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Plot signal anomalies

Since R2023a



    plotAnomalies(d,s) plots the anomalies in signal observation s detected by the detector d.


    To plot signal anomalies, the detector must be trained.

    plotAnomalies(d,s,Name=Value) specifies additional options using name-value arguments.

    f = plotAnomalies(___) returns the figure handle of the plot.


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    Load a convolutional anomaly detector trained with three-channel sinusoidal signals. Display the model, threshold, and window properties of the detector.

    load sineWaveAnomalyDetector
    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
    rnd = randperm(length(sineWaveNormal));
    for kj = 1:length(sineWaveAbnormal)
        title("Normal Signal")
        title("Signal with Anomalies")

    Use the trained anomaly detector to detect the anomalies in the abnormal data. Use the plotAnomalies function to plot each channel of each signal and annotate the anomalies found by the detector. The anomalies are assigned to all channels of a signal even when they are present just in one channel.

    for kj = 1:length(sineWaveAbnormal)

    Input Arguments

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    Anomaly detector, specified as a deepSignalAnomalyDetectorCNN object, a deepSignalAnomalyDetectorLSTM object, or a deepSignalAnomalyDetectorLSTMForecaster object. Use the deepSignalAnomalyDetector function to create d.

    Signal observation, specified as a vector.

    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: PlotReconstruction=true,ExecutionEnvironment="cpu" instructs the function to plot the reconstructed signal and use the computer CPU to detect anomalies.

    Option to plot the reconstructed signal, specified as either false or true.

    Data Types: logical

    Label priority for overlapped windows, specified as "anomaly" or "normal".

    • If you specify OverlapPriority as "anomaly", the function labels samples in an abnormal window overlapped with a normal window as abnormal.

    • If you specify OverlapPriority as "normal", the function labels samples in a normal window overlapped with an abnormal window as normal.

    This argument applies only the window overlap length is greater than zero.

    Data Types: char | string

    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

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    Figure handle of the plot, returned as a figure handle.

    Extended Capabilities

    Version History

    Introduced in R2023a