Configurable Data Processing Workflows for Predictive Maintenance
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Solution Overview
Problem
In production facilities and plants, it is challenging to set up appropriate data processing due to fixed processes and orders in software or applications used for predictive maintenance and abnormality detection, leading to suboptimal data analysis settings.
Innovation Solution
An information processing apparatus and method that include a storage unit for definition information, an acceptance unit for processing patterns, and a generating unit that creates processing setting information based on accepted patterns and definition information, allowing for flexible selection and ordering of processes for data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed processes and execution orders are used in software for predictive maintenance and abnormality detection, then the system structure is simple and easy to implement, but the data analysis settings become suboptimal and cannot be adapted to specific facility requirements
Solution Approach 1:
The system segments data processing into multiple independent processes (data acquisition, preprocessing, analysis, etc.) that can be independently selected and configured. Each process is defined separately with its own parameters, allowing flexible combination without increasing overall system complexity.
Solution Approach 2:
The system transitions from fixed execution orders to dynamic, user-configurable process sequences. Users can define custom execution orders based on their specific facility requirements, making the system adaptable while maintaining a manageable structure through standardized process definitions.
2Adaptability or versatility
If standardized software applications are used for predictive maintenance, then implementation is straightforward, but optimum data analysis cannot be set for specific facilities
Solution Approach 1:
The system performs preliminary definition of multiple data processing processes with all necessary parameters and configurations stored in advance. This preparation work enables users to simply select from pre-defined options during operation, maintaining ease of use while achieving facility-specific optimization.
Solution Approach 2:
The system creates universal process definitions that can be applied across different facilities and scenarios. Each process is designed to be multi-functional, serving various analysis needs while maintaining a unified configuration approach that balances adaptability with operational simplicity.
3Manufacturing precision
If multiple data processing processes are allowed with flexible execution orders, then optimal data analysis settings can be achieved, but the complexity of defining and managing processes increases
Solution Approach 1:
The system uses templates and reusable process definitions that can be copied and applied multiple times. Once a process is defined with optimal parameters, it can be replicated across different analysis scenarios, achieving high data analysis precision without repeatedly defining complex processes from scratch.
Solution Approach 2:
The system implements a hierarchical structure where complex data processing workflows are built by nesting simpler, well-defined processes. This nested organization allows precise control over data analysis while managing complexity through modular, layered process definitions that build upon foundational elements.
Data Source
AI summary
A data processing device stores definition information for defining a processing operation of each of a plurality of execution processes, accepts, as a processing pattern, a selection of each of the execution processes that are used for data processing and a selection of an execution order the selected execution process, and generates, based on the accepted processing pattern and the definition information, processing setting information for causing the processing pattern.