how to feed "MachineData.mat" raw data from "anomaly detection" into biLSTMAutoencoder ?

Hi all

Since I've no access to he Diagnostic Feature Designer App from the Predictive Maintenance Toolbox, and as suggested in "Part1_DataPrepFeatureExtraction", I'm trying to "train a model on raw data" => train the biLSTM with "MachineData.mat" instead of "FeatureEntire.mat".

To have "MachineData.mat" compatible with "Part2_LSTMAutoencoder.mlx", I've modified "extractLabeledData.m" file to create a [18x1] cells [70000x3 double] => 18 is the number of sequences, 70000 number of samples, 3 number of channels.

the train result is "The training sequences are of feature dimension 70000 but the input layer expects sequences of feature dimension 1." => Clearly not exepected...

Anybody know how to adapt the shape of "MachineData.mat" ?

Instead of trying to feed the 18 sequences, should I proceed sequence per sequence and try to retarin the network ?

BR

Juliette

[juliette soula]

As a test, I've set "featureDimension = 70000;" in "Part2_LSTMAutoencoder.mlx" => the training process is performed but with very poor performances. I think it is not the right thing to do because samples of a time serie should not be considered as dimensions.

is there a vocabulary problem ?

How should these "nbTimeSerie" time series "nbSamplee" long from 3 sensor should be presented to the biLSTM ?