Deep Learning

Bearing Fault Detection and Classification Based on Temporal Convolutions and LSTM Network in Induction Machine Systems.

GRU Results

Downsampling	Sequence Length	Train (Samples)	Test (Samples)	Classes
Yes	899	3095	890	16
No	1000	-	-	16

Run	Epoch	Batch Size	Architecture	Weights	Downsampling	Accuracy
1	600	128	#1	1	Yes	71.46%
2	1200	128	#1	2	Yes	84.26%
3	1200	128	#2	3	Yes	84.94%
4	1200	128	#3	4	Yes	87.52%
5	900	64	#1	5	Yes	83.37%
_	_	_	_	_	_	_
6	200	128	#4	-	No	71.35%
7	500	128	#4	-	No	94.65%
8	650	128	#4	-	No	95.37%
9	700	128	#4	-	No	95.48%
10	900	128	#4	-	No	95.62%
11	1050	128	#4	-	No	95.77%
_	_	_	_	_	_	_
12	600		#5	-	No	91.91%
13	850		#5	-	No	93.70%
14	1000		#5	-	No	92.43%
15	1050		#5	-	No	92.43%

Downsampling (Yes)

#1 Architecture

Conv1D(128) => Conv1D(128) => Conv1D(128) => Conv1D(64)

#2 Architecture

Conv1D(128) => Conv1D(256) => Conv1D(256) => Conv1D(64)

#3 Architecture

Conv1D(64) => Conv1D(128) => Conv1D(256) => Conv1D(64)

Downsampling (No)

#4 Architecture

Conv1D(128) => Conv1D(128) => Conv1D(128) => Conv1D(64)

#4 Architecture Results

#5 Architecture

Conv1D(64) => Conv1D(64) => Conv1D(64) => Conv1D(128) => Conv1D(64)

#5 Architecture Results