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12 Lieblein and Zelen, in their seminal work on the subject of bearing life 4 , found that the characteristic life, the point where statistically 63.2% of the items will have failed, was roughly 5 times the L10 life. They also found that the “life” forecasts had a median Weibull Beta value of 1.4, indicating a near constant probability of failure. This means that the likelihood of failure at any point in the life of the bearings in their study increased only marginally as the asset aged. Other published analyses have quoted a beta of “1.3” for Ball and Roller Bearings, and a beta of “1” for sleeve bearings 5 . In process manufacturing industries, we find contaminated oil as one of frequent reasons for early life failures. However, this is only one of the multitudes of stresses that bearings face as complex assets. Others can include poor storage leading to false brinnelling and early corrosion, excessive heat and pressure, overloading, exposure to vibration, abrasions and cracks. All of these could contribute to either early life failures, or premature wear out. Often, the L 10 life is mistaken for an end life point for bearings, thus used as a reference interval for replacement tasks. However, as can be seen from the information above, it is not the end-life, rather a minimum guaranteed life for 90% of bearings under specific load conditions. This is in line with Nowlan and Heaps’ findings and shows that in many cases we are at best wasting a large portion of the bearings useful life, making this an ineffective use of maintenance resources. Over one Machine this appears to be a very small maintenance Cost item. However when applied throughout a plant, or on the so-called “critical” assets, it amounts to a significant maintenance cost. Increased bearing life and decreased labor costs are not the only potential savings. Frequent replacing of bearings on, say, motor shafts we introduce the likelihood of a range of additional failure modes. For example, installation and frequent change out failures include: • Wear of the motor shaft, decreasing the adequacy of the interference fit; leading to bearings spinning on the shaft (A failure of the motor, not of the bearing) • Over heating of the bearing during installation leading to early life failures and distortion of the inner race • Excessive force (i.e. Hammers) instead of bearing pullers, damaging the races of the bearings and leading to early life failures • Bearing misalignment • Wrong bearing selection • Pre-failed bearings due to poor storage techniques While we can manage some of these, others are a direct result of frequent bearing changes. Therefore, if we use hard time bearing replacement as a maintenance policy then we are: • reducing the maximum used life of the bearing, and • increasing the likelihood of failure through the introduction of several additional failure modes In the RCM decision algorithm 6 , a management policy for an Evident Operational and Non-Operational failure mode must comply with the following: “Over a period of time, the failure management policy must cost less than the cost of the operational consequences (if any) plus the total cost of repair.” Ineffective maintenance is more common than most professionals think, it can also include areas such as maintenance out of context, where maintenance regimes are unaligned with how the asset is used, or practices that decrease an assets efficient operations. Using the decision algorithm in RCM, the first option available to the team is Predictive Maintenance. Where this is both applicable and effective it will increase the effectiveness of maintenance in a range of areas: • Predictive Maintenance detects the signs of the onset of failure. As such, it provides the capability to manage all failures, including random failures. • It can be done in-situ and often without interfering with the normal operation of the process. • It will ensure that the asset utilizes all of its economically useful life. (As opposed to hard-time replacements) INAPPLICABLE MAINTENANCE This mistaken belief that there is always a relationship between age and failure leads maintenance departments to all sorts of policies that, in practice, are achieving nothing. Often these occur during maintenance turnarounds. The opportunity to access items that are normally in a running state drives people to inspect items just in case a life related failure mode has developed. The Impact Of RCM AMMJ Vol 24 No 2
2011 RELIABILITY AND APOLLO ROOT CAUSE ANALYSIS TRAINING SCHEDULE ARMS Reliability offer a range of reliability training courses for engineers, maintenance professionals and asset managers looking to learn more about RCM, Availability, Asset Management, RAMS Analysis, FaultTree and Root Cause Analysis. ROOT CAUSE ANALYSIS RCA201: Apollo Root Cause Analysis Facilitators The course examines the basic concept of problem solving, and how to facilitate an effective investigation. Learn the simple 4-step methodology that is currently used by hundreds of companies throughout Australia. RELIABILITY TRAINING MRI201: Managing Reliability Improvement A practical course addressing the integration of a range of reliability initiatives into an asset management strategy. This course covers RCM, Life Cycle Costing, Root Cause Analysis, Data Analysis and Plant Availability Simulation. RBD201: Managing Plant Availability Provides an understanding of the applicable methods for evaluating and improving system performance and to understand the important considerations in performing system analysis using failure rate data and failure behaviour parameters. Training courses delivered at major cities throughout Australia & New Zealand as well as onsite for your team. View dates and book online for public courses at globalreliability.com/2011training or phone 03 5255 5357 for more information. II201: Incident Investigation Investigating incidents is important to learn what went wrong so repeat occurrences can be prevented. Participants will learn why and how to conduct incident investigations that get to the root cause and help prevent further incidents. RCM201: Managing Reliability Centred Maintenance Provides an understanding of the RCM method of maintenance task optimisation. Participants will gain an understanding of the use of failure data analysis and failure forecasting and how to choose optimum maintenance tasks that reduce the costs to the business. AWB201: Availability Workbench This course is designed to fast-track you through the process of using Isograph’s Availability Workbench software to make maintenance and reliability decisions. Students the three main modules in Availability Workbench, RCMCost, AvSim & LCC. RELIABILITY WEEK: PERTH SEPTEMBER 12TH – 16TH BOOK ONLINE @ globalreliability.com
Page 1 and 2: The AMMJ is Sponsored and Supported
Page 3 and 4: info@infratherm.com.au Introducing
Page 5 and 6: 6 10 20 22 30 AMMJ Maintenance Engi
Page 7 and 8: AMMJ Maintenance Enginering and Eng
Page 9 and 10: Locate electrical problems Detect p
Page 11: technology means that experienced t
Page 15 and 16: Do our people get smarter when they
Page 17 and 18: existing strategy in place, the tea
Page 19 and 20: To be effective the task of calcula
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Page 23 and 24: AMMJ Lessons Learnt In 45 Years of
Page 31 and 32: Vol 24 No 2 AMMJ to preventative ma
Page 33 and 34: Vol 24 No 2 AMMJ - Reduce equipment
Page 35 and 36: Vol 24 No 2 AMMJ • MaintiMizer is
Page 37 and 38: Vol 24 No 2 AMMJ CMMS/EAM DESCRIPTI
Page 39 and 40: AMMJ Fit at 50 39 The enormous cost
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Page 43 and 44: AMMJ Fit at 50 43 Figure 10 ABB Tra
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Page 51 and 52: Technical Short Feature: Machinery
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Page 57 and 58: Seminar 1 Duration - 1 Day The Why,
Page 59 and 60: Len Bradshaw - Seminars 1 & 2 Len B
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