I am solving a problem of predicting the failure of a water pump which causes a water supply disconnection.
There are various researchers are working on this problem, in this paper they have provides one of the solution to this problem and there are many more solutions are available. Hard Drive Failure Prediction- Modern hard drives are reliable devices, yet failures can be costly to users and many would benefit from a warning of potential problems that would give them enough time to backup their data.It will help to avoid all problems causes due to electricity disconnection in the industry, hospital, etc. Electricity board- We can monitor signals taken from the various distribution points of electricity and we can predict failure.Once a future failure is detected, they provide maintenance to the machine, it reduces maintenance cost because it provided only when there is a future failure and it also increases the life of the machine. Then ML take a step and give a prediction of failure effectively and efficiently. But when there huge data or the anomaly pattern in data is very hard to detect then SCADA can’t work. They had a system called SCADA which monitors signals and helps to predict the failure of the machine. Industries- As we discussed, in industry, it is very important to predict machine failure.Motivation:īefore delving deeper into this problem, we need to understand why it is important to address this problem, where it is used, or where it can be used. and give it to the ML model, and it tells us, let say within the next 10 minutes whether the machine will fail or function normally.
We take real-time data from the machine such as temperature, current at various point, spikes in signals, etc. If the appropriate data is available which is taken from the various sensors mounted on the machine, given to the ML model then we can predict future failure of that machine.Ī simple explanation of this problem is as follows.
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Therefore predicting the future failure of a machine is a very important task, but the question is how to do that? The answer is by using Machine Learning. Even if the machine fails and recovers in just a minute, still it causes a huge loss to the industry. They produce thousands of products in a minute, they can’t afford machine failure. Think of an industry where multiple machines work together in a cycle to produce the final product. This is an example cooked by me, but we may have seen a situation where a sudden failure of any machine of our use can cause a big problem for us. He is very angry and thinking that, if he would have been warned of this computer failure somehow then he could arrange another computer or find some other way to do this presentation.
He just lost his very important presentation due to his computer system failure, he can lose his job also. In one path group, if neither IP address is reachable, then that path group fails, and triggers a failover.A guy you can see in the image his name is Joe.
we have two path groups, each path group has two IP addresses to track, and the failure condition is all. If we have more then one Path group, and the Path monitoring has a failure condition of any, if any of the path group fails, then a failover is triggered.įor example. Suppose we have only one entry in the Path group. If the path monitoring has a failure condition of any, and the path group has a group failure condition of all, then all is preferred, because whatever's configured in the path group is preferred. If both interface 1/1 and 1/2 fail, then the link group fails and triggers a failover. If we have more then one link group and the link monitoring has a failure condition of any, if any of the link group fails, then a failover is triggered.įor example, say you have two link groups, each link group has two interfaces, and the failure condition of the link group is all. Suppose we have only one entry in the Link group. If the link monitoring has a failure condition of any and Link group has a group failure condition of all, then all is preferred, because whatever's configured in the Link group takes precedence. This document provides information about link and path monitoring behaviour.
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