Input Tensor Shape for CNN Binary Classification of Time Series Data

Question

I want to predict whether a machine will fail based on the most recent set of measurements taken by on-board sensors. I have several dozen machines, each with a sensor that takes a measurement at regular intervals. Some machines have already failed, but most have not. The resulting dataset looks something like the example data below, with one row for each machine, showing the 30 most recent sensor measurements as well as a "failure" designation, where 0 indicates that the machine is still operational, and 1 indicates that the machine failed after the measurement taken at time30.

    ID  time1   time2   time3   time4   time5   time6   time7   time8   time9   time10  time11  time12  time13  time14  time15  time16  time17  time18  time19  time20  time21  time22  time23  time24  time25  time26  time27  time28  time29  time30  failure
0   1   3.085   1.360   2.351   3.858   5.562   3.709   6.423   9.706   5.521   0.045   5.676   6.045   5.540   8.404   2.701   7.969   2.535   5.096   7.949   5.888   9.250   6.608   1.441   2.066   8.885   6.985   1.310   4.245   9.068   3.283   0
1   2   7.938   9.833   5.776   3.218   0.978   4.164   8.079   7.425   5.554   0.259   5.927   5.168   8.751   8.713   5.651   9.342   0.385   6.623   4.348   9.113   9.230   7.134   4.316   4.725   9.258   4.248   6.497   7.354   7.707   2.527   0
2   3   5.946   0.096   1.972   6.362   9.990   6.702   9.683   5.111   2.273   7.581   0.379   5.571   0.274   9.429   3.572   2.032   0.543   0.467   3.028   1.095   0.529   8.780   4.375   7.544   0.754   5.400   4.943   1.821   1.486   2.492   1
3   4   6.793   9.299   1.522   9.307   0.438   9.999   0.481   6.420   3.881   4.933   7.185   4.176   4.224   7.403   9.101   3.300   3.273   0.556   6.421   5.528   9.262   6.160   1.573   9.299   4.307   0.808   4.270   6.886   3.548   4.889   0
4   5   8.470   5.503   7.420   8.363   3.316   1.047   9.695   3.884   2.010   8.353   1.308   7.733   7.898   3.327   2.737   2.858   2.002   5.483   7.750   4.952   2.435   5.980   6.403   0.985   1.591   8.886   7.586   0.062   6.002   1.144   1

The Problem

I don't understand what shape my input tensors should take. Should my input tensor have the shape of (num_of_samples, 1, 30), or (num_of_samples, 30, 1), where the 30 is the number of time point measurements per sample? I've made a related post here that is more code-focused and has additional specifics about the structure of my CNN using PyTorch.

Answer 1

In this case, your input tensor should have the shape of (num_of_samples, 1, 30), where the 1 indicates the number of channels in the time series data and the 30 indicates the number of time points per sample. The input tensor shape of (num_of_samples, 30, 1) would be appropriate for data with 30 features and 1 time point per sample.