Industrial Analytics Model Selection for Multi-Node Inspection
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Solution Overview
Problem
Automated industrial inspection processes require significant technical expertise, advanced machine learning techniques, and access to large-scale data acquisition, making them complex and inaccessible to non-experts, and existing solutions struggle to optimize models across multiple nodes and machines with different computing languages.
Innovation Solution
A system and method that includes a machine learning module with non-transitory computing code executed by a processor, which senses performance indicators across multiple nodes, selects and optimizes models based on user input, and applies them to data sets, enabling efficient industrial inspection and analytics without requiring extensive technical expertise, using a user-friendly interface for model selection and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated inspection processes use advanced machine learning techniques and large-scale data acquisition, then measurement precision and reliability are improved, but device complexity and difficulty of operation increase significantly
Solution Approach 1:
The patent introduces a cloud-based platform as an intermediary that hosts pre-configured machine learning models and data processing pipelines. This platform mediates between the complex analytics engine and users, abstracting away the computational complexity while delivering high-precision inspection results through standardized APIs and interfaces.
Solution Approach 2:
The patent replaces manual programming and configuration of machine learning models with automated model selection and deployment systems. The system automatically selects appropriate models from a library, configures them based on inspection requirements, and deploys them without requiring expert programmers, thus substituting mechanical expert intervention with automated digital systems.
2Reliability
If expert programmers perform ad hoc, unstructured inspection programming, then manufacturing precision and reliability are improved, but loss of time and productivity decrease
Solution Approach 1:
The patent implements pre-configured machine learning models and inspection pipelines that are prepared in advance on the cloud platform. These models are pre-trained on diverse industrial data and can be rapidly deployed to new inspection tasks without requiring time-consuming custom programming, thus maintaining reliability while reducing development time.
Solution Approach 2:
The patent enables dynamic adjustment of inspection parameters and model configurations through the user interface without requiring reprogramming. Users can modify inspection criteria, data sources, and model parameters on-demand, allowing rapid adaptation to different inspection scenarios while maintaining consistent quality through the underlying robust analytics engine.
3Productivity
If multiple machine learning models are trained and validated in parallel on multiple computing nodes, then productivity is improved, but device complexity and loss of time increase
Solution Approach 1:
The patent implements a universal cloud-based analytics platform that provides model training, validation, and deployment capabilities through standardized interfaces. This multi-functional platform handles diverse machine learning workloads across multiple computing nodes using unified resource management, eliminating the need for separate specialized systems and reducing overall infrastructure complexity.
Solution Approach 2:
The patent combines model training, validation, and deployment functions into a single integrated cloud platform. Multiple computing nodes are merged into a coordinated distributed system managed by the platform, enabling parallel processing of multiple models while presenting a unified interface to users, thus improving productivity without proportionally increasing operational complexity.
4Productivity
If iterative improvements to analytics modeling are enabled without full-scale reprogramming, then productivity is improved, but measurement precision may be compromised
Solution Approach 1:
The patent implements automated feedback loops that continuously monitor model performance against validation criteria and inspection quality metrics. The system automatically triggers retraining and model updates when performance degradation is detected, enabling rapid iterative improvements while maintaining precision through data-driven feedback rather than arbitrary changes.
Solution Approach 2:
The patent creates a dynamic model management system where analytics models can be automatically updated, retrained, and deployed in response to changing inspection requirements and performance feedback. The system dynamically adjusts model configurations and retrains models using new data without requiring full-scale reprogramming, maintaining precision through automated quality assurance processes.
Data Source
AI summary
An apparatus, system and method of providing industrial analytics. The apparatus, system and method include at least a plurality of sensors sensing performance indicators for an industrial process across multiple nodes, wherein ones of the multiple nodes are remote from each other; at least one machine learning module comprising non-transitory computing code executed by a processor. When executed by the processor, the code causes the steps of: receiving user input regarding at least the industrial process and a data set; selecting a model based on at least the user input, wherein the selected model comprises a plurality of learnings based on the performance indicators sensed by multiple sensors across at least multiple ones of the multiple nodes; applying the selected model to the data set; assessing at least the performance indicators for the data set upon application of the selected model; and outputting the assessed performance indicator.