updateDetector
Update settings of trained detector and recompute detection threshold
Since R2023a
Description
updateDetector(
updates the threshold and window settings of the detector d
,data
,Name=Value
)d
, then
recomputes the detection threshold. For any name-value argument not specified, the function
uses the current value of the corresponding parameter in the detector.
Note
When ThresholdMethod
is set to "manual"
, you
do not need to specify input signal data set data
.
Examples
Update Detector Properties and Threshold
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
Update the anomaly detector using the training data.
Specify the regions to be labeled as anomalies as non-overlapping 50-sample windows.
Specify that the function compute the detection threshold as the mean window loss measured over the entire training data set and multiplied by
20
.
updateDetector(D,sineWaveNormal, ... WindowLength=50,OverlapLength=0, ... ThresholdMethod="mean",ThresholdParameter=20)
Plot the second test signal and annotate the anomalies found by the updated detector.
plotAnomalies(D,sineWaveAbnormal{2})
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.
WindowLength
— Window length
1
(default) | positive integer | "fullSignal"
Window length of each signal segment, specified as a positive integer or as "fullSignal"
.
If you specify
WindowLength
as an integer, the detector divides each input signal into segments. The length of each segment is equal to the specified value in samples.If you specify
WindowLength
as"fullSignal"
, the detector treats each input signal as a single segment.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| char
| string
OverlapLength
— Number of overlapped samples
"auto"
(default) | positive integer
Number of overlapped samples between window segments, specified as a positive integer or as "auto"
.
If you specify
WindowLength
andOverlapLength
as integers, the detector sets the number of overlapped samples to the specified value. The number of overlapped samples must be less than the window length.If you specify
WindowLength
as an integer andOverlapLength
as"auto"
, the detector sets the number of overlapped samples toWindowLength
– 1.If you specify
WindowLength
as"fullSignal"
, you cannot specifyOverlapLength
as an integer.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| logical
| char
| string
WindowLossAggregation
— Method to aggregate sample loss
"mean"
(default) | "max"
| "min"
| "median"
Method to aggregate sample loss within each window segment, specified as one of these:
"max"
— Compute the aggregated window loss as the maximum value of all the sample losses within the window."mean"
— Compute the aggregated window loss as the mean value of all the sample losses within the window."median"
— Compute the aggregated window loss as the median value of all the sample losses within the window."min"
— Compute the aggregated window loss as the minimum value of all the sample losses within the window.
The detector computes the detection loss of each sample within a window segment and aggregates the loss values over each window.
ThresholdMethod
— Method to compute detection threshold
"contaminationFraction"
(default) | "max"
| "mean"
| "median"
| "manual"
| "customFunction"
Method to compute the detection threshold, specified as one of these:
"contaminationFraction"
— Value corresponding to the detection of anomalies within a specified fraction of windows. The fraction value is specified byThresholdParameter
."max"
— Maximum window loss measured over the entire training data set and multiplied byThresholdParameter
."median"
— Median window loss measured over the entire training data set and multiplied byThresholdParameter
."mean"
— Mean window loss measured over the entire training data set and multiplied byThresholdParameter
."manual"
— Manual detection threshold value based onThreshold
."customFunction"
— Custom detection threshold value based onThresholdFunction
.
If you specify ThresholdMethod
, you can also specify ThresholdParameter
, Threshold
, or ThresholdFunction
. The available threshold parameter depends on the specified detection method.
ThresholdParameter
— Detection threshold
real scalar
Detection threshold, specified as a real scalar.
If
ThresholdMethod
is specified as"max"
,"mean"
, or"median"
, specifyThresholdParameter
as a positive scalar. If you do not specifyThresholdParameter
, the detector sets the threshold to 1.If
ThresholdMethod
is specified as"contaminationFraction"
, specifyThresholdParameter
as a nonnegative scalar less than 0.5. If you do not specifyThresholdParameter
, the detector sets the threshold to 0.01.If
ThresholdMethod
is specified as"customFunction"
or"manual"
, this argument does not apply.
Threshold
— Manual detection threshold
positive scalar
Manual detection threshold, specified as a positive scalar. This argument applies only when ThresholdMethod
is specified as "manual"
.
Use this option when you do not want the detector to compute a threshold based on training data.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
ThresholdFunction
— Function to compute custom detection threshold
function handle
Function to compute custom detection threshold, specified as a function handle. This argument applies only when ThresholdMethod
is specified as "customFunction"
.
The function must have two inputs:
The first input is a cell array of aggregated window loss values.
The second input is a cell array of sample loss values before aggregation.
Each cell contains a loss vector for one signal observation.
The function must return a positive scalar corresponding to the detection threshold.
Use this option when you want to compute a threshold based on training data.
Data Types: function_handle
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
Version History
Introduced in R2023a
See Also
Objects
deepSignalAnomalyDetectorCNN
|deepSignalAnomalyDetectorLSTM
|deepSignalAnomalyDetectorLSTMForecaster
Functions
detect
|getModel
|plotAnomalies
|plotLoss
|plotLossDistribution
|resetState
|saveModel
|trainDetector
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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