CROSS-REFERENCE TO RELATED APPLICATION

The present application claims priority to Korean Patent Application No. 10-2018-0160833, filed on Dec. 13, 2018, the entire contents of which is incorporated herein for all purposes by this reference.

BACKGROUND OF THE PRESENT INVENTION

Field of the Invention

The present invention relates to a noise data artificial intelligence apparatus and a pre-conditioning method for identifying the source of problematic noise, as a Deep Learning based artificial intelligence problematic noise source diagnostic technology for irregular noise such as vehicle noise, in particular, power train noise.

Description of Related Art

A vehicle is a finished body having many parts assembled therein.

The power system of the vehicle has inherent noise and vibration characteristics.

However, the noise information generated by a combination of many parts is very large.

The noise is different from operating noise in a normal case and problematic noise in an abnormal case.

When the problematic noise occurs, the vehicle is often placed in an abnormal state.

However, it is very difficult to determine which portion of the vehicle has a failure by use of only problematic noise information generated by the vehicle having a very complicated structure.

For example, in the case of a powertrain of the vehicle, it is very difficult to find the area where the problematic noise occurs.

This is because the problematic noise of the vehicle occurs irregularly.

Therefore, conventionally, noise experts have diagnosed it through sound evaluation and have found the source of the problematic noise depending upon their past experience.

At the present time, it may take a long time even if many noise experts use a sensor or other test conditions.

Nevertheless, it was very difficult to accurately diagnose the source of the problematic noise.

Recently, efforts have been made to find the source of the problematic noise by use of noise data, but there is no proper solution therefor.

The information included in this Background of the Invention section is only for enhancement of understanding of the general background of the invention and may not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.

BRIEF SUMMARY

Various aspects of the present invention are directed to providing a noise data artificial intelligence apparatus and a pre-conditioning method for identifying the source of problematic noise for maximizing the asset value of the noise big data accumulated for each type of noise data by use of an artificial intelligence technique.

The present invention includes a noise data artificial intelligence learning method for identifying the source of problematic noise including a noise data pre-conditioning method for identifying the source of problematic noise including selecting a unit frame for the problematic noise among noises sampled with time; dividing the unit frame into N segments; analyzing frequency characteristic for each segment of the N segments and extracting a frequency component of each segment by applying Log Mel Filter; and outputting a feature parameter as one representative frame by averaging information on the N segments, and an artificial intelligence learning by the feature parameter extracted according to a change in time by the noise data pre-conditioning method applies Bidirectional RNN.

Furthermore, the sampling samples in the range of twice a problematic frequency band.

Furthermore, an overlap is set between the unit frame with time and a next unit frame.

Furthermore, the artificial intelligence learning additionally is configured to apply Deep Neural Network (DNN).

Furthermore, the artificial intelligence learning additionally applies Attention Mechanism.

Furthermore, the artificial intelligence learning is configured to additionally apply an Early stage ensemble algorithm.

Furthermore, when the time axis of the problematic noise learning data is constantly collected, an Ensemble model of jointly trained RNNs algorithm using both a time-frequency map and an engine RPM-frequency map for improving the accuracy is additionally applied.

Furthermore, in a noise data artificial intelligence diagnostic apparatus implemented with a noise data artificial intelligence learning method for identifying the source of problematic noise, the noise of a vehicle or a powertrain is directly measured by an input means of the apparatus, or the stored noise data is provided through a storage medium.

Firstly, it is possible to apply the concept of the multiple diagnostic system configured for outputting the diagnostic result for the noise source input from 1stto nthin a probabilistic technique considering the complicated noise source in the powertrain, diagnosing the source of the problematic noise having low characteristic.

Secondly, it is possible to generate the model learned based on the big data for the problematic noise of the powertrain, probabilistically diagnosing precisely the complex and irregular powertrain noise or the vehicle noise.

Thirdly, it is possible to accurately diagnose the source of the problematic noise in a very short time.

Fourthly, it is possible to use GRU, DNN, Attention Mechanism, and Early Stage ensemble algorithms, preventing the data for past time from being lost.

Fifthly, it is possible to additionally apply the weight of the important portion of the learning data by applying the Attention Mechanism, accurately establishing the learning model for the irregular noise.

Sixthly, it is possible to efficiently improve the effort required for data analysis and determination by a person (professional engineer).

The methods and apparatuses of the present invention have other features and advantages which will be apparent from or are set forth in more detail in the accompanying drawings, which are incorporated herein, and the following Detailed Description, which together serve to explain certain principles of the present invention.

