Adaptive, on line, statistical method and apparatus for detection of broken bars in motors by passive motor current monitoring and digital torque estimation
First Claim
1. An adaptive, on line, statistical method for motor fault detection of broken bars by passive motor current monitoring comprising the steps of:
- monitoring a motor current signal during a learning stage;
estimating motor torque to detect load changes;
transforming the current signal into time-frequency spectra;
using the load changes to divide the time-frequency spectra into a plurality of segments representative of good operating modes;
estimating a representative parameter of each segment;
determining a respective boundary of each segment;
monitoring the motor current signal during a test stage to obtain test data, the test stage occurring after the learning stage;
comparing the test data with the representative parameter and the respective boundary of each respective segment to detect the presence of a fault in a motor.
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Abstract
During a learning stage a motor current signal is monitored, and estimated motor torque is used to transform the current signal into a time-frequency spectra including a plurality of segments representative of good operating modes. A representative parameter and a respective boundary of each segment is estimated. The current signal is monitored during a test stage to obtain test data, and the test data is compared with the representative parameter and the respective boundary of each respective segment to detect the presence of a fault in a motor. Frequencies at which broken bar faults are likely to occur in a motor can be estimated using the estimated motor torque, and a weighting function can highlight such frequencies during estimation of the parameter. The current signal can be further subdivided into the segments by monitoring sidebands of the frequency components of current spectrum strips of each segment. Estimating the parameter and the boundary of each segment can include calculating a segment mean (the representative parameter) and variance for each frequency component in each respective segment; calculating a modified Mahalanobis distance for each strip of each respective segment; and for each respective segment, using respective modified Mahalanobis distances to calculate a respective radius about a respective segment mean to define the respective boundary.
74 Citations
16 Claims
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1. An adaptive, on line, statistical method for motor fault detection of broken bars by passive motor current monitoring comprising the steps of:
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monitoring a motor current signal during a learning stage; estimating motor torque to detect load changes; transforming the current signal into time-frequency spectra; using the load changes to divide the time-frequency spectra into a plurality of segments representative of good operating modes; estimating a representative parameter of each segment; determining a respective boundary of each segment; monitoring the motor current signal during a test stage to obtain test data, the test stage occurring after the learning stage; comparing the test data with the representative parameter and the respective boundary of each respective segment to detect the presence of a fault in a motor. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. An adaptive, on line, statistical method for induction motor broken bar fault detection by passive stator current monitoring comprising the steps of:
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monitoring a stator current signal and estimating motor torque during a learning stage; transforming the stator current signal into a time-frequency spectra including a plurality of segments representative of good operating modes by dividing the current signal into a plurality of portions each having a specified length of time, transforming each of the plurality of portions into a respective current spectrum strip, monitoring changes in the estimated motor torque, and monitoring sidebands of harmonics of each respective current spectrum strip; estimating frequencies at which broken bars faults are likely to occur in an induction motor using the estimated motor torque; estimating a representative parameter and a respective boundary of each segment using a weighting function to highlight the frequencies at which broken bar faults are likely to occur; monitoring the stator current signal during a test stage to obtain test data, the test stage occurring after the learning stage; and comparing the test data with the representative parameter and the respective boundary of each respective segment and monitoring sidebands of harmonics of the test data to detect the presence of a fault in the induction motor. - View Dependent Claims (11)
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12. A passive motor broken bar fault detection apparatus comprising:
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a current sensor for monitoring a motor current signal during a learning stage, and for monitoring the motor current signal during a test stage to obtain test data, the test stage occurring after the learning stage; a computer for, during the learning stage, estimating motor torque, transforming the current signal into a time-frequency spectra including a plurality of segments representative of good operating modes using the estimated motor torque, and estimating a representative parameter and a respective boundary of each segment, and, during the test stage, for comparing the test data with the representative parameter and the respective boundary of each respective segment to detect the presence of a fault in a motor. - View Dependent Claims (13, 14, 15, 16)
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Specification