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

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juliette soula
juliette soula on 12 Nov 2021
Commented: juliette soula on 13 Nov 2021
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
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juliette soula
juliette soula on 13 Nov 2021
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 ?

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