an insight into reliability centred maintenance

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An Insight into Reliability Centred Maintenance

Abstract Time based Maintenance or Preventative Maintenance (PM) is a well renowned and widely implemented maintenance model in the Facility Maintenance (FM) industry, where the aim of this regime is to prevent asset failures before they can lead to any catastrophic events, therefore maintenance activities are performed based on manufacturer’s recommendations or Industry acknowledged maintenance standards but the recent studies in the area of asset management reveals that over 80% of asset failures are random, hence performing fixed or calendar based maintenance is not only inefficient but it also increases the operational and maintenance costs of the facility. This paper aims to address some of the common misconceptions of Preventative Maintenance and the ways to mitigate its drawbacks by introducing more proactive maintenance regime, which is a fusion of Preventative, Reactive and Predictive Maintenance models. Keywords: Condition based Maintenance (CbM), Predictive Maintenance (PM), Reliability Centred Maintenance (RCM)


Authors: [1] Laxmi Vajravel [2] Andrew Dutton

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Background Reliability centred Maintenance (RCM) was first introduced in the early 1960s, as the aeronautical industries were scrutinised over high failure rates of their turbines which resulted in lot of mishaps in mid-air. Faced with this challenge, the industry spent a lot of time and effort to devise a more proactive maintenance model that identifies any and all early symptoms of failures in life critical assets. During this era, there was a common misconception that all the components shall behave in a particular failure pattern and was with respect to its age and all the early failures were blamed on the manufacture’s quality of design, which was true in some cases up until two maintenance research engineers Nowlan and Heap came up with a solution that components within a life critical assets are not bound to a failure pattern, on the contrary, components behaved in six different failure patterns and amongst them, three were age and stress related and others were completely random. They concluded that a significant percentage of components in an asset shall fail randomly irrespective of any preventative measures.

Introduction Reliability Centred Maintenance is a combination of Preventative, Predictive and Reactive maintenance. The aim of this model is to combine the advantages of all three maintenance models and eliminate the demerits that are associated in each of them.

Fig 1 – RCM representation

Implementing RCM policies in critical infrastructure is often a challenge to the maintenance provider because of the culture change involved in the migration as the FM industry is deep rooted in the calendar based maintenance, where a proposal of leaving an asset run to fail (i.e. reactive) is often scrutinised or even considered “sacrilegious”. The irony is that carrying out frequent maintenance, irrespective of asset’s current operational context induces more failures than usual. This type of failure is referred as “Maintenance Induced Failures” which is often the culprit that results in early failures (Infant Mortality) in components. The key to a successful RCM plan is by being more selective, which means classifying the criticality of assets based on its functionality and the criteria can be devised based on the following questions

- What is the functionality of the asset?

- What are the consequences if an asset fails?

- Does the failure impair business continuity?

- Does the failure poses a health and safety risk?

Based on the above generic questions, the assets are classified into Critical or Non-critical Process Steps: [1] Classify the criticality of an asset [2] Analyse the possible failure modes that occur on that asset [3] Select the most appropriate maintenance task to mitigate the identified failure modes (Could be Preventative, Predictive or Reactive) [4] Create schedule [5] Execute the tasks and review

Preventative

Predictive

Reactive

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Condition based Monitoring (CbM) A quintessential aspect in the RCM model is the prognostic assessment. Visual inspection is often the primary method to access the operational status of the asset, but the amount of information that can be extracted via this method is limited as there are constraints that limit its efficiency (e.g. human errors, spurious alarms).

In some cases, detecting failures can often challenge even the most experienced engineers since some of the early signs of deterioration are hard to detect or almost impossible during visual inspections. With the rapid growth of sensors and signal processing technology engineers can now have a much broader spectrum of their asset’s operational status and allows them to detect early deterioration signs and even some of the hidden failures.

Case study: Package Chiller Units (Air Cooled) Chiller units are an inseparable part of a data room architecture thus it is imperative to maintain these asset types at optimum level as the consequence of a failure is often catastrophic. A conventional calendar based maintenance program offers very little visibility on its asset’s current operational status and offers a false hope to the owner

that failures are mitigated because the maintenance tasks were performed at regular intervals, even before any failure might occur, which begs the question, “How can we assume that failures are mitigated without any knowledge on asset’s current status and it’s maintenance requirement”?.

The example below shows an air cooled chiller’s compressor exhibiting severe signs of wear which was identified with the aid of our vibration analyser.

Asset type Chiller – Air Cooled

Manufacturer Daikin

Total Life Expectancy 20 years

Current Age 5

Fig 2 Diagnostic report

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Findings Severe wears were found on the seals and bearings on the screw compressor end. Excessive heat signatures were picked up on the compressor casing, it was recommended to isolate the unit to prevent any catastrophic failures or any internal damage. The chiller unit is part of an active redundancy system where 3 out of 4 systems are required to be online and unit no 4 acted as an active backup which implies that when cooling load increases the unit starts to support the demand.

Cause Rapid duty cycle was the cause of the excessive wear on the bearing, where the unit had 76806 starts (Ton) from the year of

installation and average hours of operation per start was found to be 30 minutes. Since the compressor had a high duty cycle, the oil in the compressor has been pumped out and did not had the chance to bring oil back into the unit causing wear on the seals and the bearings due to lack of lubrication.

Run Hours

Number of Starts

Hours Toff Duty Cycle

Ave. Operation hours per start

Chiller 1 23768 849 26297.43 2529.43 0.3 28.0

Chiller 2 23784 3163 26297.43 2513.43 0.6 7.5

Chiller 3 34822 49710 43829.05 9007.05 0.8 0.7

Chiller 4 19628 76806 43829.05 24201.05 0.8 0.3

Table 1.1

Chart 1 – Duty Cycle Comparison

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

1 2 3 4

Chiller units

Duty Cycle

0.8 0.8

0.6=

0.3=

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Findings (continued) Life data analysis is a well known method in reliability engineering which allows the engineer to identify the failure characteristics of a component, based on the historical failure data, i.e. Ages to Failure data and current findings were fed into the Predictive Maintenance Management (PMM) tool in order to identify the failure characteristic of

the compressor and found that the compressor is in the wear out region with increasing failure rate. The replacement age of the bearing was found to be 45000 hours based on current and historic failures, with 0% survival rate for the next two years of operation.

Fig 3: Snapshot – PMM tool analytics

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Cost Analysis Illustrated below is the cost breakdown of the remedial works that were performed after identifying the potential failure in the compressor, the figures were duly recorded

and updated on the predictive maintenance management system in order to identify the overall life cycle cost of the chiller.

Descriptions Amount

Initial Investigation £536

Compressor overhaul £8290

Stepless motor replacement £1460

Reoccurring yearly maintenance cost

Annual maintenance cost per chiller £ 1400

Total £ 11,128.50 Table 1.2

If the fault had left unnoticed, the seals and bearings would have damaged the actual compressor which would have resulted in compressor replacement where potential cost is listed in table1.3

Descriptions Amount

New Compressor - screw £ 27,535. 07 Reoccurring yearly maintenance cost

Annual maintenance cost per chiller £ 1400

Total £ 28,935.07

RCM Conventional Maintenance (PM)

£ 11,128.50 £ 28,935.07

Saved £17,806.57

% 62 Table 1.3

Chart 2 – Cost Comparison

PMRCM

Saved

S1

£28,935.07

£11,128.50

£17,806.57

£0.00

£5,000.00

£10,000.00

£15,000.00

£20,000.00

£25,000.00

£30,000.00

PM

RCM

Saved

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Failure Mitigation Strategy As chiller no 4 was under the RCM program, during mobilisation the asset was subjected Failure Modes and Effects Analysis (FMEA) where 11 possible failure modes were identified based on its installation & operational requirements, one of failure modes was the rapid duty cycle, the agreed mitigation action was to perform vibration

analysis to detect compressor wear. As the infrastructure personnel are currently amending BMS strategy to reduce the stress on the compressors, the inspection interval of the vibration analysis on the other three chillers have been increased to a monthly from 3-monthly interval on the predictive inspection planner.

Item Potential

Failure Mode Potential

Failure Effects

SE

V

Potential Causes

OC

C

Current Controls

DE

T

RP

N

Action

Compressor High Duty cycle i.e.

ON/OFF cycle

If a compressor is allowed to an extraordinary

fast cycling rate, oil is pump out of compressor but

may not have a

chance to bring oil back to the compressor.

8 Inappropriate BMS

strategy

6 3 monthly

visit

4 192 Perform Vibration Analysis

Fig 4: Snapshot of PMM tool’s Planner

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ISO55000: Acceptance The Institute of Asset Management (IAM) have published an international standards for good asset practice, unlike its predecessor PAS55 which outlines 28 requirements for efficient asset management, ISO55000 discusses 36 subjects and policies with great emphasis on RCM, reliability engineering and condition based monitoring programmes.

“To support better justified Operations & Maintenance Decision-Making, maintenance techniques such as ‘Reliability-Centred Maintenance’ (RCM) have been adopted, which incorporate Failure Mode and Effects Analysis (FMEA), to ensure that maintenance tasks and intervals are more appropriate for the different types of failure modes that can occur and the different consequences that result from those failures.” – Page 28, section 5.2.2

“Reliability Engineering is an ongoing process starting at the conceptual phase of a product design (including defining system requirements) and continuing throughout all phases of a product lifecycle. The goal always needs to be to identify potential reliability problems as early as possible in the product lifecycle and ensure that the reliability requirements will be met” – Page 40 section 5.3.6

“Inspection, Testing & Monitoring – non-intrusive checks to confirm safety and integrity of assets and to provide information for determining maintenance and renewal needs, perhaps using a work bank to collate and prioritise required activities, This may include the use of remote monitoring systems” – Page 39 section 5.3.5.

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Conclusion

Preventative Maintenance is the most efficient maintenance regime but also happens to be one of the overrated models in the industry; this paper does not dismiss the use of preventative maintenance but questions the real value of the it which can only be achieved if the optimum point of intervention is known and the use of condition based assessment/ inspections acts as decisive tool which allows the facilitators to identify optimum maintenance interval and also provides them a window of opportunity to Plan, Schedule and Perform remedials once a potential failure in an asset has been identified. A dynamic maintenance model such as Reliability Centred Maintenance (RCM) is ideal for this purpose as it not only offers flexibility to the maintenance provider but allows them to realise:

• Increased reliability

• More proactive

• Fewer breakdowns

• Increased operational life

• Consistent savings in maintenance cost

• Increased availability

•

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About Uptimeplus Uptimeplus is the critical environment management service from Integral UK Ltd. Powered by our revolutionary technology platform galileo; we provide the means for a

continuous systematic method of monitoring and trending of critical asset condition. The New CEM Paradigm Traditional, fixed-term maintenance plans would mean elements of your critical environment would be left until they failed, or replaced too early - increasing the risks and costs of your operation. By following the maxim that prevention is better than cure, uptimeplus powered by galileo offers continuous real-time

monitoring of your people, processes and technology, whilst supporting an accurate and detailed history of the life cycle of each of your critical assets. By using complex algorithms, we then accurately predict the point of asset failure, which is then incorporated into a bespoke Predictive Planned Maintenance strategy. Galileo checks the 'health' of an asset while it is operating, supported by 41 processes. From the condition based asset data, uptimeplus, using galileo can

perform a range of analytical techniques only found within this unique technology. Clients can now as a result, forecast when vital Maintenance should be planned and performed, which means the decision on the maintenance interval is based on asset need and criticality, delivered by an engineering team that works tirelessly to keep your critical operation risk free. For more information,

www.uptimeplus.co.uk Company Registration Number 5307588. Copyright © 2015, Integral UK Ltd, All Rights

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