Method for identifying root cause failure in a multi-parameter self learning machine application model
First Claim
1. A method for identifying root cause failure in a multi-parameter self learning machine application model comprising:
- providing at least one multi-function sensor having the capability to measure at least one of a voltage and current of the machine application model;
measuring voltages and currents of a multi-phase load with the multi-function sensors in a passive manner which includes sensing existing voltages and currents;
storing and accepting the measured voltages and currents into memory by a computer controlled analog to digital converter;
calculating at least one of a time-varying variable KW, PF, kVAr, or Z out of the measured voltages and currents;
calculating at least one of a first, second or third order derivative of the at least one time-varying variable;
classifying segments of at least one of the time-varying variables depending on a state;
choosing at least one of the calculated time-varying variables and learning their normal behavior;
comparing the normal behavior to a pattern difference;
identifying a root cause meaning to the pattern difference;
plotting Min, Max and Median values of the measured voltages and currents in a candlestick chart format; and
determining the directions with which the measured voltages and currents are heading using the Min, Max and Median values.
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Abstract
A method for identifying root cause failure in a multi-parameter self learning machine application model is presented. At least one multi-function sensor having the capability to measure at least one of a voltage and current of the machine application model is provided. The method includes measuring voltages and currents of a multi-phase load with the multi-function sensors in a passive manor and calculating at least one of a time-varying variable KW, PF, kVAr, or Z out of the measured voltages and currents. The method further provides calculating a first, second or third order derivative of the time-varying variable and classifying segments of at least one of the time-varying variables depending on a state. Then, a step of choosing at least one of the variables and learning their normal behavior is undertaken. Finally, normal behavior is compared to a pattern difference and a root-cause meaning to the pattern difference is identified.
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Citations
17 Claims
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1. A method for identifying root cause failure in a multi-parameter self learning machine application model comprising:
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providing at least one multi-function sensor having the capability to measure at least one of a voltage and current of the machine application model; measuring voltages and currents of a multi-phase load with the multi-function sensors in a passive manner which includes sensing existing voltages and currents; storing and accepting the measured voltages and currents into memory by a computer controlled analog to digital converter; calculating at least one of a time-varying variable KW, PF, kVAr, or Z out of the measured voltages and currents; calculating at least one of a first, second or third order derivative of the at least one time-varying variable; classifying segments of at least one of the time-varying variables depending on a state; choosing at least one of the calculated time-varying variables and learning their normal behavior; comparing the normal behavior to a pattern difference; identifying a root cause meaning to the pattern difference; plotting Min, Max and Median values of the measured voltages and currents in a candlestick chart format; and determining the directions with which the measured voltages and currents are heading using the Min, Max and Median values. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. A method for identifying root cause failure in a multi-parameter self learning machine application model comprising:
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providing at least one multi-function sensor having the capability to measure at least one of a voltage and current of the machine application model; measuring voltages and currents of a multi-phase load with the multi-function sensors in a passive manner; calculating at least one of a time-varying variable KW, PF, kVAr, or Z out of the measured voltages and currents; calculating at least one of a first, second or third order derivative of the at least one time-varying variable; classifying segments of at least one of the time-varying variables depending on a state; choosing at least one of the calculated time-varying variables and learning their normal behavior; comparing the normal behavior to a pattern difference; and identifying a root cause meaning to the pattern difference, wherein the derivatives of the voltages include a newly created variable for instantaneous voltage phasor that provides both amplitude and phase.
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15. A method for identifying root cause failure in a multi-parameter self learning machine application model comprising:
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providing at least one multi-function sensor having the capability to measure at least one of a voltage and current of the machine application model; measuring voltages and currents of a multi-phase load with the multi-function sensors in a passive manner; calculating at least one of a time-varying variable KW, PF, kVAr, or Z out of the measured voltages and currents; calculating at least one of a first, second or third order derivative of the at least one time-varying variable; classifying segments of at least one of the time-varying variables depending on a state; choosing at least one of the calculated time-varying variables and learning their normal behavior; comparing the normal behavior to a pattern difference; and identifying a root cause meaning to the pattern difference, wherein the derivatives of the currents include a newly created variable for instantaneous current phasor that provides both amplitude and phase.
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16. A method for identifying root cause failure in a multi-parameter self learning machine application model comprising:
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providing at least one multi-function sensor having the capability to measure at least one of a voltage and current of the machine application model; measuring voltages and currents of a multi-phase load with the multi-function sensors in a passive manner; calculating at least one of a time-varying variable KW, PF, kVAr, or Z out of the measured voltages and currents; calculating at least one of a first, second or third order derivative of the at least one time-varying variable; classifying segments of at least one of the time-varying variables depending on a state; choosing at least one of the calculated time-varying variables and learning their normal behavior; comparing the normal behavior to a pattern difference; and identifying a root cause meaning to the pattern difference, wherein the derivatives of the voltages and/or currents include a newly created variable for instantaneous impedance that provides both amplitude and phase.
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17. A method for identifying root cause failure in a multi-parameter self learning machine application model comprising:
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providing at least one multi-function sensor having the capability to measure at least one of a voltage and current of the machine application model; measuring voltages and currents of a multi-phase load with the multi-function sensors in a passive manner; calculating at least one of a time-varying variable KW, PF, kVAr, or Z out of the measured voltages and currents; calculating at least one of a first, second or third order derivative of the at least one time-varying variable; classifying segments of at least one of the time-varying variables depending on a state; choosing at least one of the calculated time-varying variables and learning their normal behavior; comparing the normal behavior to a pattern difference; and identifying a root cause meaning to the pattern difference, wherein the derivatives of the voltages and/or currents include a newly created variable for frequency that provides a delta angle of instantaneous voltage phasor or current phasor versus time.
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Specification