Manufacturing Data Encoding for NaN-Resilient Attention Models
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Solution Overview
Problem
Manufacturing datasets often contain high percentages of missing and undefined data, particularly NaN values, which pose significant challenges for neural networks, reducing their ability to learn effectively and leading to inefficiencies in prediction and regression tasks.
Innovation Solution
A method involving a tabular data encoder that converts manufacturing datasets into a reduced form by removing undefined values, such as NaN, through a sparse tensor representation, followed by a series of attention-based neural network layers with specific regularization techniques to enhance model generalizability and precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If NaN values are removed from the dataset, then the model training reliability improves, but the sequence length decreases
Solution Approach 1:
The patent extracts and removes NaN values from the manufacturing dataset before feeding data to the neural network. This extraction of harmful elements (NaN values) improves model training reliability by preventing propagation of undefined values through the network, while the encoding mechanism preserves the essential information structure.
Solution Approach 2:
The patent changes the parameter representation by encoding manufacturing data into a format suitable for neural network processing. This parameter transformation allows the model to work with cleaned data while maintaining the meaningful relationships in the dataset, effectively resolving the contradiction between reliability and sequence length.
2Object-affected harmful factors
If encoding is applied to remove NaN values, then the harmful factors are reduced, but the device complexity increases
Solution Approach 1:
The patent introduces an intermediary encoding process that transforms manufacturing data into a neural network-compatible format. This intermediary step acts as a mediator between the raw data containing NaN values and the neural network, eliminating harmful factors while managing complexity through systematic transformation rather than ad-hoc handling.
Solution Approach 2:
The encoding and NaN removal is performed as a preliminary action before data enters the neural network. By addressing the harmful factors in advance, the system prevents NaN propagation throughout training, reducing the need for complex runtime handling mechanisms and simplifying the overall system architecture.
3Measurement precision
If data is cleaned and encoded, then the prediction accuracy improves, but the processing time increases
Solution Approach 1:
Data cleaning and encoding are performed as preliminary actions during the data preparation phase rather than during model training or inference. This timing strategy allows the system to invest processing time upfront when data is static, thereby improving prediction accuracy without significantly impacting the time-critical training and inference operations.
Solution Approach 2:
The systematic parameter changes through encoding transform the data into a more efficient representation for neural network processing. While the encoding step requires processing time, the transformed data structure enables faster and more accurate model training and inference, ultimately improving the overall efficiency-accuracy trade-off.
Data Source
AI summary
A systems and methods for implementing attention-based neural networks, attention modules, regularization techniques, and unique data encoding such as for sequential tabular data and/or manufacturing data is provided. The attention-based neural networks may include a high dropout and unique softmax regularization. The encoding may attend to missing or undefined data as well as numerous data types common to manufacturing data.