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PERFORMANCE ANALYSIS AND FLEET
MAINTENANCE DEVELOPMENT FOR ITALIAN
PUBLIC TRANSPORT COMPANY (FS-BUSITALIA)
Student: Naraharisetti Prabhu Rayudu
Matricola: 1794230
Relatore: Prof. Lorenzo Fedele
Content
Historical data
Reliability modelling Weibull distribution
Output highlighting
MTBF
Equipment trend recognition
Maintenance decision
27/03/2020 Pagina 2PERFORMANCE ANALYSIS AND FLEET
MAINTENANCE DEVELOPMENT FOR ITALIAN
PUBLIC TRANSPORT COMPANY (FS-
BUSITALIA)
Why I choose only the 3 components
After analyzing the past failures
System and equipment reliability prioritization was done by taking the below in to consideration
1.Operational Cost (A)
2.Throughput Process (B)
3.Safety (C)
Here the safety is taken as the major aspect to be taken into consideration
PERFORMANCE ANALYSIS AND FLEET
MAINTENANCE DEVELOPMENT FOR ITALIAN
PUBLIC TRANSPORT COMPANY (FS-
BUSITALIA)
27/03/2020 Pagina 3
𝑆𝑦𝑠𝑡𝑒𝑚 𝑎𝑛𝑑 𝑒𝑞𝑢𝑖𝑝𝑚𝑒𝑛𝑡 𝑟𝑒𝑙𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑝𝑟𝑖𝑜𝑟𝑖𝑡𝑖𝑧𝑎𝑡𝑖𝑜𝑛 𝐷
=𝐴2 + 𝐵2 + 𝐶2
12
4
Other aspects
• Operations criticality analysis
• Asset criticality ranking
• Asset failure probability factor (AFPF)
After taking the above aspects in to consideration the main components that are to be analyzed
are the below:
1. Engine
2. Brake
3. Suspension
Pagina 4PERFORMANCE ANALYSIS AND FLEET
MAINTENANCE DEVELOPMENT FOR ITALIAN
PUBLIC TRANSPORT COMPANY (FS-
BUSITALIA)
27/03/2020
ENGINEMercedes Benz Model OM 936 engine
OUTPUT [KW] TORQUE [Nm]
Pagina 5PERFORMANCE ANALYSIS AND FLEET
MAINTENANCE DEVELOPMENT FOR ITALIAN
PUBLIC TRANSPORT COMPANY (FS-
BUSITALIA)
27/03/2020
Engine • Problem statement
Here we can see that the defects occur just before and after of
the maintenance.
0 39
37
39
90
70
35
45
53
7
55
62
5
10
28
59
15
48
78
15
74
87
16
27
13
16
42
08 18
57
03
18
57
03
23
49
63
0100002000030000400005000060000700008000090000
100000110000120000130000140000150000160000170000180000190000200000210000220000230000240000250000
ST
AR
T D
AT
E
25 A
pr
201
6
02 M
ay 2
01
6
14 M
ay 2
01
6
21 N
ov 2
01
6
22 A
ug
20
16
29 M
ay 2
01
7
31 J
ul 2
01
8
31 J
ul 2
01
8
30 A
ug
20
18
18 S
ep
20
18
28 D
ec 2
01
8
28 D
ec 2
01
8
06 M
ar
20
19
The brown
lines are PM
scheduled at
(60000KMS
interval)
Pagina 6PERFORMANCE ANALYSIS AND FLEET
MAINTENANCE DEVELOPMENT FOR ITALIAN
PUBLIC TRANSPORT COMPANY (FS-
BUSITALIA)
27/03/2020
Percentile Report:Percentage Percentile
(Time)
0.1 0.189591
0.135 0.244983
0.5 0.750237
1 1.359
5 5.462
10 10.09671209
25 23.798
50 50.413
75 91.097
90 140.48
95 175.86
99 253.84
99.5 286.11
99.865 345.47
99.9 358.82
-3,46
-2,96
-2,46
-1,96
-1,46
-0,96
-0,46
0,04
0,54W
EIB
UL
L S
CO
RE
Ln (Time-to-Fail)
Weibull Analysis: Time-to-Fail
Distribution
Characteristics:Estimate
Mean (MTTF) 65.258
Pagina 7PERFORMANCE ANALYSIS AND FLEET
MAINTENANCE DEVELOPMENT FOR ITALIAN
PUBLIC TRANSPORT COMPANY (FS-
BUSITALIA)
27/03/2020
Preliminary analysis
Pagina 8PERFORMANCE ANALYSIS AND FLEET
MAINTENANCE DEVELOPMENT FOR ITALIAN
PUBLIC TRANSPORT COMPANY (FS-
BUSITALIA)
27/03/2020
Fig.6 Pressure vs Crank angle
47.5849.5
Main analysis
Pagina 9PERFORMANCE ANALYSIS AND FLEET
MAINTENANCE DEVELOPMENT FOR ITALIAN
PUBLIC TRANSPORT COMPANY (FS-
BUSITALIA)
27/03/2020
Some corrections still
needed
SUSPENSION
Pagina 10PERFORMANCE ANALYSIS AND FLEET
MAINTENANCE DEVELOPMENT FOR ITALIAN
PUBLIC TRANSPORT COMPANY (FS-
BUSITALIA)
27/03/2020
Pagina 11PERFORMANCE ANALYSIS AND FLEET
MAINTENANCE DEVELOPMENT FOR ITALIAN
PUBLIC TRANSPORT COMPANY (FS-
BUSITALIA)
27/03/2020
Pagina 12PERFORMANCE ANALYSIS AND FLEET
MAINTENANCE DEVELOPMENT FOR ITALIAN
PUBLIC TRANSPORT COMPANY (FS-
BUSITALIA)
27/03/2020
BRAKE
Pagina 13PERFORMANCE ANALYSIS AND FLEET
MAINTENANCE DEVELOPMENT FOR ITALIAN
PUBLIC TRANSPORT COMPANY (FS-
BUSITALIA)
27/03/2020
Pagina 14PERFORMANCE ANALYSIS AND FLEET
MAINTENANCE DEVELOPMENT FOR ITALIAN
PUBLIC TRANSPORT COMPANY (FS-
BUSITALIA)
27/03/2020
y = -0,0002x + 27,667R² = 0,9061
0
5
10
15
20
25
30
0 10000 20000 30000 40000 50000 60000 70000 80000
MILEAGE VS THICKNESS
Mileage Thickness
1000 27
2000 28
3000 26
4000 26
5000 27
6000 28
7000 26
8000 28
U = Total time under use
t = time at which wear is measured
ΔD= degree of wear
R= brake pad thickness remaining
• X-mileage
• Y-thickness
• C- coefficient of intercept
• M- coefficient of slope
𝑌 = 𝑀𝑋 + 𝐶
𝑊𝑒𝑎𝑟 𝑟𝑎𝑡𝑒 = 𝑊𝑒𝑎𝑟 𝑣𝑜𝑙𝑢𝑚𝑒 / 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑎𝑓𝑡𝑒𝑟 𝑏𝑟𝑒𝑎𝑘
COST
BENEFIT
ANALYSIS
Pagina 15PERFORMANCE ANALYSIS AND FLEET
MAINTENANCE DEVELOPMENT FOR ITALIAN
PUBLIC TRANSPORT COMPANY (FS-
BUSITALIA)
27/03/2020
Cost benefit analysis-One time maintenance
X = total cost for one-time maintenance
• C(pm) = cost for the preventive maintenance
• C(cm) = cost of corrective maintenance
• R(B) = reliability at time B
• B= time interval for preventive maintenance
• C= mean time to perform preventive maintenance
• D= mean time required to conduct corrective maintenance
• t= MTTF
• E= mean time for corrective and preventive maintenance. (MTTR)
FAILURE
REPLACEME
NT
PREVENTIVE
MAINTENANCEFAILURE
REPLACEME
NT
A B
C DE
CYCLE 1 CYCLE 2
t
Pagina 16PERFORMANCE ANALYSIS AND FLEET
MAINTENANCE DEVELOPMENT FOR ITALIAN
PUBLIC TRANSPORT COMPANY (FS-
BUSITALIA)
27/03/2020
𝑋 =𝐶 𝑝𝑚 ∗ 𝑅 𝐵 + 𝐶 𝑐𝑚 ∗ 1 − 𝑅 𝐵
𝐵 + 𝐶 ∗ 𝑅 𝐵 + 0𝐵𝑡𝑓 𝑡 𝑑𝑡 + 𝐸 ∗ 1 − 𝑅 𝐵
If no maintenance is performed
Pagina 17PERFORMANCE ANALYSIS AND FLEET
MAINTENANCE DEVELOPMENT FOR ITALIAN
PUBLIC TRANSPORT COMPANY (FS-
BUSITALIA)
27/03/2020
I (n) = expected time between breakdowns
I (n) = ∑ nx probability of breakdown,
where
n = time period when a breakdown may occur,
❑ x = probability of a breakdown in period n
= Number of machine breakdowns in period n / Total number of breakdowns in total
❑ Expected number of breakdowns = 1
𝐼 𝑛
❑ Expected cost of letting Y identical machines breakdown, CT is:
a= cost of repairing a break down machine
E= mean time for corrective and preventive maintenance. (MTTR)
[So finally, the cost of letting Y machines breakdown = cost, if no maintenance is
performed]
𝑪𝑻 = (𝒀 ∗ 𝒂) / (𝑬 (𝒏))
Final formula
Pagina 18PERFORMANCE ANALYSIS AND FLEET
MAINTENANCE DEVELOPMENT FOR ITALIAN
PUBLIC TRANSPORT COMPANY (FS-
BUSITALIA)
27/03/2020
[𝑵𝒆𝒘 𝒑𝒓𝒆𝒅𝒊𝒄𝒕𝒊𝒗𝒆𝒎𝒂𝒊𝒏𝒕𝒆𝒏𝒂𝒏𝒄𝒆 𝒄𝒐𝒔𝒕
= 𝑪𝒐𝒓𝒓𝒆𝒄𝒕𝒍𝒚 𝒑𝒓𝒆𝒅𝒊𝒄𝒕𝒆𝒅
× 𝑵𝒆𝒘 𝒓𝒆𝒑𝒂𝒊𝒓 𝒄𝒐𝒔𝒕 – 𝑾𝒓𝒐𝒏𝒈𝒍𝒚 𝒑𝒓𝒆𝒅𝒊𝒄𝒕𝒆𝒅
× 𝑪𝒐𝒔𝒕 𝒐𝒇 𝒓𝒆𝒑𝒂𝒊𝒓 − 𝑨𝒎𝒐𝒖𝒏𝒕 𝒊𝒏𝒗𝒆𝒔𝒕𝒆𝒅]
Each correct prediction saves the additional cost and each
wrong prediction adds additional cost to the budget.