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

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[time]

[°C

]

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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

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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: jacopo.cassina@polimi.itTel: +39 02 2399 3951Fax: +39 02 2399 2700Skype: jacopo.cassina

Thanks for your kind attention.