Onsite AI Inspection Commissioning With Instant Feedback Loops
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Solution Overview
Problem
Current AI system commissioning processes are time-consuming and costly due to long iteration loops, lack of instant feedback, and potential data discrepancies, making it challenging to achieve optimal performance for specific use cases, especially in industrial environments where data sharing and security constraints are restrictive.
Innovation Solution
A method for commissioning an AI-based inspection system using a 'single box' approach where a commissioning computer handles data collection, training, and testing onsite, providing instant feedback and iteratively tuning the AI algorithm to achieve optimal performance within a controlled environment, reducing the need for extensive data sets and external data sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI algorithms are trained on large amounts of data with multiple use cases to achieve generalization, then the model can be used in many general scenarios, but the training and tuning process becomes time-consuming and requires multiple resources
Solution Approach 1:
The patent segments the training process into two distinct phases: (1) initial training on diverse multi-use case data to build generalization capability, and (2) subsequent fine-tuning on specific use case data to optimize for particular applications. This segmentation allows the system to achieve both general adaptability and specific performance without requiring excessively long training times for each phase.
Solution Approach 2:
The patent applies preliminary action by pre-training the AI algorithm on large amounts of diverse data before deployment. This preliminary training establishes a strong foundation of generalization capability, which then requires only minimal fine-tuning when deployed to specific use cases, significantly reducing the time needed for on-site commissioning and adaptation.
2Reliability
If iterative training and tuning is performed to achieve optimal performance for specific use cases, then the AI algorithm performance is optimized, but the process involves multiple resources and is time-consuming
Solution Approach 1:
The patent implements self-service by enabling the AI algorithm to automatically fine-tune itself on-site using local data and feedback from the specific use case environment. The system performs self-diagnosis and self-optimization without requiring external data scientists or complex training infrastructure, reducing resource requirements while maintaining high performance.
Solution Approach 2:
The patent incorporates continuous feedback loops where the AI algorithm receives performance feedback from real-world operation and automatically adjusts its parameters. This feedback mechanism enables iterative improvement without human intervention, achieving optimal performance while minimizing the need for external resources and expertise.
3Quantity of substance
If data is collected and processed externally for AI training, then comprehensive training data can be obtained, but data sharing constraints and security risks increase in industrial environments
Solution Approach 1:
The patent introduces an on-site edge computing device as an intermediary between the external data sources and the industrial environment. This intermediary collects and processes data locally within the secure industrial environment, extracting only necessary model parameters or aggregated insights for external sharing, thereby maintaining data security while still enabling comprehensive training.
Solution Approach 2:
The patent applies local quality by allowing different data processing and sharing strategies for different locations and data types. Sensitive data remains strictly local within the industrial environment, while non-sensitive aggregated data can be shared externally. This localized approach to data quality and security enables comprehensive training without compromising security constraints.
Data Source
AI summary
Described embodiments provide a technique for commissioning an AI-based inspection system. In particular, the described embodiments provide a tool for field engineers to perform tasks including data collection, and training, testing and deployment of an AI algorithm, onsite, with instant feedback. The described tasks are carried out in the same physical environment to arrive at the same sensor setting that is then used by the field device on which the AI algorithm is deployed. Deployment cycle is significantly reduced. The technique leverages the recognition that inspection tasks in industrial settings are repetitive in nature and are carried out in a controlled environment, whereby the AI algorithm need not be generalized beyond the specific use case.