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

2. Jacopo Cassina – MM 2006 2Agenda 1.Aims of the work 2.The PROMISE Project 3.Consumer Goods Scenario 4.Used tool 5.Methodology 6.Merloni Termo Sanitari application 7.Comparison with another algorithm 8.Results and Further Development

3. Jacopo Cassina – MM 2006 3Aims 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.

4. Jacopo Cassina – MM 2006 4PROMISE 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

5. Jacopo Cassina – MM 2006 5 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

6. Jacopo Cassina – MM 2006 6 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

7. Jacopo Cassina – MM 2006 7Methodology 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: 1.definition of the monitored breakdowns 2.definition of the sub-system to be controlled 3.selection of the variables to be controlled for each sub-system 4.analysis of the whole product and selection of the minimum number of variables and sensors 5.design of the experiments 6.experimentation 7.training of the algorithm 8.test and validation of the algorithm

8. Jacopo Cassina – MM 2006 8 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: 1.The domestic hot water service failure 2.The flame turn off 3.The burning efficiency reduction 4.The failure of the water pumps Second step: Definition of the corresponding Sub-Systems. 1.The domestic hot water Heat Exchanger 2.The flame sensor - The burner 3.The burner 4.The Water Pump

9. Jacopo Cassina – MM 2006 9 Sub-System: DHW heat exchanger FAILURE : limestone on the plates decrease the heat exchange capacity; CAUSES: limestone contained in the water;

10. Jacopo Cassina – MM 2006 10 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

11. Jacopo Cassina – MM 2006 11 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. Antecedent / Consequents P-OutP-INOut-SanGasAGINGweights 29,4040,2029,204953,15,001,00 33,3045,3032,804973,7575,001,00 38,5052,6037,504953,15,001,00 39,9054,7038,805035,7295,001,00 49,5054,9051,401606,6435,001,00 54,2072,5041,505115,85850,001,00 54,7073,2041,905063,79950,001,00 54,8073,3042,105063,79950,001,00 54,9073,3042,105063,79950,001,00 55,1073,5042,505032,56350,001,00 34,3042,3023,904973,757100,001,00 34,9043,0024,105004,743100,001,00 36,1044,4024,604953,1100,001,00 36,6045,0024,804973,757100,001,00 37,2045,7025,104953,1100,001,00

12. Jacopo Cassina – MM 2006 12 Training Data Sets

13. Jacopo Cassina – MM 2006 13 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. Antecedent / Consequents P-OutP-IN Sec- OUTGasAGINGDate 5070485132 19,50741 24-giu-05 4666415170 36,70522 15-lug-05 5675485095 42,09512 15-set-05 5777475132 48,99102 15-ott-05 5873515123 54,78653 15-nov-05 5774485023 62,18932 15 dec 05 5876505132 66,65374 15-gen-05 5864501620 72,37012 14-feb-06

14. Jacopo Cassina – MM 2006 14 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

15. Jacopo Cassina – MM 2006 15 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)

16. Jacopo Cassina – MM 2006 16 Ing. Jacopo Cassina e-mail: jacopo.cassina@polimi.it Tel: +39 02 2399 3951 Fax: +39 02 2399 2700 Skype: jacopo.cassina Thanks for your kind attention.

17. Jacopo Cassina – MM 2006 17 Soft Computing Techniques Soft computing concerns the integration of different techniques, such as expert system, fuzzy logic, neural network and genetic algorithms, aimed to build machine intelligence. The guiding principle of soft computing is: Exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost (Jin).

18. Jacopo Cassina – MM 2006 18 Predictive Maintenance Based on the degradation monitoring, diagnosis and prognosis. Generates over costs: If the product is replaced earlier than needed If a the failure has not been predicted For sensors, data recording and analysis. Many papers have been written about maintenance of plants or complex and expensive machines, as far as we know few on consumer goods

19. Jacopo Cassina – MM 2006 19Methodology 1.definition of the monitored breakdowns 2.definition of the sub-system to be controlled 3.selection of the variables to be controlled for each sub-system 4.analysis of the whole product and selection of the minimum number of variables and sensors 5.design of the experiments 6.experimentation 7.training of the algorithm 8.test and validation of the algorithm