Making Evidence-based Maintenance Decisions
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Making Evidence-Based Maintenance Decisions
Andrew K S Jardine CBM Laboratory Department of Mechanical & Industrial Engineering University of Toronto Canada [email protected] August 2006
www.ipamc.org
Excellence in Physical Asset Management Optimizing Equipment Maintenance and Replacement Decisions
Component Replacement
Capital Equipment Replacement
Inspection Procedures
Resource Requirements
Maintenance Management System (CMMS/EAM/ERP) www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
2
Asset Management
We want Fact–based arguments (data driven decisions)
not Intuition–based pronouncements (strength of personalities, # of mechanics’ complaints)
www.ipamc.org
Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
3
RCM Methodology Logic SELECT EQUIPMENT
Is condition monitoring technically and economically feasible to detect warning of a gradual loss of the FUNCTION? Condition-Based Maintenance
YES Is a repair technically and economically feasible to restore the performance of the item, and will this reduce the risk of FAILURE ? Time-Based Maintenance
YES Is it technically and economically feasible to replace the item, and will this reduce the risk of FAILURE ? Time-Based Discard
YES Default Actions
www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
4
Back to Basics “Once the equipment enters service a whole new set of information will come to light, and from this point on the maintenance program will evolve on the basis of data from actual operating experience. This process will continue throughout the service life of the equipment, so that at every stage maintenance decisions are based, not on an estimate of what reliability is likely to be, but on the specific reliability characteristics that can be determined at the time.” F.S. Nowlan and H. Heap www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
5
RCM Methodology Logic SELECT EQUIPMENT
Is condition monitoring technically and economically feasible to detect warning of a gradual loss of the FUNCTION? Condition-Based Maintenance
YES Is a repair technically and economically feasible to restore the performance of the item, and will this reduce the risk of FAILURE ? Time-Based Maintenance
YES Is it technically and economically feasible to replace the item, and will this reduce the risk of FAILURE ? Time-Based Discard
YES Default Actions
www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
6
Toronto Transit Commission: Subway have been able system to analyze several
“At the TTC, I components (which we were overhauling periodically) to justify if it is worthwhile doing the overhaul. It was possible to use Weibull analysis since when these components failed, they failed due to a dominant failure mode, and the defective component was replaced with a new one or one that is just like new. I found that most times the hazard rates obtained were decreasing. This I later found was due to poor quality components and questionable maintenance practices. Overhaul on these components has been suspended and we are only changing them on failure. Quality issues are also being addressed.” www.ipamc.org
Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
7
Using MTBF to Determine Maintenance Interval Frequency is Wrong “Random failures make up the vast majority of failures on
complex equipment as research has shown. For example, consider the failure of a component. Assume that each time the component failed we tracked the length of time it was in service. The first time the component is put into service it fails after 4 years, the second time after 6 years, and the third time after only 2 years (4 + 6 + 2 = 12/3 = 4). We know that the average lifespan of the component is 4 years (its MTBF is 4 years). However, we do not know when the next component will fail. Therefore we cannot successfully manage this failure by traditional time-based maintenance (scheduled overhaul or replacement).” Source: Viewpoint: Maintenance Technology, October 2003 www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
8
Fact 1. Preventive replacement at the MTBF could be the best answer, but it does depend on additional evidence. Fact 2. If a reliability engineer trained in the statistical analysis of failures analyzed the 3 failure times they would obtain a "bestestimate" that there is significant wear-out occurring, and that time-base replacement could be appropriate. This conclusion is obtained by examining the evidence (3 failure times) and doing a simple Weibull analysis. Using regression analysis the shape parameter, beta, is estimated as 1.74. Thus the “best estimate” indicates an increasing hazard function, and so the risk of bearing failure occurring could be reduced through bearing preventive replacement based on time. www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
9 © CBM Lab
MORAL Make evidence based decisions!! - Using appropriate tools
www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
10
Maintenance Excellence Optimizing Equipment Maintenance and Replacement Decisions
Component Replacement
Capital Equipment Replacement
Inspection Procedures
Resource Requirements
Maintenance Management System (CMMS/EAM/ERP) www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
11
Dear Professor Jardine, Î We are one of the largest marine cargo handling firms in the U.S. We have approx 2400 pieces of rolling stock, mostly powered lift equipment (stationary cranes, mobile cranes, side & top handlers, forklifts, etc). We have no corporate strategy on equipment repair/replacement, lease vs. buy, economic service life, etc. These decisions are based often on strength of personalities and # of mechanics complaints, not objective analysis. I'm looking to change that. Î On the plus side, we do have a CMMS (Maximo) and 4 years of "pretty good" equipment information and cost history. So we have some data to analyze. Î I'll be back in my office Sept 18-19, perhaps we could connect then. I'm on U.S. west coast time (based in Los Angeles). Look forward to learning more. www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
12
Î The outcome of the previous message was that the company was
visited for one day. Î In the morning a procedure to establish the economic life of their mobile equipment was discussed. Î In the afternoon the IT person joined the discussion, and discussed how to access their company data base. Î The data from the data base was then inputted into a standard economic life model to establish the economic life for a sample asset it was a Hustler truck - costing about USD 60,000 new. Î Company present policy was to replace their Hustlers at about 18 years of age. Î The economic life established by using the economic life model was about 10 years. Cost saving per year was USD 3340. Î There were 449 similar vehicles in their fleet. Î Therefore total annual saving was estimated at:
USD 3340.00 x 449 = USD 1.5 millions PER YEAR
www.ipamc.org
Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
13
Maintenance Excellence Optimizing Equipment Maintenance and Replacement Decisions
Component Replacement
Capital Equipment Replacement
Inspection Procedures
Resource Requirements
Maintenance Management System (CMMS/EAM/ERP) www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
14
RCM Methodology Logic SELECT EQUIPMENT
Is condition monitoring technically and economically feasible to detect warning of a gradual loss of the FUNCTION? Condition-Based Maintenance
YES Is a repair technically and economically feasible to restore the performance of the item, and will this reduce the risk of FAILURE ? Time-Based Maintenance
YES Is it technically and economically feasible to replace the item, and will this reduce the risk of FAILURE ? Time-Based Discard
YES Default Actions
www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
15
100% Potential failure
Detectable deterioration
Measurable property
Th
O E H T e
: Y R
Functional failure Time PF Gap
www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
16
T
R e h
: Y T I L EA
www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
17
Classical Approach: Warning Limits • Simple to understand • Limitations:
Alarm > 300ppm
– Which measurements? – Optimal limits? – Effect of Age? – Predictions?
• EXAKT extends and enhances the Control Chart technique
Warning > 200ppm Normal < 200ppm
WorkingAge
Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
www.ipamc.org 18
Data Plot
Data
Age
Hazard Plot Hazard
PHM
Age
www.ipamc.org
Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
19
OPTIMAL POLICY - OPTIMAL HAZARD LEVEL Hazard Plot
Ignore Hazard
Hazard
Optimal Hazard level
Age Replace at failure only
Cost Plot Cost/unit time
minimal cost Hazard Optimal hazard Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
www.ipamc.org 20
EXAKT Optimal Decision – A New “Control Chart”
www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
21
Campbell Soup Company: Executive Summary ÎAnalysis of Shear Pump Bearings Vibration Data Î21 vibration measurements provided by accelerometer ÎUsing PHM & Costs Î3 measurements significant ÎSavings obtained = 35 % Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
www.ipamc.org 22
Had we replaced at 175 days…..!!! Failed at WorkingAge = 182 days
Inspection at Working Age = 175 days
www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
23
Maintenance Optimization Optimizing Equipment Maintenance and Replacement Decisions
Component Replacement
Capital Equipment Replacement
Inspection Procedures
Resource Requirements
Maintenance Management System (CMMS/EAM/ERP) www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
24
Optimal Contracting-out Decision
Cost
Optimal level of maintenance resource
Total cost/unit time
Fixed cost/unit time Internal processing cost/unit time
Alternative service deliverer’s processing Cost/unit time
Level of Maintenance Resource www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
25
We want Fact–based arguments (data driven decisions)
not Intuition–based pronouncements (strength of personalities, # of mechanics’ complaints)
www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
26
We have 9Tools to deliver 9Fact – based arguments
www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
27
A Suggestion Let us develop our evidence based maintenance tool box. A collection of tools for identifying, assessing and applying relevant evidence for better asset management decision-making. It is important to have evidence to support asset management programs and not simply accept “expert opinion.” www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
28
THANK YOU University of Toronto Research Lab: www.mie.utoronto.ca/cbm
www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
29
Andrew K S Jardine CBM Laboratory Department of Mechanical & Industrial Engineering University of Toronto Canada [email protected] August 2006
www.ipamc.org
Excellence in Physical Asset Management Optimizing Equipment Maintenance and Replacement Decisions
Component Replacement
Capital Equipment Replacement
Inspection Procedures
Resource Requirements
Maintenance Management System (CMMS/EAM/ERP) www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
2
Asset Management
We want Fact–based arguments (data driven decisions)
not Intuition–based pronouncements (strength of personalities, # of mechanics’ complaints)
www.ipamc.org
Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
3
RCM Methodology Logic SELECT EQUIPMENT
Is condition monitoring technically and economically feasible to detect warning of a gradual loss of the FUNCTION? Condition-Based Maintenance
YES Is a repair technically and economically feasible to restore the performance of the item, and will this reduce the risk of FAILURE ? Time-Based Maintenance
YES Is it technically and economically feasible to replace the item, and will this reduce the risk of FAILURE ? Time-Based Discard
YES Default Actions
www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
4
Back to Basics “Once the equipment enters service a whole new set of information will come to light, and from this point on the maintenance program will evolve on the basis of data from actual operating experience. This process will continue throughout the service life of the equipment, so that at every stage maintenance decisions are based, not on an estimate of what reliability is likely to be, but on the specific reliability characteristics that can be determined at the time.” F.S. Nowlan and H. Heap www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
5
RCM Methodology Logic SELECT EQUIPMENT
Is condition monitoring technically and economically feasible to detect warning of a gradual loss of the FUNCTION? Condition-Based Maintenance
YES Is a repair technically and economically feasible to restore the performance of the item, and will this reduce the risk of FAILURE ? Time-Based Maintenance
YES Is it technically and economically feasible to replace the item, and will this reduce the risk of FAILURE ? Time-Based Discard
YES Default Actions
www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
6
Toronto Transit Commission: Subway have been able system to analyze several
“At the TTC, I components (which we were overhauling periodically) to justify if it is worthwhile doing the overhaul. It was possible to use Weibull analysis since when these components failed, they failed due to a dominant failure mode, and the defective component was replaced with a new one or one that is just like new. I found that most times the hazard rates obtained were decreasing. This I later found was due to poor quality components and questionable maintenance practices. Overhaul on these components has been suspended and we are only changing them on failure. Quality issues are also being addressed.” www.ipamc.org
Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
7
Using MTBF to Determine Maintenance Interval Frequency is Wrong “Random failures make up the vast majority of failures on
complex equipment as research has shown. For example, consider the failure of a component. Assume that each time the component failed we tracked the length of time it was in service. The first time the component is put into service it fails after 4 years, the second time after 6 years, and the third time after only 2 years (4 + 6 + 2 = 12/3 = 4). We know that the average lifespan of the component is 4 years (its MTBF is 4 years). However, we do not know when the next component will fail. Therefore we cannot successfully manage this failure by traditional time-based maintenance (scheduled overhaul or replacement).” Source: Viewpoint: Maintenance Technology, October 2003 www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
8
Fact 1. Preventive replacement at the MTBF could be the best answer, but it does depend on additional evidence. Fact 2. If a reliability engineer trained in the statistical analysis of failures analyzed the 3 failure times they would obtain a "bestestimate" that there is significant wear-out occurring, and that time-base replacement could be appropriate. This conclusion is obtained by examining the evidence (3 failure times) and doing a simple Weibull analysis. Using regression analysis the shape parameter, beta, is estimated as 1.74. Thus the “best estimate” indicates an increasing hazard function, and so the risk of bearing failure occurring could be reduced through bearing preventive replacement based on time. www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
9 © CBM Lab
MORAL Make evidence based decisions!! - Using appropriate tools
www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
10
Maintenance Excellence Optimizing Equipment Maintenance and Replacement Decisions
Component Replacement
Capital Equipment Replacement
Inspection Procedures
Resource Requirements
Maintenance Management System (CMMS/EAM/ERP) www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
11
Dear Professor Jardine, Î We are one of the largest marine cargo handling firms in the U.S. We have approx 2400 pieces of rolling stock, mostly powered lift equipment (stationary cranes, mobile cranes, side & top handlers, forklifts, etc). We have no corporate strategy on equipment repair/replacement, lease vs. buy, economic service life, etc. These decisions are based often on strength of personalities and # of mechanics complaints, not objective analysis. I'm looking to change that. Î On the plus side, we do have a CMMS (Maximo) and 4 years of "pretty good" equipment information and cost history. So we have some data to analyze. Î I'll be back in my office Sept 18-19, perhaps we could connect then. I'm on U.S. west coast time (based in Los Angeles). Look forward to learning more. www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
12
Î The outcome of the previous message was that the company was
visited for one day. Î In the morning a procedure to establish the economic life of their mobile equipment was discussed. Î In the afternoon the IT person joined the discussion, and discussed how to access their company data base. Î The data from the data base was then inputted into a standard economic life model to establish the economic life for a sample asset it was a Hustler truck - costing about USD 60,000 new. Î Company present policy was to replace their Hustlers at about 18 years of age. Î The economic life established by using the economic life model was about 10 years. Cost saving per year was USD 3340. Î There were 449 similar vehicles in their fleet. Î Therefore total annual saving was estimated at:
USD 3340.00 x 449 = USD 1.5 millions PER YEAR
www.ipamc.org
Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
13
Maintenance Excellence Optimizing Equipment Maintenance and Replacement Decisions
Component Replacement
Capital Equipment Replacement
Inspection Procedures
Resource Requirements
Maintenance Management System (CMMS/EAM/ERP) www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
14
RCM Methodology Logic SELECT EQUIPMENT
Is condition monitoring technically and economically feasible to detect warning of a gradual loss of the FUNCTION? Condition-Based Maintenance
YES Is a repair technically and economically feasible to restore the performance of the item, and will this reduce the risk of FAILURE ? Time-Based Maintenance
YES Is it technically and economically feasible to replace the item, and will this reduce the risk of FAILURE ? Time-Based Discard
YES Default Actions
www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
15
100% Potential failure
Detectable deterioration
Measurable property
Th
O E H T e
: Y R
Functional failure Time PF Gap
www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
16
T
R e h
: Y T I L EA
www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
17
Classical Approach: Warning Limits • Simple to understand • Limitations:
Alarm > 300ppm
– Which measurements? – Optimal limits? – Effect of Age? – Predictions?
• EXAKT extends and enhances the Control Chart technique
Warning > 200ppm Normal < 200ppm
WorkingAge
Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
www.ipamc.org 18
Data Plot
Data
Age
Hazard Plot Hazard
PHM
Age
www.ipamc.org
Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
19
OPTIMAL POLICY - OPTIMAL HAZARD LEVEL Hazard Plot
Ignore Hazard
Hazard
Optimal Hazard level
Age Replace at failure only
Cost Plot Cost/unit time
minimal cost Hazard Optimal hazard Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
www.ipamc.org 20
EXAKT Optimal Decision – A New “Control Chart”
www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
21
Campbell Soup Company: Executive Summary ÎAnalysis of Shear Pump Bearings Vibration Data Î21 vibration measurements provided by accelerometer ÎUsing PHM & Costs Î3 measurements significant ÎSavings obtained = 35 % Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
www.ipamc.org 22
Had we replaced at 175 days…..!!! Failed at WorkingAge = 182 days
Inspection at Working Age = 175 days
www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
23
Maintenance Optimization Optimizing Equipment Maintenance and Replacement Decisions
Component Replacement
Capital Equipment Replacement
Inspection Procedures
Resource Requirements
Maintenance Management System (CMMS/EAM/ERP) www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
24
Optimal Contracting-out Decision
Cost
Optimal level of maintenance resource
Total cost/unit time
Fixed cost/unit time Internal processing cost/unit time
Alternative service deliverer’s processing Cost/unit time
Level of Maintenance Resource www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
25
We want Fact–based arguments (data driven decisions)
not Intuition–based pronouncements (strength of personalities, # of mechanics’ complaints)
www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
26
We have 9Tools to deliver 9Fact – based arguments
www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
27
A Suggestion Let us develop our evidence based maintenance tool box. A collection of tools for identifying, assessing and applying relevant evidence for better asset management decision-making. It is important to have evidence to support asset management programs and not simply accept “expert opinion.” www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
28
THANK YOU University of Toronto Research Lab: www.mie.utoronto.ca/cbm
www.ipamc.org Andrew Jardine, CBM Lab “Making Evidence-Based Maintenance Decisions”
29
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