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

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

2. 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

3. 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.

4. 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

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. 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. 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

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: • 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

9. Sub-System: DHW heat exchanger • FAILURE : limestone on the plates decrease the heat exchange capacity; • CAUSES: limestone contained in the water;

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. 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.

12. Training Data Sets

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.

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. 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. 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.