A method and apparatus may include receiving a signal from a motor. The signal is received while the motor is operating. The method also includes performing a pre-processing of the signal. The method also includes inputting the signal to a 1D convolutional neural network. The method also includes detecting a fault of the motor based on the output of the neural network.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, comprising: receiving a signal from a motor, wherein the signal is received while the motor is operating; performing a pre-processing of the signal; inputting the signal to a 1D convolutional neural network; and detecting a fault of the motor based on an output of the neural network. 2. The method according to claim 1, wherein receiving the signal comprises receiving the signal from an induction motor. 3. The method according to claim 1, wherein performing the pre-processing of the signal comprises at least one of down-sampling the signal, filtering the signal, and normalizing the signal. 4. The method according to claim 1, wherein receiving the signal comprises receiving a motor current signal. 5. The method according to claim 1, wherein the 1D convolution neural network is configured to perform a plurality of back-propagation iterations, and to update weights and bias sensitivities of the 1D convolution neural network for each back-propagation iteration. 6. The method according to claim 1, wherein detecting the fault of the motor comprises detecting a bearing fault of the motor. 7. An apparatus, comprising: at least one processor; and at least one memory including computer program code, the at least one memory and the computer program code configured, with the at least one processor, to cause the apparatus at least to receive a signal from a motor, wherein the signal is received while the motor is operating; perform a pre-processing of the signal; input the signal to a 1D convolutional neural network; and detect a fault of the motor based on an output of the neural network. 8. The apparatus according to claim 7, wherein receiving the signal comprises receiving the signal from an induction motor. 9. The apparatus according to claim 7, wherein performing the pre-processing of the signal comprises at least one of down-sampling the signal, filtering the signal, and normalizing the signal. 10. The apparatus according to claim 7, wherein receiving the signal comprises receiving a motor current signal. 11. The apparatus according to claim 7, wherein the 1D convolution neural network is configured to perform a plurality of back-propagation iterations, and to update weights and bias sensitivities of the 1D convolution neural network for each back-propagation iteration. 12. The apparatus according to claim 7, wherein detecting the fault of the motor comprises detecting a bearing fault of the motor. 13. A computer program product embodied on a non-transitory computer readable medium, said computer readable medium having instructions stored thereon that, when executed by a computer, causes the computer to perform a method, comprising: receiving a signal from a motor, wherein the signal is received while the motor is operating; performing a pre-processing of the signal; inputting the signal to a 1D convolutional neural network; and detecting a fault of the motor based on an output of the neural network. 14. The computer program product according to claim 13, wherein receiving the signal comprises receiving the signal from an induction motor. 15. The computer program product according to claim 13, wherein performing the pre-processing of the signal comprises at least one of down-sampling the signal, filtering the signal, and normalizing the signal. 16. The computer program product according to claim 13, wherein receiving the signal comprises receiving a motor current signal. 17. The computer program product according to claim 13, wherein the 1D convolution neural network is configured to perform a plurality of back-propagation iterations, and to update weights and bias sensitivities of the 1D convolution neural network for each back-propagation iteration. 18. The computer program product according to claim 13, wherein detecting the fault of the motor comprises detecting a bearing fault of the motor.