plotLoss
Description
Examples
Plot Reconstruction Loss Indicating Signal Anomalies
Load a convolutional anomaly detector trained with three-channel sinusoidal signals. Display the model, threshold, and window properties of the detector.
load sineWaveAnomalyDetector
D
D = deepSignalAnomalyDetectorCNN with properties: IsTrained: 1 NumChannels: 3 Model Information ModelType: 'convautoencoder' FilterSize: 8 NumFilters: 32 NumDownsampleLayers: 2 DownsampleFactor: 2 DropoutProbability: 0.2000 Threshold Information Threshold: 0.0510 ThresholdMethod: 'contaminationFraction' ThresholdParameter: 0.0100 Window Information WindowLength: 1 OverlapLength: 'auto' WindowLossAggregation: 'mean'
Load the file sineWaveAnomalyData.mat
, which contains two sets of synthetic three-channel sinusoidal signals.
sineWaveNormal
contains the 10 sinusoids used to train the convolutional anomaly detector. Each signal has a series of small-amplitude impact-like imperfections but otherwise has stable amplitude and frequency.sineWaveAbnormal
contains three signals of similar length and amplitude to the training data. One of the signals has an abrupt, finite-time change in frequency. Another signal has a finite-duration amplitude change in one of its channels. A third has random spikes in each channel.
Plot three normal signals and the three signals with anomalies.
load sineWaveAnomalyData tiledlayout(3,2,TileSpacing="compact",Padding="compact") rnd = randperm(length(sineWaveNormal)); for kj = 1:length(sineWaveAbnormal) nexttile plot(sineWaveNormal{rnd(kj)}) title("Normal Signal") nexttile plot(sineWaveAbnormal{kj}) title("Signal with Anomalies") end
Use the plotLoss
function to display the reconstruction loss computed by the trained anomaly detector for each abnormal signal. Show the loss next to the corresponding signal. Signal regions where the loss exceeds a specified threshold are categorized as anomalous.
tiledlayout(3,2,TileSpacing="compact",Padding="compact") for kj = 1:length(sineWaveAbnormal) nexttile plot(sineWaveAbnormal{kj}) xlabel("Window Index") title("Signal No. " + kj) nexttile plotLoss(D,sineWaveAbnormal{kj}) end
Input Arguments
d
— Anomaly detector
deepSignalAnomalyDetectorCNN
object | deepSignalAnomalyDetectorLSTM
object | deepSignalAnomalyDetectorLSTMForecaster
object
Anomaly detector, specified as a deepSignalAnomalyDetectorCNN
object, a
deepSignalAnomalyDetectorLSTM
object, or a deepSignalAnomalyDetectorLSTMForecaster
object. Use the
deepSignalAnomalyDetector
function to create
d
.
data
— Signal data set
Nc-column matrix | M-element cell array | timetable | datastore
Signal data set, specified as one of these:
Nc-column matrix — A single multichannel signal observation (M = 1), where Nc is equal to the value of the
NumChannels
property of the detector.M-element cell array — M multichannel signal observations, where each cell contains an Nc-column matrix.
Timetable — A single multichannel signal observation, contained in a MATLAB® timetable. The timetable must contain increasing, uniformly-sampled, and finite values. The timetable can have:
A single variable containing an Nc-column matrix, where each column corresponds to a signal channel.
Nc variables, where each variable contains a vector that corresponds to a signal channel.
Datastore — A
signalDatastore
,audioDatastore
(Audio Toolbox), orarrayDatastore
object. The detector uses thereadall
function to read all the signal observations contained in the datastore at once. You can also use aCombinedDatastore
orTransformedDatastore
object containing any of the supported datastores.
Name-Value Arguments
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN
, where Name
is
the argument name and Value
is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.
Example: MiniBatchSize=64,ExecutionEnvironment="cpu"
instructs the
function to use a mini-batch size of 64 and use the computer CPU to detect
anomalies.
MiniBatchSize
— Mini-batch size
128
(default) | positive integer scalar
Mini-batch size used by the network to compute reconstructed signals, specified as a positive integer scalar.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
ExecutionEnvironment
— Execution environment
"auto"
(default) | "gpu"
| "cpu"
Execution environment used by the network, specified as one of these:
"auto"
— If available, use the GPU. If the GPU is not available, use the CPU."gpu"
— Use the GPU."cpu"
— Use the CPU.
Data Types: char
| string
Output Arguments
f
— Figure handle
figure handle
Figure handle of the plot, returned as a figure handle.
Extended Capabilities
GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.
The plotLoss
function
supports GPU array input with these usage notes and limitations:
The ExecutionEnvironment
option must be "gpu"
or
"auto"
when the input data is:
A
gpuArray
A cell array containing
gpuArray
objectsA datastore that outputs cell arrays containing
gpuArray
objects
For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).
Version History
Introduced in R2023a
See Also
Objects
deepSignalAnomalyDetectorCNN
|deepSignalAnomalyDetectorLSTM
|deepSignalAnomalyDetectorLSTMForecaster
Functions
detect
|getModel
|plotAnomalies
|plotLossDistribution
|resetState
|saveModel
|trainDetector
|updateDetector
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)
- Anomaly Detection in Industrial Machinery Using Three-Axis Vibration Data (Predictive Maintenance Toolbox)
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