Method and a control system for monitoring the condition of an industrial robot
AssigneeABB Research Ltd.
InventorsNiclas Sjöstrand, Dominique Blanc, Shiva Sander Tavallaey
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Abstract
An industrial robot diagnostic method including performing a condition analysis utilizing at least two selected input signals, wherein each selected input signal indicates a condition related to a property of the industrial robot, performing an analysis of any combination of the selected input signals utilizing a signal modeling of the signals and outputting from the condition analyzer a result being at least one of: a detection of a malfunction of the robot system, an identification of a root cause failure in the robot system and prediction of a potential malfunction in the robot system. Also an industrial robot system utilizing the method.
Claims12
An industrial robot system condition analysis method, comprising:
- monitoring at least two selected input signals, each selected input signal indicating a condition related to a property of the industrial robot system,
- using a processor for analyzing any combination of said selected input signals utilizing a multivariate data analysis to identify combinations of input signals and/or relationships between the input signals, wherein said multivariate data analysis of a selection of said signals comprises
- formatting the data of n selected signals as data points in an n-dimensional space, classification of state of each data point, wherein said state indicate one of:
- normal operation or failure, and
- determining a direction of each of said data points indicating a direction in said n-dimensional space in which the data point tend to move, and
- detecting condition changes in the robot system based on a result of said analysis.
A computer readable medium embodying a computer program product, the computer program product comprising:
- computer program instructions recorded on the computer readable medium and executable by a processor for performing a method comprising monitoring at least two selected input signals, each selected input signal indicating a condition related to a property of the industrial robot system, analyzing any combination of said selected input signals utilizing a multivariate data analysis to identify combinations of input signals and/or relationships between the input signals, wherein said multivariate data analysis of a selection of said signals comprises formatting the data of n selected signals as data points in an n-dimensional space, classification of state of each data point, wherein said state indicate one of:
- normal operation or failure, and determining a direction of each of said data points indicating a direction in said n-dimensional space in which the data point tend to move, and detecting condition changes in the robot system based on a result of said analysis.
An industrial robot system condition analysis method, comprising:
- monitoring at least two selected input signals, each selected input signal indicating a condition related to a property of the industrial robot system,
- using a processor for analyzing any combination of said selected input signals utilizing a multi layer principal component analysis approach, wherein said multi layer principal component analysis approach comprises
- selecting sensor signals of the robot system,
- pre-processing said sensor signals by means of calculation of high frequency RMS, peak-to-peak, crest factor, kurtosis and peak-to-mean values of the signals,
- performing a principal component analysis model of a normal behavior of the robot system based on said pre-processing of the selected signals, and
- determining a severity factor Q, representing a deviation from said normal behavior of the robot system, and
- detecting condition changes in the robot system based on the result of said analysis.
A computer readable medium embodying a computer program product, the computer program product comprising:
- computer program instructions recorded on the computer readable medium and executable by a processor for performing a method comprising monitoring at least two selected input signals, each selected input signal indicating a condition related to a property of the industrial robot system, analyzing any combination of said selected input signals utilizing a multi layer principal component analysis approach, wherein said multi layer principal component analysis approach comprises selecting sensor signals of the robot system, pre-processing said sensor signals by means of calculation of high frequency RMS, peak-to-peak, crest factor, kurtosis and peak-to-mean values of the signals, performing a principal component analysis model of a normal behavior of the robot system based on said pre-processing of the selected signals, and determining a severity factor Q, representing a deviation from said normal behavior of the robot system, and detecting condition changes in the robot system based on the result of said analysis.
Dependencies are inferred from the claim text.