Customizable Sensor Data Collection for Machinery Diagnostics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Complex commercial and industrial machinery generates overwhelming and context-less data, requiring multiple skill sets for diagnosis and repair, leading to prolonged downtime and lost income due to the difficulty in diagnosing component failures.
Innovation Solution
A system comprising a collection computer and a server computer that automatically detects sensors, allows users to customize data collection, storage, and transmission, and provides real-time monitoring and reporting, enabling users to select sensors, sampling rates, and storage locations, and generate customized reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time monitoring of all sensors is implemented, then diagnostic capability is improved, but data complexity and information overload increase
Solution Approach 1:
The system extracts and displays only the most relevant sensor data and diagnostic information needed for effective monitoring and diagnosis, filtering out unnecessary details that contribute to information overload while maintaining comprehensive diagnostic capability
Solution Approach 2:
Different users can customize their views to show locally relevant information based on their specific roles and needs, allowing each user to see only the data and diagnostics pertinent to their responsibilities rather than all available sensor data
2Measurement precision
If comprehensive sensor data is collected, then diagnostic accuracy is improved, but time to diagnose and repair increases
Solution Approach 1:
The system pre-processes and organizes sensor data in advance, creating structured information that is ready for immediate analysis during diagnosis, eliminating the need to manually sort through raw data when a problem occurs
Solution Approach 2:
The system provides feedback mechanisms that guide diagnosticians through the data analysis process, highlighting relevant information and suggesting potential causes based on patterns in the sensor data, thereby reducing diagnosis time while maintaining accuracy
3Reliability
If multiple skill sets are required for diagnosis, then diagnostic thoroughness is improved, but machine downtime increases
Solution Approach 1:
The system creates a universal interface that consolidates data from multiple sensor types and systems into a single view that can be effectively used by diagnosticians with diverse skill sets, allowing each expert to contribute their knowledge without requiring coordination of multiple specialized tools or systems
Solution Approach 2:
The system acts as an intermediary that translates and presents sensor data in a standardized format that bridges different skill sets and expertise areas, enabling diagnosticians from different disciplines to collaborate more efficiently on the same problem
4Loss of information
If all sensor data is stored and transmitted, then data availability for analysis is improved, but storage and transmission costs increase
Solution Approach 1:
The system extracts and stores only the most critical sensor data and diagnostic information, filtering out redundant or less important data points, thereby maintaining data availability for essential analysis while reducing storage and transmission requirements
Solution Approach 2:
The system implements selective data collection that captures sufficient information for effective diagnosis without attempting to store every possible sensor reading, using judgment about what level of data completeness is actually necessary for the intended purposes
Data Source
AI summary
Embodiments are directed towards enabling users to customize data collection and analysis. A collection computer may automatically detect and dynamically update each of a plurality of sensors that may be currently providing real-time data regarding at least one characteristic of a machine. At least one sensor may be selected for local storage of its corresponding real-time data at the collection computer. And at least one sensor may be selected for remote storage of its corresponding real-time data by a server computer. A template may be employed to remotely display at least one characteristic of the machine based on current real-time data provided by at least one of the sensors identified by the template. In response to the user selecting at least one sensor for remote display, the template may be modified to include the remote display of the at least one sensor's corresponding current real-time data.