Method and device for diagnosing problematic noise source based on big data information

1. A method for diagnosing a problematic noise source based on big data information, comprising:

measuring noise data of a powertrain of a vehicle by using a real-time noise measurement device and vibration data of the vehicle using a vibration sensor, and acquiring an engine speed (RPM) from controller area network (CAN) data of the vehicle, wherein the noise data, the vibration data or the engine speed of the vehicle is stored at external storage data outside the vehicle;

converting the noise data, the vibration data, or the engine speed into a signal that can be input to a portable device for diagnosing the problematic noise source through an interface device, wherein the noise data, the vibration data, or the engine speed of the vehicle is resampled to obtain the converted signals, and a frequency of resampling is twice a maximum frequency of a problematic noise;

analyzing the noise through a deep learning algorithm of an artificial intelligence on converted signals of the noise data, the vibration data, and the engine speed, and diagnosing, from the converted signals, the problematic noise source as a cause of the noise; and

displaying the cause of the noise by outputting a diagnostic result as the problematic noise source, and transmitting the diagnostic result to the portable device.

2. The method according to claim 1 , wherein the noise data is acquired through a microphone and the vibration data is acquired through the vibration sensor.

3. The method according to claim 2 , wherein a bidirectional method and a gated recurrent unit (GRU) technique are applied to the deep learning algorithm.

4. The method according to claim 3 , wherein an attention mechanism technique is applied to the deep learning algorithm.

5. The method according to claim 3 , wherein an early stage ensemble learning technique is applied to the deep learning algorithm.

6. The method according to claim 5 , wherein the artificial intelligence is configured to reproduce a sound for identification of the problematic noise source.

7. The method according to claim 1 , wherein a bidirectional method and a gated recurrent unit (GRU) technique are applied to the deep learning algorithm.

8. The method of claim 7 , wherein an attention mechanism technique is applied to the deep learning algorithm.

9. The method according to claim 8 , wherein an early stage ensemble learning technique is applied to the deep learning algorithm.

10. The method according to claim 9 , wherein the artificial intelligence is configured to reproduce a sound for identification of the problematic noise source.

11. The method according to claim 1 , wherein the external storage data outside the vehicle is transmitted to the controller area network (CAN) data of the vehicle by Bluetooth.

12. A device for diagnosing a problematic noise source based on big data information, comprising:

a microphone for measuring noise data of a vehicle;

a vibration sensor for acquiring vibration data of the vehicle;

a controller area network (CAN) module for acquiring an engine speed of the vehicle; and

a controller for converting the noise data, the vibration data, and data regarding the engine speed of the vehicle into codes and receiving a diagnosis result from an artificial intelligence,

wherein the noise data or the vibration data of the vehicle is stored at external storage data outside the vehicle,

wherein the noise data, the vibration data, or the engine speed of the vehicle is resampled to obtain the converted codes,

wherein a frequency of resampling is twice a maximum frequency of a problematic noise, and

wherein the artificial intelligence diagnoses the problematic noise source based on the converted codes of the noise data, the vibration data or the engine speed by deep learning.

13. A device for diagnosing a problematic noise source based on big data information, comprising:

an input data collector for collecting input data, the input data collector comprising:

a microphone for measuring noise data of a vehicle;

a vibration sensor for acquiring vibration data of the vehicle;

a controller area network (CAN) module for acquiring an engine speed of the vehicle; and

a memory for storing problematic noise regions from the noise data, the vibration data, and data regarding engine speed, wherein the noise data or the vibration data of the vehicle is stored at external storage data outside the vehicle;

a controller for converting the noise data or vibration data into codes and receiving a diagnosis result from an artificial intelligence, wherein the noise data, the vibration data, or the engine speed of the vehicle is resampled to obtain the converted codes, and a frequency of resampling is twice a maximum frequency of a problematic noise; and

a problematic noise diagnosis unit for diagnosing the problematic noise source based on the codes by deep learning of the artificial intelligence, wherein the artificial intelligence diagnoses the problematic noise source based on converted codes of the noise data, the vibration data or the engine speed by the deep learning,

wherein the input data collector and the problematic noise diagnosis unit are separable from each other.