jedt application of rcm for a chipping and sawing mill
TRANSCRIPT
JEDT 9,2
204
Received 10 January 2010 Accepted 28 January 2010
Journal of Engineering, Design and Technology Vol. 9 No. 2, 2011 pp. 204-226 DOI 10.1108/17260531111151078
Application of RCM for a chipping and sawing mill
Stanley Fore Department of Management/Project Management,
Cape Peninsula University of Technology, Cape Town, South Africa, and
Thabani Mudavanhu Project Engineering Consultants, PBGI Engineers & Constructors (Pty) Ltd,
Johannesburg, South Africa
Abstract
Purpose – This research is focused on the application of reliability-centred maintenance (RCM) in a chipping and sawmill company. The aim of the study was to illustrate the application of RCM in a chipping and sawing mill.
Design/methodology/approach – RCM is a structured process, which develops or optimises maintenance requirements of a physical resource in its operating context in order to realise its inherent reliability by logically incorporating an optimal combination of reactive, preventive, condition-based and proactive maintenance practices. A detailed analysis of the RCM approach is presented as a step towards improving preventive maintenance (PM) within a sawmill.
Findings – The study shows that the way that PM tasks are specified is a good indicator of the effectiveness of the PM program and could be a major source of maintenance-related downtime. It is also revealed that most maintenance programs, which purport to be proactive, are in fact reactive. The paper also shows that RCM can be successfully applied to industries anywhere; even in less industrialized countries.
Research limitations/implications – The paper focuses on a pilot study of a section of a chipping and sawmill. The development and implementation of the RCM approach is elaborated based on a pilot program in the edging unit of a sawmill company. Further application to the entire plant, albeit time-consuming, is recommended.
Originality/value – Application of RCM in sawmill industries, within developing countries, has had limited application. The paper demonstrates that regardless of technological challenges in less developed economies, maintenance approaches such as RCM can still be fruitfully applied in order to achieve maintenance excellence. The paper should be useful for maintenance practitioners and researchers, particularly in less industrialized countries.
Keywords Maintenance management, Manufacturing systems, Reliability centred maintenance, Timber
Paper type Case study
Introduction Rapid changing pace in manufacturing, along with high time to market pressure, vulnerable lean manufacturing practices, downtime sensitive designs and pressure to maximize profitability through ensuring that every business facet performs, makes it critical that manufacturing systems are reliable. Maintenance has come under the spotlight because of the direct effect that it has on production and, consequently, on sales volume. However, asset management is one of the last options to maximize cost savings in a competitive global economy (Schuman and Brent, 2005). Reliability-centred maintenance (RCM) is a structured process which is used to develop or optimise the maintenance requirements of a physical asset in its operating context. This is achieved
through logically finding an optimal mix between maintenance strategies and then objectively assigning the most applicable and effective preventive maintenance (PM) task. Nair (2007) alludes to the fact that the maintenance function has a direct impact on the operational efficiency of a plant. Good maintenance practices go a long way towards lowering production costs and improving operations management. Conversely, poor maintenance results in recurring breakdowns, which in turn, leads to intermittent production and invariably inferior quality products. Ingalls (2000) asserts that the effectiveness of the maintenance function strongly depends on maintenance of the organisational structure. Since the organisational structure influences personnel and hierarchy of communications, the allocation of resources with the right capabilities and in the appropriate maintenance areas is essential (Kelly, 1997). Over the passage of time, several different authors have proposed what they deem as best practices, strategies or models to manage and enhance the maintenance function. Hence, the challenge of coming up with the ideal model to drive maintenance activities has become a key research topic and a fundamental question that should be dealt with in order for organizations to attain effectiveness and efficiency in maintenance
management and in fulfilling enterprise objectives (Mishra et al., 2006). Several papers have been written, which deal with RCM application. RCM, among other things, can improve reliability and increase safety, while reducing unplanned corrective maintenance. This leads to a change in the entire maintenance regime within an organization (Hardwick and Winsor, 2002). An analysis of maintenance in various industries shows that it is a multi-faceted area, made up of several related preventive and proactive approaches such as total productive maintenance, RCM, and condition-based maintenance. Experiences from various industries show significant reductions in PM costs while maintaining or even improving the availability of the systems. If a maintenance program already exists, the result of RCM analysis will often be to eliminate inefficient PM tasks (Rausand, 1998). RCM also seeks to identify the best maintenance approach for an asset. This paper describes the application of RCM with respect to a sawmill company, with the aim of optimizing the maintenance function along with other company internal processes that are aimed at fulfilling external stakeholder requirements. The process can be described from both a deductive and an
inductive perspective (Soderholm et al., 2007). There is also an emerging view that maintenance not only reduces business risks, but should also be seen as a value-adding process in today’s dynamic and competitive business environment (Liyange and Kumar, 2003). Identifying a PM plan for a particular system requires identification of its functions, the way these functions may fail and establishment of a set of applicable and effective PM tasks, based on considerations of system safety and economy. A formal method to do this is the RCM methodology
(Marquez et al., 2009). In its most general meaning, optimisation covers efforts and processes of making a decision or a system as perfect, effective or functional as possible. Hence, optimisation of maintenance planning and scheduling can be conducted to enhance the effectiveness and efficiency of maintenance policies, which result from an
initial PM plan and program design (Marquez et al., 2009). The same authors further explain that because of this decision-making function, the term optimisation is often used in conjunction with procedures. The formulation and solution of an optimisation problem requires the following:
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● a description of the system configuration and boundaries that should be optimised;
● definition of preferably quantifiable parameters, for example, availability; and ● the evaluation criteria, which are also known as the pay-off or the objective
function, which can be maximised (availability, overall equipment effectiveness (OEE)) or minimised (downtime, overtime).
Thus, the most critical aspect of tabulating a formal optimisation problem is the establishment of a satisfactory criterion. The reliability and sophistication of solutions of the optimisation problem increases as they become better defined. This study extensively audited the case study plant. RCM is an optimising tool for the maintenance function, and it is therefore fitting that the definition of optimisation be explained in the RCM context. The following is a definition which was adopted from Nowlan and Heap (1978), as quoted by Moubray (1997):
RCM is a structured process for developing or optimising the maintenance requirements of a physical resource in its operating context to realise its inherent reliability by logically incorporating the optimal mix of reactive, preventive, condition-based and proactive maintenance practices.
The goal of RCM is to provide the stated function of the facility with the required reliability and availability at the lowest cost. This requires that maintenance decisions should be based on sound technical and economic justification.
The RCM logic and approach RCM does not contain any new principles for performing maintenance; it is a more structured way of utilizing the best several methods and disciplines. RCM governs the maintenance policy at the level of plant or equipment type. The strength of RCM is that it produces extraordinarily robust and effective planned maintenance programs. The goal of RCM is to provide the stated function of the facility with the required reliability and availability at the lowest cost. This requires that maintenance decisions should be based on sound technical and economic justification. Participant observation was used in this research (Yin, 1993). Top-level management commitment and support was initially obtained since these hold the purse strings and are influential within organisations. Reluctance on the part of top management to embrace RCM programs is due to insufficient understanding of the entire process (Hipkin and De Cock, 2000). Introducing RCM in order to obtain an RCM-based maintenance programme places requirements on the organisation and its existing maintenance programme. A holistic view is necessary in order to identify and manage these requirements (Backlund and Akersten, 2003), and these are outlined below:
● determine the function of the system/component 230 per cent of the RCM time is devoted to answering the question “what are the functions and associated performance standards of the equipment in its present operating standards?” (Rausand and Høyland, 2004);
● define the functional failure – a functional failure is an inability to fulfil the standard performance; it can be considered as an unsatisfactory condition;
● evaluate the consequences – looks at what happens when a failure occurs; and ● assign maintenance tasks to improve the systems reliability.
This allows an assessment to comprise the criticality of the failure and where safety, availability or costs consequences are high. It also follows that the RCM approach allows for a justifiable and auditable selection of appropriate maintenance requirements of an asset (Pride, 2005). The RCM process is governed by a set of principles and is executed by detailed methods. Smith and Hinchcliffe (2004) outline the RCM process, as presented below:
Step 1. System selection and information collection.
Step 2. System boundary definition – this is important for two reasons (Smith, 2005):
• gives knowledge of system so that important functions are not neglected; and
• determines what comes into the system as well as out of the system.
Step 3. System description and functional block diagram.
Step 4. System functions and functional failures.
Step 5. Failure mode and effects analysis (FMEA).
Step 6. Logic decision tree analysis (LTA), prioritising.
Step 7. Task selection – select only applicable and effective PM tasks.
The above seven steps provide a baseline definition of the preferred PM tasks on each system. Two additional steps are required in order to complete a successful RCM program, namely:
Step 8. Task packaging – which will carry the recommended RCM to the floor.
Step 9. Living RCM program – comprising the necessary actions to sustain the beneficial results of Steps 1-7.
Data collection Different data collection methods were used during the research and interviews, reviewing documentation and historical records, direct and participation research and action research. These were used to populate the various data fields in the RCM approach.
Interviews Face-to-face interviews were held with senior management and supervisors, while all control room operators, relief operators and artisans were also interviewed. Interviews were used as a means of checking the reliability of data that were collected by other means. Though unstructured interviews do not maintain focus on the main subject areas, they help to provide unexpected and overlooked information and therefore cover all areas of concern. Unstructured interviews also help to obtain information that cannot be formally obtained.
Reviewing documentation and historical records Weekly and monthly reports regarding production and maintenance activities were reviewed in order to obtain the required data for analysis in this research. Minutes of meetings, production records and budgets were also consulted to populate
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various data fields that are pertinent to the research. Equipment history files were also used to determine the failures and the corrective maintenance actions taken.
Direct and participatory observation
According to Yin (1993), observations of an organizational unit add a new dimension to an understanding of the context of the phenomenon that is studied. Observations were made of on how maintenance personnel and the operators executed maintenance tasks. Merriam (1988) states that one should interact practically with people in the field in order to observe behaviour within a natural setting.
Company background The company in question produces timber based on market requirements. The plant is a small to medium manufacturing facility, which was built in 1991 and was
commissioned in 1993. The company has a capability of producing 85,000 m3 of sawn timber per annum. Upon inception, the company was one of the newest sawmills in Southern Africa and represented an investment of about US$11.2 million at that time. The plant’s asset value has since doubled owing to several expansion projects and modifications. The mill employs 450 people who operate a three-shift system 24 hours a day, six days a week. All raw materials come from the company’s own estates that are located in the same area. The main product from this mill is rough sawn timber, of which 90 per cent is exported. PM strategies in many companies are not too effective nor do they assure plant reliability, whilst they cost companies hugely. The pine sawmill is no exception. The absence of an effective maintenance system at this mill has been made apparent by low plant availability of 85 per cent versus a target of 97 per cent. It has a high overtime rate of 19 per cent of the total labour hours (versus) a target of (5-7 per cent – world); and class corrective maintenance constitutes 57 per cent of the total downtime (versus , 20 per cent – world class). This suggests that the system is more reactive than proactive.
Shortcomings of these PM measures in place are evident through: ● frequent equipment stoppages owing to failure; ● several unexpected major breakdowns; ● too much maintenance overtime hours; ● startup failures after carrying out planned maintenance; ● monthly breakdown hours exceeding planned maintenance hours; ● unavailability of spares when needed; and ● the two main lines simultaneously down.
Gap analysis
Data that were used in this analysis ware derived from monthly engineering reports. This section considers various factors that compel that the issue of maintenance should be examined. Factors such as production output, sales trends, downtime, availability, and machine utilisation are analysed in order to justify the need to consider maintenance as a way to improve performance.
Production volume input trend
Figure 1 shows the production trend since 2001, which shows that on average, the plant has operated below the designed target as well as below the revised target. This has an effect on output volume, as shown by the production output trend, which was measured by the sales volume in Table I.
Production output trend
Table I shows the corresponding actual sales volume at 46 per cent recovery (yield). It should be noted that the annual average production of 64,878 m3 is approximately
8 per cent (5,322 m3) less than the set target and 24 per cent (20,122 m3) less than the design target of 85,000 m3. This equates to ZAR 6.4 million and ZAR 24.2 million losses in potential annual sales, respectively. A total of 1 m3, on average approximately costs ZAR 1,200. Pricing depended on the quality and size of timber which was produced. The trend shown in Figure 1 and Table I shows a skewed relationship between input and output. This is because the volume rate at which raw materials are processed has been affected by frequent machine breakdowns. Quality is also affected as the machines operate below optimum performance; resulting in more waste being generated, hence material throughput is negatively affected. These findings call for a look at the downtime composition. There is a relation between the section-by-section downtime and the overall plant availability in that when one section is down, the entire process is curtailed. Availability is the proportion of time that the machine is actually available out of the time that it should be available. There were basically five main contributors to the sawmill availability, among other causes and these included;
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Figure 1. Production input
volume trend
Year Actual (m3) Target (m3) Design target (m3)
2001 48,647 65,000 85,000 2002 67,072 72,000 85,000 2003 68,334 72,000 85,000 Table I. 2004 65,458 72,000 85,000 Production volume Average 64,878 70,200 85,000 output trend
200,000 180,000 160,000 140,000 120,000 100,000
80,000 60,000 40,000 20,000
0
Revised target
Designed target
Actual volume
2001
160,000
190,000
128,894
2002
160,000
190,000
147,412
2003
160,000
190,000
150,186
2004
160,000
190,000
143,863
Vo
lum
e (m
3)
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maintenance-related downtime; production-related downtime; material shortages and labour related (absenteeism, sickness, and strikes).
Total downtime compositions It is apparent that maintenance-related downtime is the biggest contributor to low availability that results in the huge discrepancy between target values and actual production values (as illustrated in the sections above); hence, the need for further analysis of the maintenance function.
Section-by-section downtime per month
The section-by-section downtime is shown as percentage contributions to total downtime in Figure 2.
The wet mill (the small log line (SLL) and the large log line (LLL)) contribute 48 per cent of the downtime. Owing to this large contribution, further analysis of the engineering indicators of the two lines (the SLL and the LLL) was carried out and shown in Figures 3 and 4.
SLL and LLL availability and utilisation
Figure 3 shows that the LLL plant availability fluctuates below the target and designed levels. This is also the same trend exhibited in the SLL plant, as shown in Figure 4. Figure 5 shows that plant utilization is not optimum, but does fluctuate with time.
Table II shows the total number of breakdowns per trade (mechanical, electrical, and saw-doctoring related). Saw doctors are responsible for maintenance of cutters and saws, as well as other ancillary equipment. Four months of analysis revealed that most breakdowns (68.4 per cent) are mechanically related, which suggests a need to re-work the PM program,
Figure 2. Section percentage
Boilers
Moulders
Drymill
Kilns
F/J
SLL
LLL
downtime contributions 0 5 10 15 20 25 30
% of total downtime
Figure 3.
LLL – plant availability trend
100
95
90
85
80
75
70
Months (2004/5)
Actual Set target Achievable
Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sept
Avai
l (%
)
Pla
nt
sect
ion
giving the mechanical section special attention. Table III divides the downtime into two (planned maintenance contribution versus breakdown maintenance), to portray a picture of the effectiveness of the current PM program. The WCM standard is according to world-class target of . 97 per cent (Ahmad and Benson, 2007).
Table III shows the relationship between planned and breakdown maintenances. Planned maintenance was compared to total maintenance time by using the following formula:
% Planned ¼ planned
planned þ breakdown
Figure 6 shows the overtime trend. The overtime averages 285 hours per month, which is fairly high. Overtime hours are directly related to maintenance work and are indicative of unplanned maintenance, hence the fluctuations.
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100
95
90
85
80
75
70
Months (2004/5)
Figure 4.
SLL – plant availability trend
95
90
85
80
75 Figure 5.
70 Percentage utilisation Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sept
Total Mechanical Electrical Saw
LLL
SLL LLL
SLL LLL
SLL LLL
SLL
June 82 67 64 47 7 9 11 11 July 85 48 49 35 12 4 24 9 August 61 50 41 34 5 7 15 9 September 66 48 46 31 12 6 8 11 Table II.
294 213 200 147 36 26 58 40 Total number Total 507 347 62 98 of breakdowns
Actual Set target Achievable
Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sept
% u
tili
sati
on
Av
ail
(%)
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Table III.
Planned work contribution to downtime
Note: aOctober 2004 and February’s high PM minutes are owing to boiler numbers 1 and 2 which are on shutdown during these months
Figure 6. Artisan overtime trend
Overall equipment effectiveness Results of monthly equipment availability, utilisation, and quality rate, respectively, were tabulated and trended as presented in Table IV. The OEE is calculated by using the following formula:
OEE ¼ A £ PE £ Q
A – Availability of the machine: availability is the proportion of time that the
machine is actually available out of the time that it should be available. Koshal (1993) asserts that for any system the availability is calculated by:
A ¼ Possib le Operati ng Time 2 D owntime
Possible Operating Time
450
400
350
300
250
200
150
100
50
0
Overtime
Ho
urs
Months (2004-2005)
Planned maintenance (min)
Breakdown maintenance (min)
Planned (%)
WCM standard (%)
October 26,605a 24,754 52 . 80
November 13,450 20,621 39 December 1,440 12,479 10 January 15,601 23,201 40 February 23,741a
25,835 48 March 21,605 22,248 49 April 15,880 20,713 43 May 13,310 17,843 43 June 11,416 18,491 38 July 12,915 18,344 41 August 30,450 12,709 71 September 9,690 12,544 43 Average 13,716 18,791 43
Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sept
246 278 263 266 302 355 322 174.5 282 288 405 238
Metric Months (2004-2005) Equipment availability (%) Equipment utilisation (%) Quality rate (%) OEE (%)
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Table IV. Overall equipment
effectiveness
PE Performance rate; this is the rate at which parts are produced divided by the machine capacity.
Q Quality rate: this is the percentage of good parts out of total produced sometimes called “yield”.
Mather (1991) states that measurement is worth mentioning because the data that will be generated from applying an effective maintenance program will allow for companies to look further at how their program is functioning than they previously could. Hence, the OEE value of 64.6 per cent as a maintenance indicator further justifies that maintenance should be investigated as this low value that can be improved upon. There is poor equipment OEE at the company owing to absence of a proper maintenance management policy and strategy.
Pilot RCM team As outlined above through analysis of various indicators, it can be seen that there was a need to embark on an RCM exercise to improve the maintenance function. A pilot RCM team was set up. All in all the team had five people. The pilot RCM team was established, which comprised selected craft personnel (a key operator, a mechanical artisan, an instrumentation and electrical artisan supported by the RCM champion, and a maintenance analyst). The approach that was followed was previously illustrated under “The RCM logic and approach” section.
RCM – system analysis process
Step 1: System selection and information collection
Maintenance costs for the wet mill (SLL and LLL) were obtained from the maintenance planning office and plotted as factored costs, as shown in Figure 7. The maintenance costs were allocated according to sections, namely; frame-saws (F/s), board edgers (B/e), chip cutters(C/c), and mill rippers (M/rip). The sections that did not fall within the above-named categories were grouped together as “other”.
Wet-mill factored cost failure data. Application of the Pareto principle (Figure 7) leads to a conclusion that frame saws (F/s) and board edgers (B/e) are the vital few,
October 83 90 88 65.7 November 80 82 92 60.3 December 83 83 90 62.1 January 89 86 91 69.6 February 84 80 93 62.5 March 81 81 94 61.7 April 86 83 95 67.8 May 88 81 95 67.7 June 86 80 93 62.6 July 88 80 91 64.6 August 90 84 90 68.1 September 89 77 91 62.4 Average OEE/month 64.6
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Figure 7. Wet mill cost failure data
40
35
30
25
20
15
10
5
0 F/s B/e C/c M/rip Other
which are responsible for a bulk of the costs. The board edging (B/e) system was selected as the candidate for the pilot RCM analysis because of its simplicity, as compared to the frame saw (F/s) system as an illustration tool. The decision was also based on the criticality of these machines in meeting throughput, quality, and delivery schedules. The following section further explores the board edging system.
The edging system. There are four-board edgers in the plant, two for the frame line (LLL) and two for the chipper line (SLL). With respect to sawmilling, board edgers have a primary function of removing wane edges and providing the width to the sideboards. They are critical, since they define the final quality of the product prior to drying. They are also critical because once one board edger fails any option which is chosen to bypass it, impacts heavily on the final yield or recovery. About 1 per cent loss in recovery for a
mill, which has output of 72,000 m3 at 46 per cent recovery represents an approximate net loss about R1.9 million per year. All four edgers are the same design, namely,
the Finnish made Kallion Konepaja SH 100/300 board edgers. The edger is designed to produce the following sizes: 76, 102, 114, 152, 200 and 300 mm and pieces up to 38 mm thick.
A typical board edger can be viewed as comprising three major systems, namely:
(1) Material conveying system is that which feeds the partially processed board and conveys them out of the machine.
(2) Chipping and edging system is where that main function of removing the board wane (edging), hence giving the width is afforded.
(3) Utility system is that which supplies electricity and hydraulic power.
The conveying system can and has been treated separately for the entire plant (that is waste conveyors and board conveyors). The same can be done with the utility system. The RCM analysis targets the chipping and sawing system.
Step 2: system boundary definition
The boundary for the chipping and sawing will follow the format and content which are shown in Figure 8. The separately identifiable systems that were used are those that were established in the design phase of the plant. For example, the waste conveying system was taken as a separate entity, as was the board conveying system.
38.2
28.3
16.1 13.6
3.8
% o
f to
tal
cost
Rotating
AC shaft
Wane board
AC power
Set point control
Hydraulic oil RCM in a chipping
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Driving Cutting Controling 215
Heat
Chips
Heat
Signal
feedback
Heat
Figure 8. Edged boards Hydraulic
oil to tank Functional block diagrams
Step 3: system description and functional block diagram
Figure 8 shows the functional block diagram for the chipping and sawing system. This shows that the subsystem can be conveniently divided into three functional subsystems namely, driving, cutting, and size variation control systems. Since this system is not too complex it will be addressed as just one entity, namely, the chipping and sawing subsystem.
Functional block diagram. Figure 8 shows the functional block diagram with the in and out interfaces as well as the crucial interconnecting interfaces. Step 1 (above) of the RCM process described these systems in more detail.
“Equipment” history the objective here is to recall and acknowledge past failures that occurred to the system and how they were solved.
Step 4: system functions and functional failures
Information developed in Steps 1-3 is used to formulate specific functions and functional failure statements as illustrated in Table V. Certain basic functions and functional failures have been grouped to avoid repetition. Instead of specifying each and every shaft on the system, shafts, in general, are considered to have one primary function.
Step 5: failure mode and effects analysis
Here the components’ failures which have potential to defeat the principal objective of the preserving function are analysed. Results of the analysis were used to develop the FMEA, which is shown in Table VI. The last column in Table VI indicates whether that particular failure can be taken to the next step of the LTA. The functional failure number (FF no.), failure mode number (FM no.), and the failure cause number (FC no.) are useful for traceability particularly when there is a computerised maintenance management system.
Step 6: logic (decision) tree analysis
Step 6, further screens failure modes using a logic (decision) tree. Each of the failure modes with a yes in Step 5 was fed to this RCM logic tree. Campbell (1999) illustrates how the LTA is executed. The output of this effort is shown in Table VII. This decision process is identified for each failure mode in one of the four bins:
Fn no.
FF no.
Component(s)
Function
Functional failure
1.1 1.11 Electric motor(s) To supply the electrical energy required to drive the whole Fails to start
1.12 system Current and voltage flow is erratic (not
constant) 1.2 1.21 Grooved pulley(s) Holds the V-belts for max power of transmission Fails to hold the V-belts firmly (allows slip)
1.22 Fails to turn with the motor shaft 1.3 1.31 V-belt(s) They transmit power at the required torque Fails to transmit power
1.32 Power is transmitted at a torque less than the
desired 1.4 1.41 Shafts(s) Transmits power smoothly by holding components in motion Fails to transmit power
1.42 which are either moving or stationary 1.5 1.51 Bearing(s) Permits connected members to rotate relative to one another Shafts cannot turn
Shaft cannot turn at the desired speed 1.6 1.61 Bushes Supports sliding parts Cannot allow the sliding effect
1.62 Allows metal to metal contact 1.7 1.71 Anvil Stabilizes the board being cut Board moves during cutting
1.72 Board shatters during cutting 1.8 1.81 Pulley cover(s) Covers the pulley and the linking V-belts to avoid interference Cover out of position 1.9 1.91 Equalising arm(s) Links the sliding shaft assembly to allow the two cutter One side is slower or faster than the other
assemblies to move precisely the same distances at exactly the
same time 2.0 2.01 Hydraulic cylinder This linear actuator moves the shaft in a straight line No motion at all
2.02 (horizontally) in and out Moves slowly and sticks 2.1 2.11 Servo valve This high-accuracy valve remotely and precisely control fluid Fails to action signal from the PCB CADS –
flow (pressure) hence speed and force via an electrical no signal at all
2.12 Partially actions the signal 2.2 2.21 4/2 way DC valve This ordinary directional control valve is used to regulate flow Fails to limit or allow flow 2.3 2.31 Three-way pressure reducing Used to regulate pressure and limit it to 130 bars Pressure goes above 130 bars
2.32 valve Fails to allow flow 2.4 2.41 PCB CADS (controller, Takes the error signal and generates the control signal required No signal at all
“brains”) to drive the actuator Sends an incorrect signal 2.5 2.51 Transducer Provides an accurate position of the control rod through its travel No signal at all
by converting electrical energy to mechanical Control rod stops at the wrong position
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Table
V.
Fun
ction
s and
fun
ctional
failure d
escriptio
n
FF Comp. Comp. FM Failure effect no. no. description no. Failure mode FC no. Failure cause Local System Plant LTA
1.11 01 37-kW electric motor
0101 Bearing seizure 01011 Burnt motor Motor inoperative Cannot set the cutters in motion
Reduced throughput
Yes
1.11 01 37-kW electric motor
0102 Motor fails to start
01022 System locked, no power supply
Motor inoperative Cannot set the cutters in motion
Reduced throughput
Yes
1.11 01 37-kW electric 0103 Motor fails to 01033 Insulation aging; motor Drive system Cannot set the Reduced Yes motor start short/ground inoperative cutters in motion throughput
1.12 02 V-grooved pulley
0201 Belt slipping 01033 Age/wear out Slip and damage to belt
Cannot set the cutters in motion
Reduced throughput
Yes
1.12 02 V-grooved pulley
0202 Belt slipping 02011 Corrosion/debris causing abrasion
Slip/damage to the belt
Poor-quality cut due to erratic speed
Reduced, quality throughput
Yes
1.13 03 V-belts 0301 Broken belts 03011 Wear out/age misalignment
No drive Cannot set the cutters in motion
Reduced throughput
Yes
1.13 03 V-belts 0302 Slack belt (no tension)
03022 Loose mountings misalignment
No drive Cannot set the cutters in motion
Reduced throughput
Yes
1.14 04 Saw drive shaft
0401 Shaft vibration, excessive noise
04011 Bent or broken misalignment
Quick loss of tension in cutters
Machine running noisily
Unsafe/poor quality
Yes
1.14 04 Saw drive shaft
0402 Age/use, misalignment
04012 Worn-out shaft Motor inoperative Cutters fail to achieve a straight cut
Poor-quality product
Yes
1.15 05 Bearings 0501 Bearing seized 05011 Lubrication deficiency/ age, misalignment
Shaft cannot turn Inoperative machine
Reduced throughput
Yes
1.15 05 Bearings 0501 Overheating 05021 Lubrication deficiency/ age, imbalance
Bearing condition Deteriorates
Unsafe machine due to vibration
Poor-quality product
Yes
1.15 05 Bearings 0501 Overheating 05031 Worn-out bearing journal, excess torque
Vibration Unsafe machine due to vibration
Poor-quality product
Yes
1.16 06 Bushes 0601 Worn out 06011 Age/not properly installed
Shaft cannot slide in and out
Board sizing erratic
Reduced, quality throughput
Yes
(continued)
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Table
VI.
Failu
re mod
e and
effects analy
sis
)
FF Comp. Comp. FM Failure effect no. no. description no. Failure mode FC no. Failure cause Local System Plant LTA
1.17 07 Anvil 0701 Out of position (unstable)
07021 Loose cap holding screws
Saws easily get blunt Board edging not smooth
Reduced throughput
Yes
1.18 08 Pulley covers 0801 Constantly moving out of position
08011 Broken/loose holding bolts
System exposed to foreign objects
Noisy machine Unsafe Yes
1.19 09 Equalising arm
0901 Stuck 09011 Seized interlinking bearing
Cannot actuate No size variation Reduced, yield throughput
Yes
1.20 10 Hydraulic cylinders
1001 Cylinders leaking
10011 Broken seal Cannot actuate Machine cannot select sizes
Reduced, yield throughput
Yes
1.20 10 Hydraulic 1002 Overheating 10021 Worn internal Failure to hold Erratic response to Reduced, yield Yes cylinders components pressure – bypass sizing command throughput
1.21 11 Servo DC valve
1101 Valve sticking (open/closed)
11011 Worn-out seals/internal Failure to regulate filter clogged with dirt/ flow no signal
Failure to control sizes
Reduced, quality throughput
Yes
1.21 11 Servo DC valve
1101 Seized 11021 Age/worn-out seals Failure to regulate flow
Failure to control sizes
Reduced, yield and throughput
Yes
1.22 12 4/2 way DC valve
1201 Valve sticking (open/closed)
12011 Age/worn-out seals Failure to regulate flow
Failure to control sizes
Reduced, yield and throughput
Yes
1.23 13 Three-way 1301 Seized 13011 Age/worn-out Pressure too high and System None Yes pressure reducing valve
components can cause further problems
deterioration and unsafe
1.24 14 PCB CADs 1401 Signal loss 14011 No power, CAD not Failure to send signals Failure to give Reduced, yield Yes inserted home, dry joints on PCB
to servo sizes and throughput
1.24 14 PCB CADS 1401 Signal loss from 14021 Age/burnt (ICs, Failure to send signals Failure to give Reduced, yield Yes control resistors, fuses) to servo sizes throughput
1.25 15 Transducer 1501 Control rod 15011 Age/worn-out internal Incorrect positions Incorrect sizes and Reduced, Yes stops at the wrong position
components and failure to hold at those points
constantly changing sizes
quality throughput and yield
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Table
VI.
FF no.
Comp. no. Component description
FM no. Failure mode Evident Safety Outage Category Comment
1.11 01 37-kW electric motor 0101 Burnt motor Yes No Yes B Very costly must be available at all cost 1.11 01 37-kW electric motor 0102 Motor failing to start Yes No Yes B Must be connected quickly 30 min or less 1.11 01 37-kW electric motor 0103 Motor short/ground Yes No Yes B Can shorten electric motor life 1.12 02 V-grooved pulley 0201 Broken/cracked pulley Yes No Yes B Can lead to drive failure 1.12 02 V-grooved pulley 0202 Groove surface No No No C Can shorten belt life
deterioration 1.13 03 V-belts 0301 Broken No No Yes B Can lead to production disturbances 1.13 03 V-belts 0302 Slack (no tension) No No Yes C Can slow production 1.14 04 Saw drive shaft 0401 Bent or broken Yes No Yes B Can be costly production wise, should be
avoided 1.14 04 Saw drive shaft 0402 Worn out No No Yes D/C Main concern is of broken shaft 1.15 05 Bearings (6314 and Nu314) 0501 Seized Yes No Yes B Can be costly production wise 1.15 05 Motor bearings 0501 Overheating Yes No No C Main concern is motor failure to start 1.15 05 Bearings (all) 0501 Overheating Yes No No C Main concern is failure of bearing to
fulfill function 1.16 06 Bushes 0601 Worn out No No No C Gives audible indication 1.17 07 Anvil 0701 Out of position (unstable) Yes Yes No A Can cause heavy vibrations 1.18 08 Pulley covers 0801 Out of position Yes Yes No A Exposes the fast moving V-belts,
a safety concern 1.19 09 Equalising arm 0901 Stuck Yes No Yes B Leads to production disturbances 1.20 10 Hydraulic cylinders 1001 Leaking Yes Yes Yes C Can lead to production disruptions 1.20 10 Hydraulic cylinders 1002 Heating Yes No Yes C Can lead to production disruptions 1.21 11 Servo DC valve 1101 Valve sticks (open/closed) Yes No Yes B Leads to production disruptions 1.21 11 Servo DC valve 1101 Seized No No Yes B Threatens viability of edging operation 1.22 12 4/2 way DC valve 1201 Valve sticks (open/closed) Yes No Yes B Can lead to production disruptions 1.23 13 Three-way pressure-reducing 1301 Seized No Yes No A Too high a pressure threatens safety
valve (130 bars) 1.24 14 PCB CADs 1401 No response to command Yes No Yes D/C Should be avoided as it heavily
interrupts production 1.24 14 PCB CADS 1401 Control units No No Yes B It is a very costly incident and should be
avoided 1.25 15 Transducer 1501 Control rod stops at the Yes No No C Leads to production interruptions
wrong position
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(1) Bin A – safety related;
(2) Bin B – outage related;
(3) Bin C – economic related; and
(4) Bin D – hidden (failure that is evident to the operator).
When the LTA process was concluded, each failure mode which passed onto the LTA was classified as A, B, C, D/A, D/B, or D/C.
In the RCM analysis process, Bin A (safety) is priority number 1 (safety comes first); Bin B (outage) is priority number 2 and Bin C is priority number 3. The priority numbers were allotted based on the RCM team’s perception. In summary, the LTA revealed the following categories: {A or D/A ¼ 3}, {B or D/B ¼ 12}, and {C or D/C ¼ 10}. As suggested in the RCM process analysis, the initial action is to relegate the category C or D/C failure modes to RTF status, and then to carry out a sanity check in Step 7. The critical failures are automatically passed on to Step 7 (preventive task selection) which is shown in Table VIII.
Step 7: task selection
RCM system analysis efforts, to this point, were directed at delineating those failure modes where a preventive task gives the biggest return on the investment which is made. Step 7 tests the critical failure modes for applicability and effectiveness by using a task selection roadmap. This roadmap was highly useful in logically developing the candidate preventive tasks for each failure mode. A sanity check was conducted to argue the validity of sending failure modes to RTF in Step 6. Although a formality of completing the form was not done, by going through this stage a rationale, which justifies each failure mode status, was done. It included considering issues such as marginal effectiveness, OEM recommendations conflict, internal conflict of policies, analysis of secondary damage and regulatory constraints. The output of this RCM analysis process is summarised in Table VIII. The following preventive task types are used in the fifth and seventh column:
TD time directed;
TDI time-directed intrusive;
TDN time-directed non-intrusive;
CD condition directed;
FF failure finding;
RTF run to failure; and
A/E age exploration.
Summary of RCM pilot study Availability after implementation of the PM options is shown in Table IX. The table shows a change in plant availability to an average of 82 per cent for the period October 2006 to March 2007. These results show an improvement, however, it should be pointed out that there is a possibility of an even higher availability, since certain recommended tasks would be implemented over a longer cycle period.
FF no.
Component description Failure mode Failure cause
Recommended preventive task Effective information
Selected decision
Estimated frequency
1.11 37-kW electric motor
Burnt motor Bearing seized due to age/misalignment
1. Vibration monitoring 1. Is not considered cost- effective
RTF –
1.11 37-kW electric motor
Motor cannot start
System locked, no power supply
1. Periodically check power supply system (TD)
2. RTF
1. Equipment history indicate no such failure
RTF –
1.11 37-kW electric motor
Motor short/ ground
Insulation aging 1. RTF Is the most practical option to follow
RTF –
1.12 V-grooved pulley
Broken/cracked pulley
Age/wear out 1. Periodically check pulley condition (TD)
2. RTF
2. RTF is the only most practical option
RTF –
1.12 V-grooved pulley
Groove surface deterioration
Corrosion/debris causing abrasion
1. Periodically remove and check pulley condition (TD)
2. RTF
2. RTF is not preferable because it causes secondary damages
TDI Six-months A/E
1.13 V-belts Broken belts Wear out/age excessive torque, misalignment
1. Periodically remove and check belt condition (TD)
2. RTF
1. Is the most effective because it avoids production interruptions
TDN Two weeks
1.13 V-belts Slack (no tension)
Loose mountings misalignment
1. Periodically check tension and tightness of mountings
2. RTF
1. Is the most effective because it avoids production interruptions
TDN Weekly
1.14 Saw drive shaft
Bent or broken Age, fatigue, vibration, excessive torque
1. Vibration monitoring (CD)
2. Periodically check condition
3. RTF
2. Is the most effective way CD Yearly A/E
(continued)
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FF no.
Component description Failure mode Failure cause
Recommended preventive task Effective information
Selected decision
Estimated frequency
1.14 Saw drive shaft
Worn out Use/excessive torque, misalignment
1. Inspect for signs of deterioration (CD)
2. RTF
2. Is the most effective way CD Yearly A/E
1.15 Bearings (6314 and Nu314)
Seized Lubrication deficiency, fatigue, imbalance, misalignment
1. Monitor temp (CD) 2. Check for bearing
condition and lubricate (TD)
2. Is the most effective option
TDI 18-months A/E
1.15 Motor bearings Overheating Lubrication deficiency/ age excessive thrust
1. Listen for increase in noise level (CD)
It is the most effective way CD Six-months A/E
1.16 Bushes Worn out Age/not properly installed
1. Periodically check condition (TD)
2. RTF
1. It has less secondary damages
TDI Yearly
1.17 Anvil Out of position (unstable)
Loose holding cap screws
1. RTF 2. Check level to assure
cap screw tightness (FF)
2. Is the most applicable FF Two weeks
1.18 Pulley covers Out of position Broken/loose holding bolts
1. RTF 2. Check tightness of
bolts
2. Is the most effective FF Two weeks
1.19 Equalising arm
Stuck Seized interlinking bearing
1. Check bearing condition
2. RTF
1. Most effective CD Six months
1.20 Hydraulic cylinders
Leaking Broken seal 1. Visually inspect for leaks
2. RTF
1. Most effective FF Daily
1.20 Hydraulic cylinders
Heating Worn internal components
1. Check operation temp 2. RTF
1. Most effective CD Three months
(continued)
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Table
VIII.
FF no.
Component description
Failure mode
Failure cause Recommended preventive task
Effective information Selected decision
Estimated frequency
1.21 Servo DC valve Valve sticks (open or closed position)
Worn-out seals/internal filter clogged with dirt/ no signal
1. Remove inspect and clean
2. Monitor valve temp
Valve cannot be opened without special equipment
RTF –
(CD) 3. RTF
1.21 Servo DC valve Seized Worn-out seals 1. Remove, inspect and clean
2. RTF
Valve cannot be opened without special equipment
RTF –
1.22 4/2 way DC valve
Valve sticks (open or closed position)
Worn-out seals 1. Check for leaks and oil level (FF)
2. RTF
2. Cost-effective RTF –
1.23 Pressure Reducing valve (130 bars)
Seized Age/worn-out components
1. Remove inspect and clean replace, if necessary
2. RTF
1. Is most appropriate since its safety
TDI Yearly A/E
1.24 PCB CADs No response to command
No power, CAD not inserted home, dry joints on PCB
RTF 1. With electronic devices there is no way to detect failure
RTF –
1.24 PCB CADS Control units Age/burnt (ICs, resistors, fuses)
RTF 1. Failure is random (not detectable)
RTF –
1.25 Transducer Control rod stops at the wrong position
Age/worn-out internal components
1. Check transducer operation
2. RTF
1. Is more applicable TDI Yearly A/E
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The recommended PM tasks which are highlighted in Table VIII were applied to the edging plant. The results of preliminary application of the tasks are shown in Table IX. A pilot application was conducted before a plant-wide RCM exercise in order to have preliminary insight of what RCM would possibly offer. Table X presents a summary of the system analysis results for each component in the chipping and sawing system of the edging plant following pilot implementation of RCM PM tasks. This was obtained after pilot implementation of the PM tasks.
It can be seen that TD tasks amount to 29.2 per cent in the RCM-based program, as compared to 41.5 per cent of the current program. This suggests that the current program is more intrusive than the RCM-based one. The high FF percentage also suggests that the current PM program is biased towards inspections with the CD and TD tasks being carried out, but not formally approved and documented. Certain FF tasks require that one should dismantle components in order to obtain a clear picture. A total of 66.5 per cent of the current PM program can be viewed as intrusive or interruptive. The fact that condition based (CD) is 8.5 per cent with the current program versus 20.8 per cent, further supports this argument. About 25 per cent of the non-specified tasks suggest that the current program, to some extent, is under-maintaining this system. These results show the objectivity of RCM when assigning PM tasks, and the path that should be followed in a function-driven maintenance program.
Concluding remarks The paper illustrates the application of RCM model in order to improve plant maintenance in a chipping and sawmill company. Adoption of RCM can reduce downtime, whilst reducing reworks to a minimum. The pilot application of RCM showed that plant availability can indeed be improved when the appropriate maintenance
Availability (%)
Months (2006-2007) Week 1 Week 2 Week 3 Week 4 Average Std. Target (%)
October 75.00 74.87 79.01 72.55 75.63 7.90 97 November 75.35 78.20 78.79 76.39 78.13 5.39 December 81.11 78.13 79.56 84.56 80.84 2.68 January 85.69 86.01 88.22 86.33 86.81 3.29
Table IX. February 81.58 76.74 89.28 89.34 88.74 5.22 Availability after March 89.45 90.36 85.32 92.19 90.96 7.44 pilot RCM Mean average 82.00
RCM based Current
Item no. Task profile n % n %
16.5 41.5 25
8.5 25
Summary of pilot RCM implementation
– 6 None specified – – 6 25
24 100 24 100
1 Time directed intrusive (TDI) 4 16.7 29.2 4 2 Time directed non-intrusive (TDN) 3 12.5 6 3 Condition directed (CD) 5 20.8 2 4 Failure finding (FF) 3 12.5 6
Table X. 5 Run to failure (RTF) 9 37.5
–
strategies are adopted. The effectiveness of RCM was highlighted by identifying the most appropriate maintenance tasks. RCM has the effect of encouraging proactive maintenance as opposed to reactive maintenance. This can be clearly seen by reduction in FF activities from 25 to 12.5 per cent after implementation of RCM. Significant reduction in TD task was realised as illustrated in Table X, further confirming the fact that RCM implementation saves time as it leads to a less intrusive maintenance program. Unnecessary routine maintenance tasks are eliminated. CD tasks increased from 8.5 to 20.8 per cent, a clear indication that RCM leads to improved understanding of equipment in its operation context. Availability is considered as an important aspect during production and RCM provides a means to close the gap between the cost of doing nothing and the cost of doing something by improving the maintenance function, through objectively specifying the PM tasks. This, to a wider extent, leads to increased profitability and image, both of which should ensure competitiveness. RCM uses strictly defined steps that have procedures to produce auditable outputs. Presently, and to a great extent, society relies on goods and income, which are generated by highly complex factories. Within such systems, risk management has become a crucial practice together with the demand for verification of quality and reliability of products. RCM as demonstrated has the inherent feature of improving plant availability and by embodying principles such as FMEA, criticality of failure can also be ascertained. However, it should be pointed out that the RCM process is very disciplined and logical and without training it can prove difficult. The competence and skill levels among RCM team members is important and cannot be over emphasised; therefore, further training should be arranged if necessary. The team should also be motivated to carry out the task at hand. Plant-wide application of RCM might be involving, but it is beneficial in the long term.
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