Automation method of AI-based diagnostic technology for equipment application
An automation method of an artificial intelligence (AI)-based diagnostic technology for equipment application includes receiving one or more pieces of data among vibration data, noise data, and controller area network (CAN) data, a data input processing operation of trimming the input data, an operation of extracting features from the trimmed data, setting a setting value of a hyper-parameter with respect to the one or more pieces of data thereamong, and generating a total of N models to include both of machine learning (ML) and deep learning (DL) as N individual models and generating ensemble prediction model structures for the N individual models. As a parameter updating is being proceeded due to the hyper-parameter so as to minimize values of cost functions of the N individual models, a reward for model accuracy performance is optimized and the ensemble prediction model structures of the N individual models change.
1. An automation method of an artificial intelligence (AI)-based diagnostic technology for equipment application, the automation method comprising:
receiving one or more pieces of data inputted from among vibration data, noise data, and controller area network (CAN) data, which are collected from a rotating body in a vehicle;
a data input processing operation of trimming the input one or more pieces of data;
an operation of extracting features from the trimmed one or more pieces of data;
setting a setting value of a hyper-parameter with respect to the input one or more pieces of data among the vibration data, the noise data, and the CAN data; and
generating a total of N models to include both of machine learning (ML) and deep learning (DL) as N individual models and generating ensemble prediction model structures with respect to the N individual models,
wherein, as a parameter updating is being proceeded due to the hyper-parameter so as to minimize values of cost functions of the N individual models, a reward with respect to model accuracy performance is optimized and the ensemble prediction model structures of the N individual models change.
2. The automation method of claim 1 , wherein, in the data input processing operation, the input one or more pieces of data is trimmed according to a problem frequency band and a data time length.
3. The automation method of claim 2 , wherein the trimmed one or more pieces of data is classified into a training dataset, a validation dataset, and a test dataset.
4. The automation method of claim 3 , wherein, in the operation of extracting, one algorithm or two or more algorithms for extraction of independent features are used according to a classification performance determination index, and an ensemble prediction model is selectively additionally applied.
5. The automation method of claim 4 , wherein:
when the two or more algorithms for extraction of the independent features are used, each of the two or more algorithms for extraction of the independent features has a weight value of 1:1; and
when the ensemble prediction model is selectively additionally applied, a sum of the weight values is one.
6. The automation method of claim 1 , wherein:
optimizing the hyper-parameter is performed by a grid search, a random search, or a random Latin hypercube automation algorithm; and
as the hyper-parameter is updated, an Auto ML/DL model structure is optimized.
7. The automation method of claim 6 , wherein, when the Auto ML/DL model structure, to which a final hyper-parameter is applied, is optimized, model verification is performed using a validation dataset, and evaluation of a final model is performed using a test dataset.
8. The automation method of claim 7 , wherein cost functions of the N individual models are confirmed, and then a robust model configuration is obtained by applying the cost functions of the N individual models to the ensemble prediction model structures, respectively.
9. The automation method of claim 8 , wherein a weight value is assigned to an individual cost function constituting a corresponding one of the cost functions applied to the ensemble prediction model structures.
10. The automation method of claim 1 , wherein the rotating body is a rotating body for power generation or power transmission.
11. An equipment to which the automation method of an artificial intelligence (AI)-based diagnostic technology for equipment application according to claim 1 is applied.