Get documents about "Failure Prognosis for Permanent Magnet AC Drives Based on Wavelet Analysis Wesley G Zanardelli Elias G Strangas and Selin Aviyente zanardel egr msu edu strangas egr msu edu aviyente egr msu edu
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Failure Prognosis for Permanent
Magnet AC Drives Based on
Wavelet Analysis
Wesley G. Zanardelli, Elias G. Strangas, and Selin Aviyente
zanardel@egr.msu.edu strangas@egr.msu.edu aviyente@egr.msu.edu
•Objectives •Linear Discriminant Analysis •Linear discriminant analysis applied to the 64
•Detection of non-catastrophic faults in Permanent Dk (x ) = x1 1k + x2 2k + , , + x N Nk + N +1,k k = 1,2 ,...,K samples beginning 8 samples prior to where detection
Magnet AC machines which lead to reduced life and •Categorization of xi into j occurred
eventually failure D j (x ) > Dk (x ) for every k ≠ j
•Stator Faults •Iterative training procedure for the weighting •Experimental Setup
•Insulation failures coefficients makes adjustments to j and l following an
•Resistance changes initial guess, where
Dl (x ) = max l ≠ j [D1 (x ), , DK (x )]
j (i + 1) = j (i ) + axi l (i + 1) = l (i ) − axi
•Discrete Wavelet Transform (DWT) •and a is a gain constant
•Wavelets have finite energy concentrated around a
point which helps to localize irregularities in a signal
•Can give a sparse representation of a fault •Faults Explored
•Can choose different basis functions (mother wavelets) •Series Resistance (5 and 10ms) •Typical Results
Iq for Series Resistance Fault (10ms) Iq for Turn-Phase Short (10ms)
to achieve the best results for a specific application •Intermittent increased series contact resistance 28 29
Current (A)
Current (A)
•Coefficients can be realized using a filter bank •A normally closed switch and a resistance in parallel 26
24 28
22
•FIR filter coefficients h1 and h0 based on the scaling are added in series with one of the motor phases 20
18
27
and wavelet functions Inverter Electronic
Switch
PMAC
Motor
6
UDWT for Series Resistance (10ms)
6
UDW T for Turn-Phase Short (10ms)
5 5
Scale
Scale
h1 ( −n ) 2 dj R 4
3
4
3
c j +1 h1 ( −n ) 2 d j −1
•Turn-to-Phase Short (5 and 10ms) 2
1
2
1
h0 ( − n ) 2 cj •Insulation failure in the stator windings of the motor 4
Classification
4
Classification
h0 ( − n ) 2 c j −1 •A normally open switch is added between a 3 3
2 2
winding and its corresponding phase 1 1
0 0
2.1 2.2 2.3 2.4 2.1 2.2 2.3 2.4
Time (s) Time (s)
•Undecimated Discrete Wavelet Transform •Algorithm Performance
(UDWT) •Analysis Methods
•Shift-invariant representation of the DWT •Field oriented currents are used since the fundamental Number of False
Fault Inception Fault Clearing
Test Description Total / Detected / Total / Detected /
•Realized using the “Algorithme à Trous”, which omits electrical frequency is not present Detections
Classified Correctly Classified Correctly
downsampling and inserts zeros between filter •UDWT applied to measured q-axis current Healthy 0 0/0/0 0/0/0
Series Resistance (5ms) 0 2/2/2 2/2/2
coefficients at each successive scale •Daubechies D4 wavelet used Series Resistance (10ms) 0 2/2/2 2/2/2
•Decomposition performed for 6 scales Turn-to-Phase Short (5ms) 0 2/1/1 2/2/2
Original Signal DWT of Original Signal UDW T of Original Signal
Turn-to-Phase Short (10ms) 0 2/2/2 2/2/2
28
26
4 4 •Inception and clearing of faults are identified separately
Amplitude
24 3 3
Scale
Scale
22 2 2 •Detection Algorithm
20
18
Original Signal
1
DWT of Original Signal
1
UDW T of Original Signal
•A threshold is applied to the weighted energy of the •Conclusions
(S hifted by 6 Samples) (Shifted by 6 Samples ) (Shifted by 6 S amples)
28
26
4 4 UDWT at each time instant •Detection and classification of machine faults which
Amplitude
24 3 3
•Threshold is set to 40% greater than the largest
Scale
Scale
22
20
2 2 manifest themselves in the stator current is achieved
18
100 120 140 160
1
100 120 140 160
1
100 120 140 160
observed on healthy motors •Data from an exhaustive set of operating conditions is
Sample Number Sample Number Sample Number
•Classification Algorithm necessary to develop a robust algorithm
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