Manufacturing Sensor Sequence Modeling for Next-Station Prediction
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Solution Overview
Problem
Industrial machine failures in manufacturing processes lead to production disruptions and resource losses due to inadequate predictive maintenance, as existing technologies struggle to accurately forecast machine performance and detect potential failures in real-time.
Innovation Solution
A computer-implemented method and system using sequence modeling with embeddings generated from history and observed measurement data, processed through an encoding network and decoding network, to predict future measurements of parts as they traverse a station sequence, incorporating self-attention and cross-attention mechanisms within a transformer model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional predictive maintenance methods are used, then implementation complexity is reduced, but prediction accuracy and failure detection capability deteriorate
Solution Approach 1:
The patent segments the manufacturing process into discrete station sequences, with each station having its own embedding model. The system divides historical measurement data into station-specific segments and processes them through separate encoding networks, allowing complex predictions to be broken down into manageable station-level components that can be independently trained and optimized.
Solution Approach 2:
The patent introduces embedding vectors as intermediary representations between raw measurement data and prediction outputs. These embeddings serve as intermediate features that capture essential patterns from historical data at each station, acting as mediators that transform complex sensor readings into meaningful predictive features without requiring direct complex modeling of all raw data relationships.
2Reliability
If real-time sequence modeling is implemented, then failure detection capability is improved, but computational resource consumption increases
Solution Approach 1:
The patent performs preliminary encoding of historical measurement data at each station into compressed embedding representations during the offline training phase. These pre-computed embeddings capture essential failure patterns and are stored for rapid retrieval during real-time operation, allowing the system to perform accurate failure detection without reprocessing all historical data in real-time, thus reducing computational resource consumption during deployment.
3Measurement precision
If comprehensive historical data from multiple stations is utilized, then prediction accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent segments comprehensive manufacturing data into station-specific subsets, with each station having its own dedicated encoding network and embedding model. This segmentation allows the system to process and analyze data from multiple stations without requiring a single complex model to handle all data simultaneously, reducing data processing complexity while maintaining the benefits of comprehensive multi-station historical data for improved prediction accuracy.
Data Source
AI summary
A computer-implemented system and method include establishing a station sequence that a given part traverses. A history embedding sequence is generated and comprises (a) history measurement embeddings based on history measurement data, the history measurement data relating to attributes of at least one other part that traversed the plurality of stations before the given part, (b) history part identifier embeddings based at least one history part identifiers of at least one other part, and (c) history station identifier embeddings based on the at least one history station identifier corresponding to the history measurement data. An input embedding sequence is generated and comprises (a) measurement embeddings based on observed measurement data, the observed measurement data relating to attributes of the given part at each station of a station subsequence of the station sequence, (b) part identifier embeddings based on a part identifier of the given part, and (c) station identifier embeddings based on station identifiers corresponding to the observed measurement data. An encoding network generates intermediate history features based on the history embedding sequence. A decoding network generates predicted measurement data based on the intermediate history features and the input embedding sequence. The predicted measurement data includes next measurement data of the given part at a next station, where the next station follows the station subsequence in the station sequence.