Background:
With the explosive growth of the manufacturing data, the manufacturing
enterprises paid more and more attention to dealing with the manufacturing big data. The manufacturing
big data also can be summarized as "5Vs”, volume, variety, velocity, veracity and value. Recently,
the researchers are focused on proposing better knowledge discovery algorithms to handling
the manufacturing big data.
Objective:
The high dimensional data can be reduced from two directions. The one was the dimension
reduction. It makes the data set simple and overcome the problem of curse dimensionality.
This method reduced the data set form the data width.
Methods:
We proposed a hybrid data reduction and knowledge extraction algorithm (HDRKE) for
quality prediction. There are 5 steps in the algorithm: Step 1: Data preprocessing; Step 2: Dimension
reduction; Step 3: Extract SVs by SVM; Step 4: Extract rules from the subset; Step 5: Prediction
by the rules extracted in step 3.
Results:
The presented HDRKE method reduced the data scales from the data dimensions and the
data attributions. Then, the prediction method was used on the subset of reduced data. At last, the
HDRKE method was applied to a enterprise sample, the validation of the method can be validated
on the enterprise sample.
Conclusion:
Quality prediction and control was an important procedure in manufacturing. The
HDRKE algorithm was a novel method based on the attribution reduction and dimensionality reduce.
The data set simplified from double direction made the data set easily to calculate. The
HDRKE method also proposed a new thought of decision rules extracting on the low-embeddings.
The HDRKE method also applied to a manufacturing instance and proved its validity.
Parameter space dimension reduction of an adaptive interpolator during multidimensional signal differential compression