Industrial IoT Sensor Switching for Real-Time Edge Diagnostics
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Solution Overview
Problem
Heavy industrial environments face challenges in efficient data collection and utilization due to the complexity of dealing with data from multiple sensors, limiting the effectiveness of 'smart' solutions for optimization and diagnosis.
Innovation Solution
The implementation of a system for continuous ultrasonic monitoring, cloud-based machine pattern recognition, on-device sensor fusion, self-organizing data marketplaces, and distributed ledgers to enhance data collection, processing, and utilization at the edge and in the cloud, enabling intelligent monitoring and optimization of industrial operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is collected by human beings using dedicated data collectors recording batches of sensor data for later analysis, then data collection is simple and equipment complexity is low, but data processing time is long (weeks or months) and productivity is low
Solution Approach 1:
The patent segments the data collection and processing system into distributed edge computing nodes that operate autonomously. Each node processes sensor data locally using embedded processors, eliminating the need for centralized batch processing. This segmentation enables parallel processing across multiple nodes, dramatically reducing data processing time from weeks/months to real-time or near-real-time while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The system performs preliminary data processing, filtering, and analysis at the edge devices before data leaves the local environment. By pre-processing data locally (aggregating, filtering, initial analysis), the system reduces the volume and complexity of data requiring centralized processing, thereby accelerating overall data processing speed while keeping the centralized system simpler.
2Measurement precision
If the range of available data is limited in complex industrial environments, then device complexity is low, but measurement precision and diagnostic capability are insufficient
Solution Approach 1:
The patent merges data from multiple heterogeneous sensors (vibration, temperature, pressure, acoustic, flow sensors) and combines edge computing capabilities with cloud-based analytics. This fusion of diverse data sources and processing levels creates a comprehensive monitoring system that achieves high diagnostic precision by correlating multiple parameters simultaneously, while the modular edge-cloud architecture manages complexity through clear separation of functions.
Solution Approach 2:
The edge computing devices are designed as universal platforms capable of handling multiple sensor types and performing various functions (data acquisition, preprocessing, local analytics, predictive maintenance). This multi-functionality allows the system to process diverse industrial data streams with a single standardized platform, improving measurement precision across different applications while avoiding the complexity of specialized equipment for each function.
3Loss of time
If batches of data are returned to a central office for analysis, then device complexity at the edge is low, but loss of time in data processing and response is significant
Solution Approach 1:
The edge computing devices perform preliminary data processing, filtering, aggregation, and initial analysis locally before transmitting results to the central office. This pre-processing eliminates the need to transfer raw sensor data, reducing communication bandwidth requirements and enabling faster local responses. Critical anomalies can be detected and addressed immediately at the edge, reducing data analysis time from weeks/months to minutes or seconds.
Solution Approach 2:
The edge computing devices serve as intermediaries between sensors and the central office, performing local data processing and filtering. This intermediary layer reduces the volume of data requiring centralized processing, accelerates response time for local decisions, and protects the central system from being overwhelmed by raw data streams, thereby reducing overall data analysis time while managing complexity through hierarchical processing.
4Productivity
If smart solutions are implemented for industrial optimization, then productivity and operational efficiency improve, but device complexity and difficulty of dealing with multiple sensor data increase
Solution Approach 1:
The patent segments intelligent data processing functions across edge devices and cloud platforms, with each segment handling specific tasks (local anomaly detection, predictive maintenance modeling, operational optimization). This segmentation enables sophisticated smart solutions to be implemented without concentrating all complexity in a single system, allowing operational efficiency improvements through distributed intelligence while keeping individual components manageable.
Solution Approach 2:
The edge computing devices are designed to autonomously perform data processing, anomaly detection, and local optimization decisions without requiring constant centralized intervention. This self-service capability enables smart solutions to operate independently at the edge, improving operational efficiency through real-time local decisions while reducing the complexity burden on centralized systems by eliminating the need for complex coordination overhead.
Data Source
AI summary
The system generally includes a crosspoint switch in a local data collection system having multiple inputs and multiple outputs including a first input connected to a first sensor and a second input connected to a second sensor. The multiple outputs include a first output and a second output configured to be switchable between a condition in which the first output is configured to switch between delivery of a first sensor signal and a second sensor signal and a condition in which there is simultaneous delivery of the first sensor signal and the second sensor signal. Each of multiple inputs is configured to be individually assigned to any of the multiple outputs. The local data collection system includes multiple data acquisition units each having an onboard card set configured to store calibration information and maintenance history. The local data collection system is configured to manage data collection bands.