1. ANALYTICAL METHODS IN MAINTENANCE
What Maintenance Analysis method
Examples of Using Maintenance Data

2. Maintenance Analysis
 Dependant upon criticality, differing levels of analysis effort should be applied.
 For the top 5-10% quantities risk and performance analysis may be warranted
 For the next 40-50% use template a rule-based methods (such as RCM).
 At the lower levels of criticality, not even simple analysis is worthwhile, and instead may rely on manufacturer’s recommendations or retain current practice.

3. ANALYSIS/SIMULATION SOFTWARE
 For the top 5-10% quantities risk and performance analysis may be warranted:
ANALYSIS/SIMULATION SOFTWARE
** SIMLOX
Powerful and versatile simulation tool for event based simulation and analysis of complex operational and logistic support operations. Provides graphs on system availability, resources utilisation, actual versus requested mission time, etc
** LOGAN Monte Carlo simulation tool
Evaluates availability of complex systems. Can include effect of manning levels, spares holdings, redundant and standby systems, etc

4.  For the top 5-10% quantities risk and performance analysis may be warranted:
AvSim+
AvSim+ is an availability simulation and system optimisation tool developed specifically to handle the complexity of large plants and systems.
It uses Monte-Carlo simulation to calculate the expected system reliability, availability and capacity performance over a chosen life cycle.

5. Maintenance PLANS: Horses for Courses
 Dependant upon criticality, differing levels of analysis effort should be applied.
 For the top 5-10% quantities risk and performance analysis may be warranted
 For the next 40-50% use template a rule-based methods (such as RCM).

6. RCM SOFTWARE ** RCM Turbo
Australian owned and developed expert methodology for the implementation of reliability centred maintenance strategies. A unique decision support platform leading directly to new schedules

7. DISPLAYING MAINTENANCE AND RELIABILITY DATA

8. Log Scatterplot of mean repair times versus number of failures with limit values.
“Downtime Priorities, Jack-Knife Diagrams and the Business Cycle” Peter Knights, May 2004 issue The Maintenance Journal

12. CASE STUDY USING MAINTENANCE DATA

13. ELECTRIC MOTOR
GEAR BOX
Bearing 1
Bearing 2
coupling
Casing seal
Shaft seal

14. 900 hrs 450 hrs 36 14 MTBF Failures over a 48 month period :-
ELECTRIC MOTOR
GEAR BOX
Bearing 1
Bearing 2
coupling
Casing seal
Shaft seal
Failures over a 48 month period :-
MTBF
DOWNTIME
No FAILURES
FAILURES >3hrs DT
900 hrs
450 hrs 36 14

15. Bearing 2 2000hrs 427hrs 16 12 ELECTRIC MOTOR GEAR BOX Bearing 1
coupling
Casing seal
Shaft seal
MTBF
DOWNTIME
No FAILURES
FAILURES >3hrs DT
Bearing 1
4000hrs
9hrs 8 2
Bearing 2
2000hrs
427hrs 16 12
Coupling
8000hrs
2hrs 4
Shaft seal
6000hrs
5hrs 6
Casing seal
16000hrs

16. HISTORY OF FAILURES BEARING 2
Clock hrs
Hrs to fail
History of failure 1.
190
Bearing incorrectly……… 2.
205 15
Lack of……… 3.
3705
3500 4.
3785 80 5.
4105
320 6.
8305
4200 7.
8330 25 8.
8570
240 9.
13570
5000
10.
17570
4000
11.
17630 60
12.
22130
4500
13.
22570
440
14.
26670
4100
15.
26710 40
16.
29510
2800

17.  1st failure at 190 hrs 2nd failure at 205 hrs
3rd failure at 3705 hrs
4000hrs   
0hrs

18. 1st failure at 190hrs 2nd failure at 205hrs 3rd failure at 3705hrs   
8000hrs 
12000hrs   
16000hrs 
20000hrs  
24000hrs  
28000hrs  
32000hrs 

19. BEARING 2 *RANKED ORDER* BEST WORST 1 15.00 2 25.00 3 40.00 4 60.00 5
80.00 6
190.00 7
240.00 8
320.00 9
440.00 10 11 12 13 14 15 16
BEST
*RANKED ORDER*

20. HISTOGRAM OF BEARING No.2 FAILURES
NUMBER OF FAILURES

21. WEIBULL ANALYSIS It allows analysis of HOW COMPONENTS FAIL.
If it is found that failure is AGE RELATED then you can also identify the optimum REPLACEMENT INTERVAL

22. BEARING 2 *RANKED ORDER* 1 15.00 2 25.00 3 40.00 4 60.00 5 80.00 6
190.00 7
240.00 8
320.00 9
440.00 10 11 12 13 14 15 16
*RANKED ORDER*

23. WEIBULL ANALYSIS RELCODE Data Analysis Software
RELCODE is a software package for Weibull analysis of Failure Data.

24. MONTE CARLO SIMULATION6
NUMBER OF FAILURES 5 4 7 1 2 3
Use random numbers to recreate the failures
Number range 1 to 7
If No. 1 then this is a failure at 2500 hrs

25. 1st failure at 2500 hrs 
0hrs

26. Add repair hrs and cost data6
NUMBER OF FAILURES 5 4 7 1 2 3
Add repair hrs and cost data

27. 1st failure at 2500 hrs
Repaired at 2520 hrs cost $1000  
0hrs
Using more random numbers you can continue generating failures and repair/cost data.
You may test a number of fixed time replacement events, test which failures they will prevent and see which replacement time is cost effective.
You can run the simulation for 100yrs for similar machines operating in parallel and establish number of man hrs per week required to maintain the machines.