AC Motor Fault Detection - NN Classifier

import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, InputLayer, Dropout, Conv1D, Conv2D, Flatten, Reshape, MaxPooling1D, MaxPooling2D, AveragePooling2D, BatchNormalization, TimeDistributed, Permute, ReLU, Softmax from tensorflow.keras.optimizers import Adam EPOCHS = args.epochs or 1000 LEARNING_RATE = args.learning_rate or 0.0005 # this controls the batch size, or you can manipulate the tf.data.Dataset objects yourself BATCH_SIZE = 32 train_dataset = train_dataset.batch(BATCH_SIZE, drop_remainder=False) validation_dataset = validation_dataset.batch(BATCH_SIZE, drop_remainder=False) # model architecture model = Sequential() model.add(Reshape((int(input_length / 80), 80), input_shape=(input_length, ))) model.add(Conv1D(32, kernel_size=5, padding='same', activation='relu')) model.add(MaxPooling1D(pool_size=2, strides=2, padding='same')) model.add(Conv1D(32, kernel_size=5, padding='same', activation='relu')) model.add(MaxPooling1D(pool_size=2, strides=2, padding='same')) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(16, activation='relu', activity_regularizer=tf.keras.regularizers.l1(0.00001))) model.add(Dense(classes, name='y_pred', activation='softmax')) # this controls the learning rate opt = Adam(learning_rate=LEARNING_RATE, beta_1=0.9, beta_2=0.999) callbacks.append(BatchLoggerCallback(BATCH_SIZE, train_sample_count, epochs=EPOCHS)) # train the neural network model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy']) model.fit(train_dataset, epochs=EPOCHS, validation_data=validation_dataset, verbose=2, callbacks=callbacks) # Use this flag to disable per-channel quantization for a model. # This can reduce RAM usage for convolutional models, but may have # an impact on accuracy. disable_per_channel_quantization = False

Layer type
Dense Fully connected layer, the simplest form of a neural network layer. Use this for processed data, such as the output of a spectral analysis DSP block.
1D Convolution / pooling Learn features that take spatial information into account along a single dimension. Use this for raw data, or for DSP blocks that output spatial data, such as the MFCC block.
2D Convolution / pooling Learn features that take spatial information into account along two dimensions. Use this for raw data, or for DSP blocks that output spatial data, such as the MFCC block.
Reshape Turn one-dimensional data from a DSP block into multi-dimensional data. Use this as an input to a convolutional layer. Use this for deep learning on raw data, or to process MFCC output.
Flatten Flatten multi-dimensional data into a single dimension. You need to flatten data from a convolutional layer before returning.
Dropout Reduce the risk of a model overfitting your dataset by randomly cutting a fraction of network connections during training. Can be helpful if your model's training performance is better than its validation performance.

Model	Author
PyTorch MLP example (20x10 hidden layers) Enterprise Custom ML block with PyTorch	Edge Impulse Inc.
MCSA Enterprise A learning block designed specifically for the MCSA project.	Edge Impulse Inc.
Linear regression (scikit-learn) Enterprise Implementation of sklearn.linear_model.LogisticRegression	Edge Impulse Inc.

Neural Network settings

Training settings

Audio training options

Neural network architecture

Architecture presets

Model

Last training performance (validation set)

Accuracy

Loss

Confusion matrix (validation set)

Feature explorer (full training set)

On-device performance

Inferencing time

Peak RAM usage

Flash usage

Dense

1D Convolution / pooling

2D Convolution / pooling

Reshape

Flatten

Dropout

PyTorch MLP example (20x10 hidden layers)
Enterprise

MCSA
Enterprise

Linear regression (scikit-learn)
Enterprise

Description

Developer

Enterprise

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