Machine Failure Prediction.

Machine Failure Prediction.

Predictive maintenance is a technique used in various industries to reduce machine downtime by predicting its failure. It is fair to say that most enterprises consider this a difficult technique to deploy in production.

Know your machine breakdown, before a breakdown will happen.

Table of Contents:

  1. Introduction.
  2. Motivation.
  3. Problem definition.
  4. Data information.
  5. Exploratory Data Analysis.
  6. Filling NULL values.
  7. Performance metrics.
  8. Feature engineering approach 1
  9. Feature engineering approach 2
  10. Final result analysis
  11. Table of Results
  12. Conclusion and Future work
  13. References

1. Introduction:

What happen?

A guy you can see in the image his name is Joe. He just lost his very important presentation due to his computer system failure, he can lose his job also. 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. 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.

Think of an industry where multiple machines work together in a cycle to produce the final product. They produce thousands of products in a minute, they can’t afford machine failure. Even if the machine fails and recovers in just a minute, still it causes a huge loss to the industry.

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. 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.

A simple explanation of this problem is as follows. We take real-time data from the machine such as temperature, current at various point, spikes in signals, etc. 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.

2. Motivation:

Before 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.

  • Industries- As we discussed, in industry, it is very important to predict machine failure. They had a system called SCADA which monitors signals and helps to predict the failure of the machine. But when there huge data or the anomaly pattern in data is very hard to detect then SCADA can’t work. Then ML take a step and give a prediction of failure effectively and efficiently. 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.
  • Electricity board- We can monitor signals taken from the various distribution points of electricity and we can predict failure. It will help to avoid all problems causes due to electricity disconnection in the industry, hospital, etc.
  • *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. 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.

3. Problem definition:

I am solving a problem of predicting the failure of a water pump which causes a water supply disconnection. There is a water supply system to provide water to a big town and located far from that town. I have an observation of 5 months in which the water pump get failed 7 times. Those failures cause a huge problem for many people and also lead to some serious living problems for some families.

Some people are taking care of that water pump, they tried to analyze all the readings taken from the sensors mounted on a water pump but they failed to make sense out of it to predict the next failure. Hence they proposed this problem to solve by Machine Learning. We have to train a model on the given data and give warning of failure as soon as possible to the person who is taking care of that water pump so that he can take the required step.

It is a binary classification problem where we have to predict the state of the water pump, is it working normally or it is broken.

It is a binary classification problem where we have to predict the state of the water pump, is it working normally or it is broken.

4. Data information:

This problem is posted on Kaggle which is the world’s largest data science community by an unknown source. You can download data from here.

Data I have, is actually a time series data. You may ask what is time series data? A time series is a sequence taken at equally spaced points in time. This is a very simple definition of time series data.

imbalanced-dataset data-science towards-data-science time-series-analysis machine-learning

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