The advent of good remote monitoring is a big plus in this area. My question would be what can be done to automate the response to any events that show up in the monitoring? If the lubricant is monitored for particulates such as metals then any rapid rise in particulate count would indicate a problem. In this case, it would seem there is not much possibility for an automated solution other than to reduce the likelihood of further damage by shutting down the system until the damage to metal parts can be repaired.
In the event the lubricant becomes contaminated it would seem to be a good idea to include a conditioning system to remove contaminates like water if possible. Perhaps this conditioning system operates continuously anyway and then any out of acceptable condition must be addressed with some kind of on-site visit.
Temperature of the lubricant under load is another factor and any system like this presumably has some automatic controls in place to maintain the correct temperatures. It might be possible to monitor this system so that any increase in coolant flow over time can be detected. This would help in predicting deterioration or some other adverse effect in the temperature monitoring and controls. If the amount of cooling is steadily increasing the automatic system is adjusting to the condition correctly. However it would be good to know in advance when the ability to increase cooling was about to reach a limit.
I recall a proposed system design using Helium as the working fluid and as the bearing lubricant. Since it was a sealed system the claim was made that this could theoretically operate for 30 years without much attention as long as the correct operating conditions were maintained.
Robots that walk have come a long way from simple barebones walking machines or pairs of legs without an upper body and head. Much of the research these days focuses on making more humanoid robots. But they are not all created equal.
The IEEE Computer Society has named the top 10 trends for 2014. You can expect the convergence of cloud computing and mobile devices, advances in health care data and devices, as well as privacy issues in social media to make the headlines. And 3D printing came out of nowhere to make a big splash.
For industrial control applications, or even a simple assembly line, that machine can go almost 24/7 without a break. But what happens when the task is a little more complex? That’s where the “smart” machine would come in. The smart machine is one that has some simple (or complex in some cases) processing capability to be able to adapt to changing conditions. Such machines are suited for a host of applications, including automotive, aerospace, defense, medical, computers and electronics, telecommunications, consumer goods, and so on. This discussion will examine what’s possible with smart machines, and what tradeoffs need to be made to implement such a solution.