ML Template Framework for Sensor Anomaly Detection
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Solution Overview
Problem
Users without intimate knowledge of sensor data manipulation face difficulties in determining anomalies from raw sensor data, leading to potential machine breakdowns and secondary damage due to delayed diagnosis and repair.
Innovation Solution
A system and method that integrate machine learning kernels into a template framework, allowing users to create and execute pipelines for anomaly detection and fault diagnosis through a user-friendly interface, incorporating pre-processing, feature transformation, manifold learning, and post-processing layers with customizable algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If machine learning kernels are integrated into a template framework with user interface, then ease of operation is improved for users without intimate knowledge, but device complexity increases due to the framework structure
Solution Approach 1:
The patent introduces a template framework as an intermediary layer between raw sensor data and the end user. This framework pre-processes and structures data into standardized templates that users can directly apply without needing to understand complex data manipulation techniques. The framework acts as a mediator that translates complex machine learning operations into user-friendly templates, resolving the contradiction by hiding complexity while maintaining ease of use.
Solution Approach 2:
The patent segments the machine learning process into distinct, modular templates (e.g., anomaly detection templates, fault diagnosis templates). Each template encapsulates specific data processing operations, allowing users to select and apply only the templates relevant to their needs. This segmentation reduces the perceived complexity for users while maintaining the full functionality of the underlying system.
2Adaptability or versatility
If multiple machine learning kernels are aggregated into templates, then functionality is improved for solving industry problems, but device complexity increases due to the aggregation structure
Solution Approach 1:
The patent merges multiple machine learning kernels into unified templates that can handle complete data processing workflows. Each template combines various kernels (e.g., filtering, transformation, aggregation) into a single cohesive unit that solves specific industry problems. This merging increases functionality by providing comprehensive solutions while managing complexity through unified template structures.
Solution Approach 2:
The patent creates universal templates that can be applied across different industry domains and problem types. Each template is designed to be multi-functional, handling various data types and analysis requirements through a standardized interface. This universality enhances adaptability and versatility while reducing the need for domain-specific customizations, thereby managing overall system complexity.
3Ease of operation
If templates are integrated into user interface for executing machine learning pipelines, then ease of operation is improved, but loss of information increases due to abstraction from raw data
Solution Approach 1:
The patent performs preliminary actions by pre-processing and structuring raw sensor data into standardized templates before user interaction. The framework automatically executes data validation, transformation, and formatting operations during template creation, ensuring that all necessary information is preserved and organized. This preliminary processing reduces information loss by establishing a consistent data structure that maintains fidelity to the original sensor data while enabling easy user operation.
Data Source
AI summary
According to some embodiments, a system and method are provided to create a template associated with an industrial problem. The method comprises receiving one or more kernels from a machine learning library. The one or more kernels are then aggregated, via a processor, into a template. The template is integrated into a user interface where is may be executed.