Robust sensor correlation analysis for machine condition monitoring
AssigneeSiemens Aktiengesellschaft
InventorsChao Yuan, Christian Balderer, Tzu-Kuo Huang, Claus Neubauer
Life of this patent
Estimating the term…
Abstract
A method for monitoring machine conditions is based on machine learning through the use of a statistical model. A correlation coefficient is calculated using weights assigned to each sample that indicate the likelihood that that sample is an outlier. The resulting correlation coefficient is more robust against outliers. The calculation of the weight is based on the Mahalanobis distance from the sample to the sample mean. Additionally, hierarchical clustering is applied to intuitively reveal group information among sensors. By specifying a similarity threshold, the user can easily obtain desired clustering results.
Claims12
A method for machine condition monitoring, comprising the steps of:
- in a machine condition monitoring system processor, determining a robust correlation coefficient ρxy between pairs of sensors using data from a group of samples (xi, yi) from the pairs of sensors, the robust correlation coefficient ρxy being determined by:
- initializing a weight wi for each sample (xi, yi), wherein 0≦wi≦1 and Σwi=1, each weight wi being proportional to an inverse of a distance between the sample (xi, yi) and a sample mean;
- estimating a mean μ and covariance matrix Ω of the sample as
- μ=Σwizi and Ω=Σwi(zi−μ)(zi−μ)T,
- wherein zi=[xiyi]T;
- updating the weight wi for each observation (xi,yi) as
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- repeating the estimating and updating steps until convergence;
- calculating the robust correlation coefficient as
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- where x=Σwixi and y=Σwiyi;
- and
- predicting sensor readings using the robust correlation coefficient.
A method for machine condition monitoring, comprising the steps of:
- receiving a group of readings from a plurality of sensors;
- in a machine condition monitoring system processor, for at least one pair of sensors (x, y) of the plurality of sensors, determining a robust correlation coefficient ρxy, using a plurality of samples (xi, yi) from the group of readings, and using a weight wi for each sample (xi, yi) based on how closely the sample obeys a joint distribution of the readings of the pair of sensors (x, y), the robust correlation coefficient being calculated as
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- where x=Σwixi and y=Σwiyi;
- and
- clustering the sensors in a hierarchical cluster scheme using distances calculated from the robust correlation coefficient ρxy.
A non-transitory computer-usable medium having computer readable instructions stored thereon for execution by a processor to perform a method comprising:
- receiving a group of readings from a plurality of sensors;
- for at least one pair of sensors (x, y) of the plurality of sensors, determining a robust correlation coefficient ρxy, using a plurality of samples (xi, yi) from the group of readings, and using a weight wi for each sample (xi, yi) based on how closely the sample obeys a joint distribution of the readings of the pair of sensors (x, y), the robust correlation coefficient being calculated as
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- where x=Σwixi and y=Σwiyi;
- and
- clustering the sensors in a hierarchical cluster scheme using distances calculated from the robust correlation coefficient ρxy.
Dependencies are inferred from the claim text.