DEVELOPMENT OF A METHOD FOR RELIABLE AND LOW COST PREDICTIVE MAINTENANCE
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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.

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DEVELOPMENT OF A METHOD FOR RELIABLE AND LOW COST PREDICTIVE MAINTENANCE Jacopo Cassina

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Development of a method for reliable and low cost predictive maintenance jacopo cassina

DEVELOPMENT OF A METHOD FOR RELIABLE AND LOW COST PREDICTIVE MAINTENANCE

Jacopo Cassina


Agenda

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


Development of a method for reliable and low cost predictive maintenance jacopo cassina

Aims

  • 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.


Promise

PROMISE

  • 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


Consumer goods scenario

Consumer Goods Scenario

  • Business requirements:

  • Attention to costs of:

    • the development of the algorithm

    • The sensors

    • The computational power

    • Transmission of data

  • Simple product

Soft computing

  • Easy to use

  • Short training

  • Could train itself

  • Robust - Adaptable

  • Can analyze easily lots of parameters

  • Can model rules and particular conditions


Short overview on the used tool

Short 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


Methodology

Methodology

  • To 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:

  • definition of the monitored breakdowns

  • definition of the sub-system to be controlled

  • selection of the variables to be controlled for each sub-system

  • analysis of the whole product and selection of the minimum number of variables and sensors

  • design of the experiments

  • experimentation

  • training of the algorithm

  • test and validation of the algorithm


Merloni termo sanitari application

Merloni Termo Sanitari Application

  • First 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:

  • The domestic hot water service failure

  • The flame turn off

  • The burning efficiency reduction

  • The failure of the water pumps

  • Second step: Definition of the corresponding Sub-Systems.

  • The domestic hot water Heat Exchanger

  • The flame sensor - The burner

  • The burner

  • The Water Pump


Sub system dhw heat exchanger

Sub-System: DHW heat exchanger

  • FAILURE : limestone on the plates decrease the heat exchange capacity;

  • CAUSES: limestone contained in the water;


3 step selection of the controlled variables

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


7 step training of the fes

7° 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.


Training data sets

Training Data Sets


8 step test and validation

8° step: test and validation

  • The 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.


Comparison with another es

Comparison with another ES

  • Previously an expert System has been trained by MTS human Experts.

  • It has been compared with the self training fuzzy expert system we used.

4 months

32 real months


Conclusions and further development

Conclusions 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)


Development of a method for reliable and low cost predictive maintenance jacopo cassina

  • Ing. Jacopo Cassina

  • e-mail: [email protected]

  • Tel: +39 02 2399 3951

  • Fax: +39 02 2399 2700

  • Skype: jacopo.cassina

Thanks for your kind attention.


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