Another example could be temperature and humidity measurements.
e.g.
at 9am: temp 10°, humidity 60%
at 10am: temp 13°, humidity 57%
Each point in time would have two values. In this example the input data has two channels.
With kernel 2 and stride 1, the convolution will look at successive pairs of timestep, looking at two values for each timestep because there are two channels.
If you specify 3 output channels then the conv1d will be applied 3 times to the input with 3 different sets of weights, and produce 3 output values per timestep.
I hope this is becoming clearer.