{"id":1022,"date":"2020-10-14T09:35:47","date_gmt":"2020-10-14T09:35:47","guid":{"rendered":"https:\/\/www.uptime-predictive-maintenance.com\/?page_id=1022"},"modified":"2021-01-27T10:12:47","modified_gmt":"2021-01-27T10:12:47","slug":"steel-industry-2","status":"publish","type":"page","link":"https:\/\/www.uptime-predictive-maintenance.com\/index.php\/steel-industry-2\/","title":{"rendered":"Steel Industry"},"content":{"rendered":"\n
\n
\n
\n

Steel Industry: cold rolling process in
a corrosive environment<\/h1>\n<\/div><\/div>\n\n\n\n
\n
<\/div><\/div><\/div>\n<\/div><\/div>\n<\/div><\/div><\/section>\n<\/div><\/section>\n\n\n\n
\n
\n
\n
Are all of the maintenance
activities necessary or can
maintenance intervals
be expanded?<\/h6>\n<\/div><\/div>\n\n\n\n
\n

For MAILLIS, UPTIME platform monitors and predicts the wear on the moving parts of the equipment based on real time data, coming from a group of sensors.  Evaluating the deviation from the healthy state, Maillis becomes capable to predict future machine conditions; that way, the company\u2019s maintenance policy relates directly to the level of the equipment\u2019s health. At the end of the day, the cost of maintenance drops, allowing further improvements on the critical parts\u2019 entire life cycle.<\/p>\n<\/div><\/div>\n<\/div><\/div><\/section>\n<\/div><\/section>\n\n\n\n

\n
\n
\n

<\/i><\/p>\n\n\n\n

Sensors operating <\/strong>
in an extremely aggressive environment<\/strong><\/p>\n\n\n\n

<\/p>\n<\/div><\/div>\n\n\n\n

\n

<\/i><\/p>\n\n\n\n

Time to change over the milling rollers > 8 h leads to a big impact on the OEE<\/strong><\/p>\n<\/div><\/div>\n\n\n\n

\n

<\/i><\/p>\n\n\n\n

24 hours a day <\/strong>
running production<\/strong><\/p>\n\n\n\n

<\/p>\n<\/div><\/div>\n<\/div><\/div><\/section>\n<\/div><\/section>\n\n\n\n

\n
\n
\n
\n