Managing Building Services Maintenance Risk with Prediction Theories

Transcription

1 Managing Building Services Maintenance Risk with Prediction Theories K. C. Lam Dept. of Building Services Engineering The Hong Kong Polytechnic University Hong Kong Abstract When building services maintenance solely relies on the experience of the individual engineer, results are often not wholly satisfactory. There are high risk in building services availability. Poor maintenance is a risk. Risk must be assessed and managed before a design is fully implemented. The application of prediction theories (e.g. reliability evaluation of engineering systems) has not yet been widely used in the building services industry. Therefore, the quality of operation and maintenance is still not cost-effective and reliable. As high environmental performance and users expectations have lead to increasingly complex systems, the application of risk assessment, prediction theories and reliability-centred maintenance (RCM) should result in improved quality of building services operation and maintenance. The discussions given should be of great help to both designers and maintenance engineers. Introduction When a building is put into use, its building services have to perform day in, day out for the life of a building. Building services must operate in an efficient and effective manner. Not only must they deliver the necessary environmental conditions, but also, to be considered successful, they must do so reliably and economically. Without effective and efficient M&E services, a building would not function properly. To ensure the required function of the building is maintained all services systems require proper design and maintenance. As services are installed to support the functions to be carried out by the building users, the importance of the functions determines the engineering parameters, such as type, cost, availability of each of the services to perform its designated function, maintenance features, etc. for the design of a specific service. Of these parameters availability is of greatest importance to the user whilst risk, cost and reliability are probably the most important from the point of view of the owner of the building services. The application of prediction theory and Reliability-Centred Maintenance (RCM) has been widely practiced for many years in many industries. Although these methods are not new, their application has not been widely used with the building services industry. With increasing complexity of building services and the need to provide appropriately high up-time at reasonable cost and availability (availability is a function of reliability and maintainability), analysis is of considerable benefit to designers in that system performance can be

2 quantified, the need for component or system redundancy clearly established, and alternative designs compared. Also, weak links in the system can be identified and removed. In a nutshell, the key challenges facing modern maintenance engineers can be summarized as follows: To better analyse design and maintenance risk To select the most appropriate techniques with a much greater emphasis on reliability and maintainability To give the most cost-effective solutions The industry should take a deeper look at these approaches in comparison with the traditional practices. As thoughts on the direction both the designer and maintenance practitioners must go, this paper seeks to provide an overview of how risk analysis, reliability and RCM could enhance the quality of building services design and maintenance. Designing Maintenance Good maintenance management is not only about reducing the cost of maintenance. Maintenance is an integral part of the business activity, since it ensures the availability of important assets (i.e. building and its functions). Maintenance is done to prevent loss of core business (due to a failure or breakdown in any of the support activities like M&E services) and to prevent the excessive cost of catastrophic plant failures and a number of undesirable outcomes (e.g. customer dissatisfaction, non-compliance with legal requirements, health and safety problems, increase of energy and environmental loss, etc). To ensure that the physical assets continue to perform to organizational need, the use of risk analysis and availability studies should be of great help when one selects the services systems with good maintenance in mind. Effective maintenance management minimizes the costs associated with the non-availability of an engineering service. Therefore maintenance needs should be design-out (i.e. Designing-out) as much as possible to achieve the required level of reliability. The reduction of risk of failure is an ideal solution. The use of designing-out can put a major responsibility on the design engineer. In most building services applications it is unlikely to be encountered as the only option, but the client should be aware of it. This calls for a clear understanding of: The business function of the organization The relationship between the M&E services and the business function The function of the M&E plant and the required performance The implications of failure How the plant could fail? The probability of failure How failure could be pre-empted or stopped, and how the effects of failure could be limited? Are standby facilities available? The complexity of the design and ease of maintenance What level of use is envisaged? What type of maintenance is envisaged? What level of technical experts expertise will be available and how will it be organized?

3 What financial resources will be available for proper maintenance? This series of questions could be elaborated further, but serves to identify the areas needing careful consideration when assessing design and maintenance requirements. They also lead on to how to satisfy these requirements. To ensure that the aforesaid are fully considered a structured approach is required. Obviously, all these factors can only be addressed at the design stage rather than during or after installation. It follows that an engineer should make good use of the concepts of FMEA (Failure Mode Effects Analysis) and FMECA (Failure Mode Effects and Criticality Analysis) to provide the desired reliability and availability of building services. For best maintenance management, a designer should consider services firstly in terms of systems to support the business function and secondly on an individual basis (i.e. the plant item). Hence, we need to think about function analysis (i.e. what it is we can try to do and also to identify the associated cost or worth), criticality to the building and the use of maintenance engineering technique. This systematized approach should enable us to seek out the best functional balance between the cost, reliability and performance of a design. Risk Management There is an element of risk in all our lives including all M&E services that serve our buildings. Failure is the non-availability of the service when it is required. The loss of a service i.e. failure must be considered in the light of the resulting effect on the building use. Failure of a system may have little or no direct impact on the business or environment; likewise it may also be completely the reverse. At least there will be indirect consequences. The loss of each service must be assessed and alternatives such as preventing the occurrence or reinstating the services within a permissible time must be evaluated to determine the most acceptable solution. Reliability will be influenced by actions dictated by the nature of the risk arising from failure. The greatest challenge facing the building services engineers is to manage and eliminate risk. This requires the application of risk assessment techniques. Two approaches can be used. The quantitative approach is extremely theoretical and requires a high degree of knowledge and becomes very complex for many building services designers and maintenance engineers. However, a more practical approach (qualitative) to reliability and risk can be used but this is not a rigorous and quantifiable approach. Nonetheless, a simple approach is essential during the beginning of performing risk analysis. In a qualitative way, we can develop a table showing the type of building services, its failure mode; its possible causes; its potential consequences (low, L, medium, M, and high H); the frequency (L, M, H); the severity/impact of the risk (L, M, H) and finally what would be the typical safeguards.

4 Example: medical gas pipeline system Failure mode Possible causes Potential Loss of supply Loss of supply or control Frequency Risk Typical safeguards consequences Medium Medium Medium Back up supply, distribution network Incorrect or contaminated gas Incorrect supply, bottle or gas contamination High Medium High Quality assurance, connector standards The assessment matrix is also based on the following combinations of possible consequence and frequency. Frequency High Medium Low Negligible Consequence Low Medium High Medium risk High risk Very high risk Low risk Medium risk High risk Low risk Low risk Medium risk Very low risk Low risk Low risk The recommended follow-up management (e.g. back up supply in our example) action(s) for each system risk would include improved design such as using a standby facility or more reliable system layout and better maintenance technique (e.g. condition-monitoring system). We can further improve the matrix by using a modified model like the figure shown below: Details Technical complexity Effect on building/user Consequences General reliability Importance Summation of importance times likelihoods (0 to 1) and severity (L to H or 1 to 5). ( ) x 0.9 x 5 = 85.5 In this risk assessment system, one has to make a subjective judgement for the calculated risk level such as 0 to 45 is low; 46 to 74 is medium and over 75 is high. In our example 85.5 means very high risk and consideration should be given to better design and maintenance. For best result, the importance index may include the frequency of the failure of a system, this should give a simple and effective aid to decision making. In summary, designing services systems and their maintenance based on a risk-based concept is useful but the analysis should be supplemented by reliability calculations (see calculations for reliability included in this paper). Reliability-Centred Maintenance (RCM) Modern plant and equipment is now so complex and the consequences of failures are so serious that it is no longer possible to develop viable physical asset management strategies using traditional approaches. Many analytical techniques are available but RCM avoids the problem of analysis paralysis by

5 combining the qualitative and quantitative aspects into a single, coherent maintenance framework which gives a high level of equipment reliability with the cost effective use of maintenance resources. The basic RCM process requires the following steps: Identify important items with respect to maintenance Obtain appropriate failure data Develop fault tree analysis data Apply decision logic to critical failure modes Classify maintenance requirements Implement RCM decisions Apply sustaining-engineering on the basis of field experience RCM recognizes failure-finding, improved design for reliability and more cost-effective maintenance. Understanding Maintainability Maintainability is an inherent characteristic of system or product design. It pertains to the ease, accuracy, safety, and economy in the performance of maintenance actions. Hence, a building services system (or product component or sub-system) should be designed such that it can be maintained without large investments of time, at the least cost, with a minimum impact on the environment, and with a minimum expenditure of resources (e.g. personnel, materials, facilities, and test equipment). Maintainability, as a characteristic of design requires the consideration of many different factors, involving system design, performance characteristics, reliability, human factors, safety, logistics, quality, reconfigurability, flexibility, testability, producibility, disposability, environmental considerations and economic factors. An easily maintainable system is costly in its initial procurement, but it will reduce the follow-on sustaining maintenance and support costs. Nonetheless, if an equipment item is packaged with complex components, inadequate accessibility provisions, sophisticated built-in test and condition monitoring devices, all these can add complexity and increased failure potential and, therefore, the reliability of the system is lower. During the user use phase, a highly maintainable system can be repaired rapidly (Sometimes, it is better to exchange items in lieu of repair if the system is packaged with interchangeable components), with a minimum expenditure and supporting resources, without causing detrimental effects on the environment, and without inducing additional faults in the process. In any event, incorporating reliability and maintainability characteristics into a design leads to a reduction in overall life-cycle cost. Review of Terms/Factors For completeness a brief introduction is given to some important terms which are needed for system design and planning of maintenance. The objectives of a system design with maintenance analysis should provide

6 useful information such as identification of problem areas; the causes of failure; the most critical components from an operational/safety viewpoint; the vulnerability of a system; the adequacy of the design; the right planned maintenance and replacement strategies. Most importantly, from the analysis it can be determined which components/sub-systems should have a particularly high reliability, be backed up by redundant components, be derated, be provided with special designs with other components/sub-systems or be subject to special maintenance procedures. All in all, the use of availability and reliability analyses allows suitable design and maintainability to be studied in detail. In addition, the designer can ensure that the different services are integrated in a logical manner as a total system thus providing the desired up-time for the total system. Maintainability requires the consideration of many different factors, involving all aspects of a system (e.g. conceptual design, system design, production, installation, T&C, system operations as well as system retirement and life-cycle cost), and the measures of maintainability often include a combination of the following: 1. Maintainability (M) The word, maintainability, refers to the concept of being able easily to maintain/restore an item to a state in which it can perform its required functions. Maintainability is another measure of quality, and is normally expressed as mean time to repair (MTTR). 2. Mean Time to Repair (MTTR) This is a measure of the duration of repair time. 3. Availability (A) under steady state The word, availability, refers to the concept of whether an item will be ready when it is needed. Availability is determined by the reliability, maintainability, logistics, and administrative policy. The concept can be expressed as the ratio of uptime to the total time of intended operation. Specifically, uptime is the amount of time that the system is usable, and the total time is the amount of time that the system is needed. Down time is the time during which an element cannot perform. Total time is the sum of uptime and downtime, as shown in the following equation. A = uptime uptime + Downtime NB In designing for reliability the operational availability concept should be used (see use of reliability calculations) 4. Reliability (R)

7 The word, reliability, refers to the concept of being able to depend on something. Hence, it is defined as the probability of an item performing its required function for a stated interval under stated conditions. Reliability is normally expressed as a probability. For example, a device that fails randomly in time but once a year on average, will have a probability of failing (P F ) in any one particular week of 1/52. i.e., P F = Conversely the probability of success (P S ) i.e. not failing is 51/52 = which is the same as 1 P F. Mathematically these expressions can be expressed as:- t / T P S = e NB There are many distributions, say Welibull Distributions, Lognormal distribution can be used to express time to failure. and P F = 1 e t / T where t = the time interval during which success is required T = mean time between failure of the device System reliability is improved if each element is as reliable as possible and if simple layouts using few different parts are planned. 5. Mean Time Between Failure (MTBF) This is a measure of the frequency of failure for repairable system, defined as running time/number of failures. The reliability of large installation can be quoted as a single figure, but the MTBF or maximum failure rate should be quoted. 6. Mean Time to Failure (MTTF) MTTF is used for non-repairable system. This is the mean value of the probability density function. Therefore MTTF = R ( t). dt. If the mean failure rate is constant, then MTTF = λ 1. 0 Use of Reliability Calculations If a product s life can be approximated by the exponential distribution: Failure rate: h (t) = λ (N.B. To apply the mathematical prediction model to the probability of failure requires carefully collected and collated statistics. Published data have inherent limitations) Life distribution: f ( t) = 1 R( t) = 1 e λ t Reliability at t: R( t) = e λ t

8 Mean life: MTTF ( or MTBF) = 1 λ NB In systems where the components are repaired, the meantime to fail becomes mean time between failures Example: A certain type of building services component is known to have life exponentially distributed with a constant failure rate of 0.03 x 10-4 failures per hour. 1. The probability of a given component will last beyond 10,000 hours is R( t) = e λ t R(1000) = e = = e x10000 i.e., the probability the component survives beyond 10,000 hours is The MTTF of the component is 1 1 MTTF = = = 333, 333 hours 4 λ ( ) i.e., the average life of the component is 333,333 hours 3. The reliability at MTTF is R(333333) = e = x i.e., only 37 percent of the component (or population) will last beyond the average life. 4. The recommended design life of this component if the reliability at design life has to be at least 90% R (T) = 0.9; i.e., e T = 0.9 Taking logarithm of both sides, T = T = 35,133 hrs. (Note: If design life has to be chosen with reasonably large reliability it will have to be much smaller than the MTTF. The figures obtained from the above calculations can be used for planning maintenance) It should also be noted from the above calculations that the first data needed Total fails is the failure rate which can be obtained from. Total operating time When this information is know, the MTBF can be determined by the simple formula Total accumulative functioning time of a system MTBF = Number of failures observed To the user, the most apparently useful factor is the availability of an item, which is the probability of that item, at any instant in time, will be available.

9 The factor contributing to availability (up-time) is the reliability (time to failure) and maintainability (time to return to service). In brief, the steady state availability of a system without redundancy is: up time A = up time + down time MTBF = MTBF + MTTR NB In designing for reliability, the operational availability concept should be used. The correct definition of A o should be: Mean uptime/(mean uptime + mean corrective or repair time + mean logistic delay time) e.g. If the MTBF is 2 years and the MTTR is 6 hours (assuming an active repair time of 2 hours and a logistics delay time of 4 hours), the availability will be 99.97%. A system availability of 99% is equivalent to a down time of nearly four days each year. Availability can be improved by increasing the MTBF or reducing the MTTR, it can also be improved by including redundant components. Influence of Reliability Reliability at least cost is the basis of the development of an asset strategy. The aim has been to reduce downtime, and as a result, there will be dramatic improvement in availability. Equipment reliability can be considered as the characteristic of design which results in durability of the equipment item or system. This item will then operate successfully for a particular duration of time. Like most evaluation techniques, there is a limitation to the prediction. The approach is based on historical data of general components failure rates. It can only be valid for the conditions under which the data was obtained. The application of the reliability methodology necessitates the availability of reliability data on HVAC equipment, but the information is not readily available and the data will inevitably be coupled with limitations. So the details of the life of plant and equipment cannot give very accurate predictions. Nonetheless, the information provided will (with or without merging several failure data to get a more reliable figure) still be useful for the preliminary prediction. Based on this information, an engineer is able to develop a maintenance plan or schedule. Once the maintenance engineer has obtained sufficient statistical data of the services system for one or two year s time, he can modify some of the predicted schedules to meet the actual systems and the maintenance strategy. In practice, the general conditions for setting a maintenance schedule can be devised if data on the failure rates of the equipment components is known. For example, consider the maintenance requirements for a pump and motor assembly. This installation is required to be available for up to 4160 hours per annum. 20,000 hours of 5 year operation 1. The mean time between failure is ( ) = 4000 hours or 5 failures

10 0.962 years 2. The availability of this pump is A = 4000 hrs hrs = 99.4% 4000 hours for overhaul on an annual basis 3. Reliability is R = e λ t = e = (4160 hrs / year 22.5) 20,000 From the above calculation, in order to improve the performance of the pump, it is necessary to reduce down time (i.e. to improve A) and a reduction of the number of failures on the pump set assembly (to increase R). From this information, it may be judged whether the system should contain a standby pump. The reliability calculation shown above has implications for the way availability and maintainability should be designed as a series of items of equipment. Frequency of Function Tests The importance, or criticality, of a function will be reflected in the required level of availability. 100% A is not impossible, but many engineers do not go for it. This highest level can only be met by the provision of separate item(s) serving the same function (i.e. redundancy). The result of increasing the availability of an item capable of a hidden failure is to ensure that down times are short. This is also achieved by function testing to reveal failures which may have occurred. The frequency of any function test can be estimated by: Time between tests = MTBF Log n (2 Av 1) The availability of an equipment function is directly related to the equipment reliability and the interval test. For instance, if a BS part requires 99% A and has an MTBF of 24 months. The time between tests should be: = 24 Log n (2 x ) = 0.48 months (or 2 weeks) Put another way, the time between tests is 2% of the MTBF for 99% A. With other A, the figures would be: A MTBF Tests % of MTBF months 2 wks months 2 months months 5 months 20

11 The function tests carried out at the various intervals confirm that the required availability is possible. It is also seen if the failure rate is known it is possible to provide a range of availability figures for given test intervals allowing a choice of availabilities and the management of maintenance task. As the time interval between the function tests extends then the level of confidence reduces. Then one can duplicate or triplicate the item of equipment in order to increase the probability of ensuring function availability. In practice, the periodicity of function test is often determined from experience. Clearly, the function test based on calculation is a valuable technique as this will eliminate ineffective and unsuitable maintenance. Calculations for Reliability As discussed earlier (see risk management), risk in relation to design and maintenance can be assessed by using a simple scoring technique. However, quantitative approach is often adopted by many designers. In practice, building services systems are combined in series and parallel. The system reliability can be calculated by the use of simple calculations. (e.g. In series system, we have R = R 1 N and in parallel circuit, we have R = 1 (1 R 1 ) N ). Throughout the system design process, reliability models can therefore be used to aid in the accomplishment of reliability location, the evaluation of alternative configurations, and the accomplishment of reliability prediction. With this technique, risk assessment can be made much easier. The following example (based on the CIBSE Guide Section 9) shows the usefulness of the reliability calculations. For quick reference, the system reliability for various arrangement of plant based on parallel redundant systems is calculated and summarized as below: Arrangement Probability of failure, P Equation for R S System Reliability, R Equipment capacity, % of full load a) Single item b) Two 100% load 0.9@ 2 P P in parallel 200, 100% standby units c) Three 50% load 0.9@ 3P 2 2P in parallel 150, 50% standby units d) Three 33% load units 0.9@ P 3 3P 2 2P full load 0.97 partial load in parallel e) Two 75% load 0.9@ P full load - units 2P P partial load in parallel The benefit of 100% redundancy (arr. b) gives 0.99 reliability. Arrangement (c) still gives 0.97 reliability. Arrangements (d and e) have sharply lower system reliabilities at full load conditions (under partial conditions, the system reliabilities increase substantially as redundant capacity emerges). Obviously, standby redundancy gives higher value of MTBF and, therefore, higher availability. The point being made here is that as more plant is provided to enhance the reliability of the system, so the maintenance commitment is increased. Alternatively, it may be considered more appropriate to provide a more comprehensive maintenance scheme for the services which both eliminates breakdowns and the need for additional standby support facilities.

12 Availability as A Design Tool In designing and installing building services systems the system must provide the functions required by the user and the best value for money for the owner, it is fundamental in providing a solution to an engineering function that the designer can, quantitatively, determine whether a design solution will perform as required and can compare the relative merits of various alternative design solutions. A further example of a simple hot water supply system having two 100% parallel pumps should show the application of analysis and the way to increase the reliability. Data 4380 hours/year operation Failure rate of a pump is x 10-6 hour Calculations per annum 1. Reliability of a pump (R P1 ) 6 ( ) (4380) = e = Reliability of the pumping circuit (parallel circuit with two pumps only and one of the pumps must be successful) = 1 (1 R P1 ) (1 R P2 ) = 1 ( ) x ( ) = (0.985) 3. Probability of failure for the pumping circuit = = Down time for the pumping circuit = x 4380 = 65.7 hours 5. To improve the reliability, a third pump can be used (in parallel circuit) R = 1 (1 R P1 ) (1 R P2 ) (1 R P3 ) = 1 (0.123 x x 0.123) = The probability of failure = = The revised down time will be = x 4380 = hrs. It can be seen from this calculation that the reliability of the hypothetical hot water supply system can be improved by using one more pump. Furthermore, the down time can be reduced from 65.7 to 8.15 hours. However, the weakness

13 on adding a component includes: Increase in capital cost Increase in maintenance needs Increase in space, weight and power needs Hence, this system reliability must be evaluated against the cost of interruption to the hot water system. With this prediction method, a designer can make better analysis with regard to improved design and maintainability. The calculations provided will also enable the client to assess the suitability of the proposed system. Short summary All the examples given have demonstrated the use of prediction theories. Reliability calculations can be used to quantitatively compare different systems. By evaluating the reliability and costs of different solutions to a particular design, it is possible to iteratively develop a system which optimizes the desired reliability, availability and cost. The Use of Reliability Centred Maintenance (RCM) Through Better Prediction Procedures Reliability centred maintenance is a systematic process used to determine what has to be accomplished to ensure that any physical facility is able to continuously meet its designed functions. RCM is an engineered process used to determine the maintenance requirements of any physical asset in its operating context by identifying the function of the asset, the causes of failures, and the effects of failures. RCM advocates condition based maintenance and reassessing the system design. Most importantly, the maintenance process is based on a detailed study of risk, availability, reliability, maintainability and engineering economy. RCM leads to a maintenance programme that focuses preventive maintenance on specific failure modes likely to occur. Any organization can benefit from RCM. However, RCM may be a cost-adding element for some organizations. Conclusions For years maintenance was a craft learned through experience and rarely examined analytically. As higher building services performance requirements have led to increasingly complex system design and maintenance, it is necessary to adopt more engineered based decision support for building services design and maintenance. The current approach to managing building services maintenance is mainly based on engineers experiences and the results are not the best, there will be high risk in building services availability. It is fundamental in providing a solution to an engineering function that the designer can, quantitatively and qualitatively determine whether a design solution will perform as required and can compare

14 the relative merits of various alternative design solutions. Maintenance risk assessment must be carried out in conjunction with the more objective and engineered prediction techniques given in this paper. It is possible to provide a better design and maintenance. This improved way of working should enhance the quality of building services. Whereas reliability needs to be tackled at the design stage, availability of the building service is also dependent on the subsequent maintenance of installed plant. Hence, good design is the first line of defence against poor maintainability and, the management of maintenance after the completion of the physical installation is the final line of defence against inadequate functioning of a building. This paper has given an introduction to the integrated approach with the use of maintenance risk assessment and the prediction theories. This approach to modern day design of building services maintenance is no longer based on assumptions, experience, rules of thumb and fashionable trends. The techniques discussed by the author should facilitate better building services design and maintenance. Bibliography Blanchard, B.S., Dinesh, V., and Peterson, E.L., Maintainability: A Key to Effective Serviceability and Maintenance Management, John Wiley & Sons, Inc., USA, Brister, A., Chancing your ARM, CIBSE Journal, page 27-29, UK, Bromley, A.K., and Pyle, P.C., Specifying the reliability of building management systems, BRE Report, BRE, UK, Guide to ownership, operation and maintenance of building services, CIBSE, UK, CIBSE Guide, Section A9 (Estimation of plant capacity), Moubray, J.M., Reliability centred Maintenance RCM II, Butterworth and Heinemann, Oxford, UK, Robbins, P., Managing risk in device engineering, Health Estate Journal, 27-32, June 2005.