Precom

CUSTOMER PROFILE

SAKANA

Low-volume manufacturing

Soraluce milling centre in use at Sakana, producing wind turbines. Critical components monitored:

Spindle head
Spindle gearbox
Power source units
Machine geometry

SPINEA

High-volume manufacturing

Danobat Overbeck grinding machine in use at Spinea, producing reduction gears. Critical components monitored:

Spindle head
Grinding spindle
Power source units
Process load and vibrations

GOMA CÀMPS

Continuous manufacturing

Paper factory at Goma Càmps, with components from Lantier. Critical items monitored:

Yankee dryer roll
Suction press roller
Roll-forming roll
Creping scraper

PROJECT GOALS

To implement and test a cognitive system for maintenance decision support, capable of:

Identifying and locating damages and evaluate their severity
Predicting the evolution of damages
Evaluating asset remaining useful life and decrease the likelihood of false alarms
Providing an early detection of failures that is more precise, to enable preventive maintenance actions whenever necessary
Optimizing maintainability
Increasing efficiency of equipment in service
Sharing information effectively amongst users
Costs reduction

PROVIDING SOLUTIONS

As a key partner, Savvy has applied and customized their technologies in order to digitize, end to end, the entire data life cycle into the PreCoM platform.

To achive the goal of obtaining a greater digital integration of machines into their operating environment, a novel set of tools has been designed, centered around the Smart eMaintenance Decision Support System (Smart eMDSS) component, that seamlessly integrates with Savvy's data collection system.

With this, PreCoM is capable of gathering complex data from a wide variety of factory-level sources, such as intelligent sensors, condition analysis systems for hydraulic units, compressors, cranes, condition analysis software, etc.

For data gathering, Savvy leverages their "Savvy Smart Box". Once collected and processed, data is relayed to "Savvy Industrial Cloid", and from there to the PreCoM cloud automatically, where the Smart eMDSS analyzes, diagnoses, predicts and suggests what, where, when and how to act.

Thanks to this analysis pipeline, data is transformed into valuable information for the customer.

VERIFIED
RESULTS

SPINEA

Improvements to availability, performance and OEE, with the latter (8.6%) generating a benefit of 11.085,65 units (base profit margin being 1).

Maintainability and availability:

20% faster maintenance
70% reduced time for supervising new hires & staff
Time saved by utilizing the communication tool (around 15 minutes lower wait time per employee)
Reduction in the number of expected errors when performing maintenance tasks

Parameter	Value during Period 1	Value during Period 2
Availability	92.5%	99.5%
Total failure soppage (down time)	535 hr	32 hr
Planned production time / loading time	7200 hr	7200 hr
Production Performance	81.3%	84.4%
Actual cycle time	0.27 hr	0.26 hr
Theoretical cycle time	0.225 hr (average)	0.225 hr (average)
Quality	99.7%	99.5%
Rejected items	73 items	133 items
Total items	24089 items	26889 items
Overall equipment effectiveness	75%	83.6%
Total failure stoppage related to PreCoM monitored components	-	-
No. of failures	19	21
No of failures related to PreCoM monitored components	-	-
Overall process effectiveness	0.92	0.99

GOMA CÀMPS

Marginal improvements to quality and OEE. The slight OEE improvement (0.9%) resulted in a benefit of 7.13 (base profit margin being 1).

Implemented predictive maintenance:

Through the solutions provided by PreCoM, a problem in the Yankee bearing was detected, which was repaired during scheduled maintenace before it could break. An unplanned maintenance would have cost 40,000€ and 3 days of stopped production.

Maintainability and availability:

Invaluable for enablement purposes and ramp-up for recent onboards (new staff can perform maintenance unsupervised from the start)
Fewer expected errors when performing maintenance tasks

Parameter	Value during Period 1	Value during Period 2
Availability	98.2%	98.2%
Total failure soppage (down time)	228 hr	97 hr
Planned production time / loading time	13011 hr	5417 hr
Production Performance	76%	76.8%
Actual production rate	4.1 t/hr	4.2 t/hr
Theoretical production rate	5.5 t/hr	5.5 t/hr
Quality	98.3%	98.6%
Rejected items	1.6% of total quantity	1.4% of total quantity
Total quantity	53500 tons	22500 tons
Overall equipment effectiveness	73.5%	74.4%
No. of failures	112	44
Total failure stoppage related to PreCoM monitored components	8 hr	40 hr
No. of failures related to PreCoM monitored components	1	2
Saved hours due to PreCoM	-	72 hr
Overall process effectiveness	0.96	0.96

SAKANA

Implemented predictive maintenance:

A problem was detected in the spindle through the solutions provided by PreCoM. Estimated impact of not repairing was a complete work.week (5 days), 100 hours of production at 200€/hour.

Maintainability and availability:

Reported time savings in knowledge transfer and ramp-up of new hires (around 15%-20%), as well as requiring lower supervision from expert colleagues (only the first time as opposed to the first three).