Quick start:
Click the start/pause button to start/pause the training process accordingly. The training loss vs. testing
loss graph will be updated every batch. The intermediate outputs, final training output, final testing
output, and weights are updated every epoch.
Functionalities
Graph description
Please click on each of the graphs to view the graph in details.
After loading, the system displays the input in the form of the heat-maps, one heat-map
per sensor data column. The system does not display the axes for graphs at the input layer. Instead, it
gives their descriptions on top of each layer for the simplicity of the visual display. The x-axis of the
heat-map is the engine units (e.g., 0-99 for dataset 1). Its y-axis is the sensor data sequence (the number
of cycles). The heat-map color at a specific point represents the sensor's value of a corresponding engine
at a
time step accordingly. The input units are also sorted by their RUL to see if they appear to have any sensor
input pattern. The first panel of the picture above shows the details view of a sample input for one sensor.
Similar to the input layer, the outputs from LSTM layers are also sequences with the corresponding number of
engines and steps. Therefore, the system also represents them as heat-map (second panel) with a similar
specification. The second panel of the picture above shows the details view of a sample LSTM layer output.
Outputs of the Dense layers, and the final training and testing outputs are alike and are visualized as
scatter plots. Y-axis also represents the number of engines, and x-axis describes the output
values. The green circles at the scatter plot represent the predicted outputs of the corresponding engine
units as
inputs. The gray x symbols are the actual target RULs. The two are scaled linearly to the domain of target
RULs for better visualization. The second panel of the picture above shows the details view of a sample
Dense layer output.