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DEVELOPMENT OF A METHOD FOR RELIABLE AND LOW COST PREDICTIVE MAINTENANCE Jacopo Cassina. Agenda. Aims of the work The PROMISE Project Consumer Goods Scenario Used tool Methodology Merloni Termo Sanitari application Comparison with another algorithm Results and Further Development. Aims. - PowerPoint PPT Presentation
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DEVELOPMENT OF A METHOD FOR DEVELOPMENT OF A METHOD FOR RELIABLE AND LOW COST PREDICTIVE RELIABLE AND LOW COST PREDICTIVE
MAINTENANCEMAINTENANCE
Jacopo Cassina
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Jacopo Cassina – MM 2006
AgendaAgenda
1. Aims of the work2. The PROMISE Project3. Consumer Goods Scenario4. Used tool5. Methodology6. Merloni Termo Sanitari application7. Comparison with another algorithm8. Results and Further Development
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Jacopo Cassina – MM 2006
AimsAims
This paper will present a methodology, which can assist technician and researchers during the development of a predictive maintenance algorithm, based on soft computing techniques, into the consumer goods scenario.
It has been developed, improved and tested within a research and two application packages of an European project called PROMISE.
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Jacopo Cassina – MM 2006
PROMISEPROMISE
PROduct lifecycle Management and Information Tracking using Smart Embedded Systems.
The Promise aim: develop a new PLM tool and new PLM methodologies, also for consumer goods.
The PROMISE R&D: Data and information management and modelling Smart wireless embedded systems …
Predictive maintenance Design for X End Of Life planning Adaptive production management …
Data Management tools
Decision Support System Tools
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Jacopo Cassina – MM 2006
Consumer Goods ScenarioConsumer Goods ScenarioBusiness requirements:• Attention to costs of:
the development of the algorithm The sensors The computational power Transmission of data
Simple product
Soft computingSoft computing•Easy to use
•Short training
•Could train itself
•Robust - Adaptable
•Can analyze easily lots of parameters
•Can model rules and particular conditions
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Jacopo Cassina – MM 2006
Short overview on the used ToolShort overview on the used Tool
The proposed soft computing methodology is the following:
Inside a Fuzzy environment we will use a neural network
to train an expert system
Then the Rules of the expert system will be used to predict the residual life of the product.
This approach could exploit the advantages of all the techniques, reducing the weaknesses.
Exist dedicated hardware for fuzzy expert systems
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Jacopo Cassina – MM 2006
MethodologyMethodologyTo achieve the algorithm a methodology has been developed and followed.It aims to exploit the peculiarities of the scenario and of the used tool,
reducing the complexity and the costs of the experiments and of the whole development.
Eight steps will compose the methodology:1. definition of the monitored breakdowns 2. definition of the sub-system to be controlled3. selection of the variables to be controlled for each sub-system4. analysis of the whole product and selection of the minimum number of
variables and sensors5. design of the experiments 6. experimentation7. training of the algorithm8. test and validation of the algorithm
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Jacopo Cassina – MM 2006
Merloni Termo Sanitari ApplicationMerloni Termo Sanitari ApplicationFirst application of the methodology and of the tool.
Aim: achieve a reliable predictive maintenance algorithm for a boiler produced by MTS.
First step: selection of the failures that has to be analyzed.The selected failures, till now, are:1. The domestic hot water service failure2. The flame turn off3. The burning efficiency reduction4. The failure of the water pumps
Second step: Definition of the corresponding Sub-Systems.1. The domestic hot water Heat Exchanger2. The flame sensor - The burner3. The burner4. The Water Pump
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Jacopo Cassina – MM 2006
Sub-System: DHW heat exchanger
FAILURE : limestone on the plates decrease the heat exchange capacity;
CAUSES: limestone contained in the water;
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Jacopo Cassina – MM 2006
3° step: Selection of the controlled variables
Measurable variables by boiler control board:
• Domestic Hot water temp (San-Out)• Primary circuit flow temp (P-In)• Primary circuit return temp (P-Out)• Burned power
Additional measured variables • DHW tapping flow rate• Heating circuit pressure• …
Sensitivity analysis with these other variables
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Jacopo Cassina – MM 2006
7° step: Training of the FES7° step: Training of the FES
3 different products:
• A new Heat Exchanger• A “half” aged Heat Exchanger• An old, broken Heat Exchanger
For each 3 experiments using different hot water target temperature.
Antecedent / Consequents P-Out P-IN Out-San Gas AGING weights
29,40 40,20 29,20 4953,1 5,00 1,00
33,30 45,30 32,80 4973,757 5,00 1,00
38,50 52,60 37,50 4953,1 5,00 1,00
39,90 54,70 38,80 5035,729 5,00 1,00
49,50 54,90 51,40 1606,643 5,00 1,00
54,20 72,50 41,50 5115,858 50,00 1,00
54,70 73,20 41,90 5063,799 50,00 1,00
54,80 73,30 42,10 5063,799 50,00 1,00
54,90 73,30 42,10 5063,799 50,00 1,00
55,10 73,50 42,50 5032,563 50,00 1,00
34,30 42,30 23,90 4973,757 100,00 1,00
34,90 43,00 24,10 5004,743 100,00 1,00
36,10 44,40 24,60 4953,1 100,00 1,00
36,60 45,00 24,80 4973,757 100,00 1,00
37,20 45,70 25,10 4953,1 100,00 1,00
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Jacopo Cassina – MM 2006
Training Data SetsTraining Data Sets
0,00
10,00
20,00
30,00
40,00
50,00
60,00
70,00
80,00
90,00
0,00 2,00 4,00 6,00 8,00 10,00 12,00 14,00 16,00 18,00 20,00
[time]
[°C
]
0,00
100,00
200,00
300,00
400,00
500,00
600,00
700,00
800,00
900,00
1000,00
1100,00
P-Out P-In In-san Out-san Gas Flow
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Jacopo Cassina – MM 2006
8° step: test and validation8° step: test and validationThe algorithm has been tested and validated on some data of aged boilers and a set of data coming from an accelerated aging test (acceleration 8X ).Data recorded for 1 day a week.Sample rate = 30 sec.
It started about one year ago, and is still ongoing; the boiler still works well.
The algorithm analyzes each set of antecedents and provide an estimation of the aging.
Then the final result is a moving average of 1000 estimations.
Antecedent / Consequents
P-Out P-INSec-OUT Gas AGING Date
50 70 48 5132 19,50741 24-giu-05
46 66 41 5170 36,70522 15-lug-05
56 75 48 5095 42,09512 15-set-05
57 77 47 5132 48,99102 15-ott-05
58 73 51 5123 54,78653 15-nov-05
57 74 48 5023 62,18932 15 dec 05
58 76 50 5132 66,65374 15-gen-05
58 64 50 1620 72,37012 14-feb-06
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Jacopo Cassina – MM 2006
Comparison with another ESComparison with another ESPreviously an expert System has been trained by MTS human Experts.It has been compared with the self training fuzzy expert system we used.
Aging
0
20
40
60
80
100
120
01-mag-
05
24-giu-05
15-lug-05
15-set-05
15-ott-05
15-nov-05
15dec05
15-gen-05
14-feb-06
Fuzzy ES
Human Expert
4 months
32 real months
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Jacopo Cassina – MM 2006
Conclusions and Further DevelopmentConclusions and Further Development
Conclusions:• A methodology for the development of soft computing predictive
maintenance algorithms has been proposed • The first tests has been done• Till now, on simple products and sub-systems, works well and
required few data for training
Further Development:• Make a comparison with neural networks• Improve the training with more data• Complete the testing analyzing the accelerated aging test till the
breakdown of the boiler.• Make a sensitivity analysis using also other sensors data• Use the methodology on other and more complex product inside the
PROMISE Project (even beyond consumer good scenario)
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Jacopo Cassina – MM 2006
Ing. Jacopo Cassina e-mail: [email protected]: +39 02 2399 3951Fax: +39 02 2399 2700Skype: jacopo.cassina
Thanks for your kind attention.