[vsskanth]

Fleet management solutions are built for EDL requirements. They have event based logging and won't log data at high sample rates and don't send it to a server of your choice because they want you to use their subscription based dashboard.

[trevett]

Curious which applications require logging at high sample rates vs. doing event-based?

[vsskanth]

From my narrow experience as an R&D engineer in the auto sector:

You need to understand the duty cycle of the platform you're developing for (ex. Tires for a vehicle). A garbage truck is going to have a different loading cycle from an EV or a CAT morning truck. If you know the right conditions your product will fail on durability.

Fleets are ok with collecting this data for R&D as long as you don't bother them with extra work and give them useful analytics. Data collection devices need to be easy to install, collect for a few weeks and remove.

You can also use this as an R&D platform to develop IoT data-enabled products like predictive maintenance, route analytics, insurance and warranty claims etc. ML has a lot of potential here but you need to collect data.

Eventually when you have an IoT product you can get a vendor to make an optimized ASIC to collect and process exactly what signals you want.

There are many more applications in the auto industry for these kind of niche products.

[HeyLaughingBoy]

Vehicle crash analysis for one.

There's an existing product called the SlamStick that's used to record high-shock/high-vibration environments. They released a paper on using one to find the source of a troubling vibration in a Navy helicopter. I came across them when doing research for a customer in the Vehicle Dynamics space.

It's one of those little niches that no one knows about and seems to support quite a few small companies.