White Goods Appliance

 

Predictive Maintenance in Complex Automatic Production Line of White Goods

The Use Case deals with a complex automatic production line to produce drums for dryer. The product is basically a carbon steel cylinder used to keep and rotating clothes during drying stage. The equipment is a sequence of different steps involving many operations and requiring the synchronized action of mechanical, electrical, hydraulic and pneumatic tool and moving parts.

The process is the very first step for producing a dryer and has to guarantee a high overall efficiency. The maintenance plan is usually suggested by supplier based on the equipment ledger: a visual plan compiled according to our internal Whirlpool Production System. Currently only preventive and reactive maintenance are implemented. In the future vision the equipment systems will provide data and information about their current and future health status along with actions recommendations as well as appropriate visualizations.

Interview with Whirlpool Corporation

Interview with UPTIME project partner Pierluigi Petrali, Manager of Manufacturing R&D of Whirlpool Corporation, who is based in Italy. He shared his views with us on the benefits of predictive maintenance for Whirlpool’s complex automatic production line, which produces drums for clothes dryers. The Whirlpool use case deals with the newly installed production line for clothes dryers at their Polish factory in Lodz.

How has Whirlpool performed maintenance activities so far?

 

The drum production equipment is very complex, highly automated and critical from many perspectives. It is critical to ensure the highest possible quality of the drum, which is the core component of the clothes dryer. At the same time, it is vital to keep the production equipment running efficiently and keep costs under control. The equipment in question involves mechanical, hydraulic, electrical and electronic apparatuses. The maintenance of these assets has to be organized and managed at a World Class level.  

Thus, maintenance activities in Whirlpool factories are organized according to World Class Manufacturing principles. In particular, Early Equipment Management, Autonomous Maintenance and Professional Maintenance pillars provide the guidelines and the roadmap to evolve towards Zero Breakdown Maintenance. Currently, we adopt reactive and preventive maintenance approaches using SAP-PM as a major supporting tool to manage the activities. Maintenance is managed mostly with internal resources. Whirlpool only requires support from external suppliers in major cases of severe damage.

How will UPTIME improve your maintenance service performance?

 

The capability of the UPTIME system to predict future failures of the Drum Line and to give indications about prognostic measures will modify the preventive maintenance plan allowing us to anticipate planned intervention on components, and thus reduce unexpected breakdowns and delay other interventions, and thus save money. The new preventive maintenance plan, modified according to predictions, will be more efficient and will impact on most important key performance indicators. 

We expect the “Mean Time Between Failures” to increase thanks to the fact that some unforeseen breakdowns will be predicted by the system, allowing maintenance to act before the component breaks. Moreover, the “Mean Time to Repair” is expected to decrease thanks to the fact that the maintenance action will be planned in advance, and thus optimising equipment downtime. All these effects will also positively impact the total cost of maintenance thanks to an optimized management of spare parts, technicians scheduling and improved effectiveness.

PUBLIC DELIVERABLES

DELIVERABLE 5.1: Whirlpool Business Case, Conceptualisation and Evaluation Strategy

D5.1 provides comprehensive definition of the Whirlpool Business case, including as-is and to-be business processes and identification of Whirlpool stakeholders/business, system and technical requirements.

 

SCIENTIFIC PUBLICATIONS

Predictive Maintenance in a Digital Factory Shop-Floor: Data Mining on Historical and Operational Data Coming from Manufacturers’ Information Systems

Pertselakis M., Lampathaki F., Petrali P. (2019).. In: Proper H., Stirna J. (eds) Advanced Information Systems Engineering Workshops. CAiSE 2019. Lecture Notes in Business Information Processing, vol 349. Springer, Cham

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