# Problem Scenario

I am working on an industry specific problem focussed on predicting the failure of a seal/gasket in the given time interval(T) in a high-pressure-compression environment. Whenever this seal/gasket is broken there is loss of pressure and a leak. This leak is extremely dangerous. The gas in question is H2 and this makes things even scarier. The specific problem would be this, "Predict the likelihood of this Seal Surviving past a time Ti provided that the event has not happened yet". This Ti is typically in the future for example, 2 days, 2 weeks, 2 months etc.

# Dataset

The Dataset I have is timeseries, which are sensor measurements which have been collected over the past couple of months. Please note that these sensor readings are done every 100 ms and each machine has ~ 60 of these. There are 7 such machines where the gasket/seal had to be changed a couple of times (1-3 per machine) in the last year. So these would be the 'Event' in my prediction task. You could imagine the dataset as the following for each machine where S_1 to S_34 are the sensor readings for simplicity.

Timestamp;S_1;S_2;S_3;S_4;S_5;S_6;S_7;S_8;S_9;S_10;S_11;S_12;S_13;S_14;S_15;S_16;S_17;S_18;S_19;S_20;S_21;S_22;S_23;S_24;S_25;S_26;S_27;S_28;S_29;S_30;S_31;S_32;S_33;S_34
0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0 --> No Seal Change
0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0 --> No Seal Change
0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0 --> No Seal Change
0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0 --> No Seal Change
0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0 --> Seal had to be changed



P.S. I am aware this isn't in the typical format survival analysis data is structured.

# Problems I am facing - All related to Data Scarcity

1. There is a huge class-imbalance if I can call it that. The times the seal have been changed is very little compared to all the data being logged. Any suggestions on how I can tackle this would be appreciated?

2. Can I get suggestions for any other way to predict the likelihood of this Event happening in a given time Ti without Survival analysis if that is better?

3. I would also know of any other methods usually used in Survival Analysis, of restructuring the data in a certain way if that helps me solve the Class Imbalance?

# References

## Python Libraries

• First off, thanks for the reply. Point 1 This is not currently my area of concern and the goal of this project is to predict the likelihood of a seal breaking, it is assumed that the data is rich enough to contain a concept. Point 2 All the sensors note the readings at a certain time interval, but i feel that is more of a Data cleaning/Data pre-processing concern, Survival models output the predictions with respect to time, so the risk is evaluated days before it actually happens(atleast should ^^'), Point 3 This is not my concern too. Jun 17 '21 at 13:34
• I dont believe or dont believe in what you are saying that i do. It is just not my point of concern with respect to the questions asked. that is all. Jun 17 '21 at 14:14