOve = Cost of workers overtime

IV. CASE STUDY

In order to validate the proposed maintenance decision making method, a case study has been carried out at a pulp manufacturing industry located in the north of peninsular of Malaysia. The case study is performed at one of the production plants called end-of-line (EOL), which the function of this plant is to process semi-finished product to a finished product. In the production floor of EOL, production machines are fully automated in order to perform many machining processes such as embossing, perforating, rewinding, sealing, cutting and packaging.

The focused of this case study is on the sealing process (also known as gluing process). The purpose of the sealing process is to glue the top sheet of pulp (semi-finished product called log roll) with certain surface quality appearance. Figure 3 shows the part on the product that has been glued.

Figure 3. Gluing line on the product

In relation, Figure 4 shows the graphical view of the sealing process mechanism. The basic technique applied for this process is spry-based technique. Generally, this technique used air pressure mechanism to spry the glue liquid on the surface of the product (see Figure 3). Referring to Figure 4, sealing process mechanism can be classified as a single-repairable system that consists of four main machinery units; spry head, piping system, glue liquid tank and air pressure system.

In the current maintenance practise, two types of maintenance decisions are commonly decided on the sealing process system. First is minor maintenance or minor repair decision that is carried out during the operation when the surface appearance quality of gluing line/area is out of specification. Second is major maintenance or overhaul that is planned once a month in order to restore the entire sealing process system. The details maintenance activities performed at each maintenance types is tabulated in Table 1.

TABLE I. DETAILS MAINTENANCE ACTIVITIES

Maintenance types Details maintenance activities Average time taken (minutes)

Minor repair Clean the head of glue spry and adjust 20-30 minutes the level of air pressure

Overhaul Clean the whole sealing process 240 minutes system including glue spry head, glue (4 hours) tank, piping system, sensors and reset the air pressure level

However, the current mamtenance practIse on the sealmg process system is designed based on maintenance engineers experience without depending on any analytical analysis. Therefore, the basic consideration towards maintenance decisions making (minor repair and overhaul) process and the benefits of the maintenance decisions is vague. In the following section, the application of the proposed maintenance decision making method is presented.

A. Results and Discussion

In this case study, the data of product quality focused on the surface appearance quality of gluing line/area have been collected from 15 March to 16 April during the week days operation in order to represent the sealing process system performance or condition. The data was collected every two hours of operating time at all working shifts (three working shifts). Twenty samples of the product (as shown in Figure 2) were taken from the production line to evaluate its quality (surface appearance quality of gluing line/area). This

evaluation process is carried out by product quality inspector based on the quality scale that currently applied; "1 =very good", "2=good", "3=ok" and "4=immediate!y reject". Then, the average scale of the product quality is determined to indicate the current performance of the sealing process system. The minor repair on sealing process system is required if the average scale of the product quality reaches or exceeds the scale of 2 (predetermined failure limit of the sealing process system).

Figure 5 shows the overall trend of sealing process system performance based on surface appearance quality of gluing line/area. Based on the maintenance records from the case study company, three minor repairs have been carried out on 22 March, 30 March and 07 April, respectively before the overhaul activity is performed on 19 April. Referring to Figure 4, each of the minor repairs that have been carried out has increased the average scale of the product quality. For example, the minor repair that performed on 22 March has increased the product quality scale from 1.88 to 1.80. However, basic consideration towards minor repair and overhaul decisions do not follows the rule as mentioned in the previous paragraph. For instance, the decisions of minor repair on 22, 30 March and overhaul decision on 19 April did not show the sealing process system required maintenance because the trend of the product quality was

Figure 4. Graphical view of sealing process mechanism.

still under the scale of 2. Only the minor repair decision performed on 8 April seems the right decision of minor repair because the trend of the quality scale has reached the predetermined failure limit area.

Figure 5. Overall trend of sealing process system performance.

18

Based on the trend of the sealing process system performance (Figure 5) and the series of maintenance activities (minor repair and overhaul) that have been carried out, the proposed maintenance decision making method is applied. The main purpose is to compare the decision suggested by decision algorithm model that has been introduced in Section 3. The results are presented in Figure 6.

Referring to Figure 6, it presents the future prediction trend of the sealing process system performance based on the surface appearance quality of gluing line/area from 15 March to 16 April. Figure 6 (a) shows the predicted trend on 23 March is still far from the predetermined failure limit, thus the proposed decision algorithm indicated it is still do not needs the minor repair as decided via current maintenance practise instead of "do-nothing" decision.

Figure 6 (b) shows the predicted trend on 31 March has exceeded the predetermined failure limit, thus maintenance

decisions either to do minor repair or major maintenance (overhaul) is needed. Based on the repair limit policy analysis (refers to Section 3.0), the decision of minor repair is suggested and it must be planned before the predicted trend exceeds the predetermined failure limit (as shown in Figure 6 (b)).

In Figure 6 (c), the predicted trend on 8 April indicates that it already exceeded the predetermined failure limit, thus repair limit policy analysis is required. From the result of repair limit analysis, the decision of "minimal or minor repair" is still economical to be performed and it must be planned before the predicted trend exceeds the predetermined failure limit (as shown in Figure 6 (c)).

In Figure 6 (d), the predicted trend on 19 April shows that it also has exceeded the predetermined failure limit, thus like the results in Figure 6 (b) and (c), the analysis of repair limit policy is required. The result of repair limit policy analysis indicates that it still economical to carry out minimal repair. The result of Figure 6 (d) is opposites from the current practise of the case study company, where the decision of "overhaul" is done.

v. CONCLUSION

A maintenance decision making method by using output­based maintenance (OBM) technique is presented. A simple

decision algorithm model for deciding the appropriate maintenance decisions of single-repairable system case is introduced. A case study at pulp manufacturing industry has been carried out to validate the proposed maintenance decision method. Results show that the method is simple and practical to be applied in real industrial case. One of the contributions of this paper is proposing the maintenance decision making method for the case of repairable system based on condition-based maintenance (CBM) approach, where in the literature very limited works has been done. Another contribution is introducing the decision algorithm model based on decision tree diagram approach, which is simple and easy to interpret. In addition, the proposed decision algorithm model is not only able to suggest what the best maintenance to be taken but also when the right time to perform the maintenance. However, the decision algorithm model that has been introduced in this paper only focuses on the single-repairable system case. Therefore, the future works of this research will be extended to the development of decisions algorithms models for more complex cases such as multiple-components that consist of repairable and/or non­repairable components.

Figure 6. Future trend prediction based on proposed maintenance decision algorithm.

19

ACKNOWLEDGMENT

The authors wish to gratefully acknowledge the Universiti Sains Malaysia Fellowship Scheme and Research University Grant Scheme from the Universiti Sains Malaysia for supporting this research. The authors also would like to thank the referees for their useful suggestions to improve the first version of our article.

[I]

[2]

[3]

[4]

[5]

[6]

REFERENCES

L. H Crow, "Reliability analysis for complex repairable systems", in Proschan, F. and Serfiing, R.J. (Eds), Reliability and Biometry, SIAM, Philadelphia, PA, (1974), pp. 379-410

A.N. Das and S.P. Sannah, "Preventive replacement models: an overview and their application in process industries", European Journal Industrial Engineering, Vol. 4, No. 3, 2010, pp. 280-307.

M-T. Lai, "A periodical replacement model based on cumulative repair-cost limit", Applied Stochastic Models in Business and Industry, Vol. 23,2007, pp. 455-464.

c-c. Chang, S-H Sheu and Y-L. Chen, "Optimal number of minimal repairs before replacement based on a cumulative repair-cost limit policy", Computers & Industrial Engineering, (2010), doi: 1O.1016�j.cie.201O.07.005.

S-H. Sheu, C-C. Chang, Y-L. Chen and Z.G. Zhang, "A Periodic Replacement Model Based on Cumulative Repair-Cost Limit for a System Subjected to Shocks", IEEE Transactions on Reliability, Vol. 59, No. 2, June 2010.

Y. Sun, L. Ma and J. Mathew, "Prediction of system reliability for single component repair", Journal of Quality in Maintenance Engineering, Vol. 13 No. 2, 2007, pp. 111-124.

20

[7]

[8]

[9]

[10]

[11]

[12]

l.T. Castro and E .L. Sanjuan, "An optimal repair policy for systems with a limited number of repairs", European Iournal of Operational Research, Vol. 187,2008, pp. 84-97.

RH. Yeh, K-C. Kao and W.L Chang, "Optimal preventive maintenance policy for leased equipment using failure rate reduction", Computers & Industrial Engineering, Vol. 57, 2009, pp. 304-309.

R Dekker, "Application of maintenance optimization models; a review and analysis", Reliability Engineering and System Safety, Vol. 51, 1996, pp. 229-240.

J. Campos, "Development in the Application of ICT in Condition Monitoring and Maintenance", Computers in Industry, Vol. 60,2009, pp. 1-20.

H.P. Bloch and F.K. Geitner, "Machinery Failure Analysis and Troubleshooting, Gulf, Houston, TX, 1983.

A. Heng, S. Zhang, A.C.C. Tan and J. Mathew, "Rotating machinery prognostics: State of the art, challenges and opportunities", Mechanical Systems and Signal Processing, Vol. 23, 2009, pp. 724-739.

[13] A. Bellini, F. Filippelli, C. Tassoni and G-A. Capolino, "Advances in diagnostic techniques for induction machines", IEEE Transactions on Industrial Electronic, Vol. 55, No. 12, December 2008.

[14] N.S. Arunraj and J Maiti, "Risk-based maintenance-Techniques and applications", Journal of Hazardous Materials, Vol. 142,2007, pp. 653-66\.

[15]

[16]

G. Taguchi, E,A, Elsayed and T. Hsiang, "Quality Engineering in Production Systems", McGraw-Hill, New York, NY, 1989.

M. Ben-Daya and S.O. Duffuaa, "Maintenance and quality: the missing link", Iournal of Quality in Maintenance Engineering, Vol. I No. 1, 1995, pp. 20-26.