deepSignalAnomalyDetector
Syntax
Description
d = deepSignalAnomalyDetector(numChannels)d based on a 1-D convolutional
autoencoder.
d = deepSignalAnomalyDetector(numChannels,modelType)
d = deepSignalAnomalyDetector(___,Name=Value)
Examples
Load the file sineWaveAnomalyData.mat, which contains two sets of synthetic three-channel sinusoidal signals.
sineWaveNormal contains 10 sinusoids of stable frequency and amplitude. Each signal has a series of small-amplitude impact-like imperfections. The signals have different lengths and initial phases. Plot the first three normal signals.
load sineWaveAnomalyData.mat sl = 3; tiledlayout("vertical") ax = zeros(sl,1); for kj = 1:sl ax(kj) = nexttile; plot(sineWaveNormal{kj}) title("Normal Signal") end
sineWaveAbnormal contains three signals, all of the same length. Each signal in the set has one or more anomalies.
All channels of the first signal have an abrupt change in frequency that lasts for a finite time.
The second signal has a finite-duration amplitude change in one of its channels.
The third signal has spikes at random times in all channels.
Plot the three signals with anomalies.
for kj = 1:sl plot(ax(kj),sineWaveAbnormal{kj}) title(ax(kj),"Signal with Anomalies") end
Create an autoencoder object to detect the anomalies in the abnormal signals. The object contains a neural network that you can train to best reproduce an input set of anomaly-free data. The trained object encodes the patterns inherent in the data and can recognize if a signal contains regions that deviate from the patterns. You do not need to label any data to train the detector. The training process is unsupervised.
By default, the deepSignalAnomalyDetector function creates objects with convolutional autoencoders. The only mandatory argument is the number of channels in the training and testing signals.
D = deepSignalAnomalyDetector(sl)
D =
deepSignalAnomalyDetectorCNN with properties:
IsTrained: 0
NumChannels: 3
Model Information
ModelType: 'convautoencoder'
FilterSize: 8
NumFilters: 32
NumDownsampleLayers: 2
DownsampleFactor: 2
DropoutProbability: 0.2000
Threshold Information
Threshold: []
ThresholdMethod: 'contaminationFraction'
ThresholdParameter: 0.0100
Window Information
WindowLength: 1
OverlapLength: 'auto'
WindowLossAggregation: 'mean'
Use the trainDetector function to train the convolutional neural network with the normal data. Use the training options for the adaptive moment estimation (Adam) optimizer. Specify a maximum number of 100 epochs to use for training. For more information, see trainingOptions (Deep Learning Toolbox).
opts = trainingOptions("adam",MaxEpochs=100);
trainDetector(D,sineWaveNormal,opts) Iteration Epoch TimeElapsed LearnRate TrainingLoss
_________ _____ ___________ _________ ____________
1 1 00:00:02 0.001 0.49381
50 50 00:00:07 0.001 0.06591
100 100 00:00:10 0.001 0.047143
Training stopped: Max epochs completed
Computing threshold...
Threshold computation completed.
Use the trained detector to detect the anomalies in the abnormal data. The detect function outputs two cell arrays, with each array element corresponding to a signal in the testing data.
[lbls,loss] = detect(D,sineWaveAbnormal);
The first output of detect is a categorical array that declares each sample of a signal as being anomalous or not. A sample can be either a point, a signal region, or an entire signal. An anomaly is detected when the reconstruction loss, or the difference between the value of a signal and the value reconstructed by the detector based on the training data, exceeds a given threshold. You can set a threshold manually or you can direct the detector to compute a threshold based on a specified statistic of loss values computed on training data.
for kj = 1:sl hold(ax(kj),"on") plot(ax(kj),lbls{kj},LineWidth=2) title(ax(kj),"Signal with Anomalies") hold(ax(kj),"off") end
Alternatively, use the plotAnomalies function to plot each channel of each signal and the anomalies found by the detector. The detector decides that there is an anomaly in a signal if it detects one in any of the signal channels. plotAnomalies plots the anomaly locations in all channels.
for kj = 1:sl figure plotAnomalies(D,sineWaveAbnormal{kj}) end
The second output of detect is the computed reconstruction loss that the object uses to determine the labeling. You can plot the reconstruction loss directly from the output of detect, but it is more convenient to use the plotLoss function, which also displays the threshold value above which a sample is declared to be anomalous.
tiledlayout("vertical") for kj = 1:sl nexttile plotLoss(D,sineWaveAbnormal{kj}) end
The plotLossDistribution function displays the reconstruction loss distribution and gives insight into the way the detector chooses the threshold. You can plot the distribution loss for the normal data alongside the distribution loss for the abnormal data. The threshold chosen by the detector is larger than the loss values for most of the normal-data samples but smaller than an appreciable number of abnormal-data losses that signal the possible presence of anomalies. You can improve the choice of threshold by using the updateDetector function.
clf plotLossDistribution(D,sineWaveNormal,sineWaveAbnormal)
Use the getModel function to obtain the underlying neural network model corresponding to the detector. You can then inspect the model using analyzeNetwork (Deep Learning Toolbox).
M = getModel(D); analyzeNetwork(M)
Input Arguments
Name-Value Arguments
Output Arguments
Algorithms
Extended Capabilities
Version History
Introduced in R2023aSee Also
Objects
deepSignalAnomalyDetectorCNN|deepSignalAnomalyDetectorLSTM|deepSignalAnomalyDetectorLSTMForecaster
Functions
Blocks
- Deep Signal Anomaly Detector (DSP System Toolbox)
Topics
- Detect Anomalies in Signals Using deepSignalAnomalyDetector
- Detect Anomalies in Machinery Using LSTM Autoencoder
- Detect Anomalies in ECG Data Using Wavelet Scattering and LSTM Autoencoder in Simulink (DSP System Toolbox)
- Detect Anomalies in Industrial Machinery Using Three-Axis Vibration Data (Predictive Maintenance Toolbox)
- Anomaly Detection Using Convolutional Autoencoder with Wavelet Scattering Sequences
