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Method and assistance system for parameterizing an anomaly detection method

An anomaly identification and parameterization technology, applied in general control systems, control/regulation systems, character and pattern recognition, etc., which can solve the problems of time-consuming calculation and ignoring current information.

Active Publication Date: 2021-05-18
SIEMENS AG
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Problems solved by technology

This automated approach has the drawback that the parameterization is generated according to rules that only approximate the optimal value for the specific situation without taking into account current information such as the user's contextual and domain knowledge
The calculation of cluster attributes by clustering algorithm is very time-consuming

Method used

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  • Method and assistance system for parameterizing an anomaly detection method
  • Method and assistance system for parameterizing an anomaly detection method
  • Method and assistance system for parameterizing an anomaly detection method

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Embodiment Construction

[0072] In order to perform data-based anomaly detection, for example for industrial installations or technical installations, it is necessary to select input parameters in the clustering method which characterize the clusters. These parameters can eg be input into the anomaly recognition device, and from the input parameters of the clustering of sensor data points the sensor data to be checked can be determined and the results of the clustering method can be output. Only on the basis of this clustering result can it be estimated whether the entered parameters lead to a meaningful clustering result for this field of application. It is therefore frequently necessary to rerun the cluster analysis with changed parameters. Since the time for determining the clustering result is time-consuming especially in the case of large data volumes, the parameters for the clustering method can be time-optimized by the following method and taking expert knowledge into account has been identifi...

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Abstract

A method for parameterizing an anomaly detection method, which takes a multiplicity of sensor data points as a basis for performing a density-based cluster method, comprising a) mapping (S10) each sensor data point (SP1, SP2, SP3) in a data space into a pixel data point (PP1, PP2, PP3) in a pixel space, b) reproducing (S11) at least one operation of the density-based cluster method in the data space by means of at least one pixel operation in the pixel space, c) receiving (S12) at least one parameter value for each parameter of the density-based cluster method, d) applying (S13) the at least one pixel operation in accordance with the parameter values to the pixel data points (PP1, PP2, PP3), e) outputting (S14) a cluster result in visual form in the pixel space, and f) providing (S16) the received parameter values for the anomaly detection method, and an assistance apparatus (80) for parameterizing an anomaly detection apparatus (90) that performs the anomaly detection method.

Description

technical field [0001] The present invention relates to a method, an auxiliary system and a computer program product for parameterizing an anomaly identification method, wherein the method approximates a density-based clustering method in real time based on a large number of sensor data points. Background technique [0002] Data-based anomaly detection is used in industrial installations to detect undesired or dangerous operating states of machines or other components of the installation at an early stage and to be able to react to this in a timely manner, for example by shutting down or repairing them. [0003] Examples of anomalies in industrial installations that should be detected with the aid of data mostly recorded by sensors are unintended reductions in production, equipment failures due to wear, wear phenomena, incorrect settings of equipment, but also reductions in demand, output degradation or loss of quality. [0004] Technical methods for anomaly detection are g...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B23/02G06F16/28G06F16/26
CPCG05B23/0221G05B23/0281G06F30/20G06T7/0004G06T2200/24G06T2207/10024G06T2207/30108G06F18/23
Inventor J·克雷尔S·H·韦伯C·保利奇
Owner SIEMENS AG
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