Thank you for joining the Asset Management Council in our third article on predictive maintenance. In our previous two articles, we gained an understanding of what predictive maintenance is, and data’s role within it. Today, we follow on from the latter, with a specific example in Melbourne.
If anyone reading has ever been on a tram in Melbourne, you’ll know the network is extensive and reliable. As a tram ambles along its tracks, most passengers have their heads down staring at their phones, or nodding off to music. Some on board might be alert for Myki inspectors, ready to clamber off to avoid a fine. Given these distractions, many on board would be unaware of how the smoothness of the tracks or weeds sprouting along the track might impact a tram’s journey.
This is the job of Spy-Tram, as it’s colloquially known: to collect every minute detail from Lygon Street to Wattletree Road, from Plenty Road to Acland Street. Spy Tram sets out at night under a blanket of darkness. Like any good spy, it blends into its surroundings by being just the right amount of forgettable. While doing so, again like any good spy, it collects information about the condition of the assets.
Spy-Tram, at first glance, is just an ordinary B-Class Tram. But if you’re one of the few who’ve noticed Spy-Tram doing its job, you will have noticed it’s far from ordinary. It has state-of-the-art 3D lasers, sensors and cameras attached on the front and back, top and bottom of the tram1,
collecting important data as it travels along the tram tracks. The data is mapped accurately using GPS. All the data collected by Spy-Tram informs maintenance plans, minor and comprehensive, to better prioritise works over the next five to fifteen years. Since 2018, twenty-five significant maintenance2 and renewal works have been completed.
That’s predictive maintenance done well.
Let us know your success stories with predictive maintenance. Get in touch by leaving a comment, or make contact through firstname.lastname@example.org today.
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