Dynamic Sensor Data Grouping for Industrial Machine Train Analysis
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Solution Overview
Problem
Industrial condition monitoring systems face challenges in effectively grouping and presenting data from multiple sensors on graphical user interfaces, particularly due to the decreasing availability of technically inclined personnel, requiring improved systems and methods for operating these systems.
Innovation Solution
A system that includes sensors on machine trains, a communication circuit, and a graphical user interface (GUI) controlled by a processor to dynamically group measurements based on predefined modes, allowing users to select machine trains, sensors, and grouping metrics, thereby automatically organizing data into columns for easier analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data from multiple sensors is displayed individually on the GUI, then complete data information is provided, but the complexity of data interpretation increases and requires highly technical personnel
Solution Approach 1:
The patent combines multiple sensor data streams into unified groupings displayed on the GUI. Sensors are organized into groups based on spatial relationships, functional relationships, or user-defined criteria, allowing complete data information to be presented in an integrated manner rather than as separate individual displays, thereby reducing interpretation complexity while maintaining information completeness
Solution Approach 2:
The patent implements a universal grouping mechanism that can organize sensor data through multiple criteria (spatial proximity, functional relationship, user selection). This multi-functional approach allows the same data set to be viewed through different organizational lenses, making the system adaptable to various user expertise levels and analysis needs without losing data completeness
2Ease of operation
If sensors are grouped by spatial proximity, then data organization is simplified, but sensors that are functionally related but spatially distant may be separated
Solution Approach 1:
The patent applies different grouping criteria to different sets of sensors based on their local characteristics. Spatial grouping is applied to sensors physically close to each other, while functional grouping is applied to sensors monitoring related parameters regardless of location. This localized application of appropriate grouping methods ensures both organizational simplicity and functional relationship preservation
Solution Approach 2:
The patent implements dynamic grouping that allows switching between different organizational criteria. Users can dynamically change grouping methods from spatial to functional or create custom groupings based on current analysis needs. This dynamic reorganization capability ensures that functional relationships are preserved when spatial grouping is not appropriate, while maintaining organizational simplicity through automated grouping
Data Source
AI summary
Systems and methods are provided for dynamically grouping data analysis content derived from a plurality of sensors. In one embodiment, a plurality of sensors can be disposed on a plurality of machine trains or one or more components within the plurality of machine trains, each machine train including one or more machines configured to operate in an industrial environment. A communication circuit can be operatively coupled to the plurality of sensors and configured to communicate data measured by the plurality of sensors, and a graphical user interface (GUI) can be configured to generate one or more visualizations of the measured data. A processor can be configured to receive the measured data via the communication circuit, to generate a plurality of measurements based on the measured data, and to operatively control the GUI.