IIoT Sensor Crosspoint Switching for Real-Time Industrial Diagnostics
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Solution Overview
Problem
Heavy industrial environments face challenges in 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, with existing methods being time-consuming and inefficient.
Innovation Solution
The implementation of a system for data collection and processing that includes continuous ultrasonic monitoring, machine pattern recognition, self-organizing data marketplaces, on-device sensor fusion, and AI training based on industry-specific feedback, utilizing a platform with a crosspoint switch for simultaneous data delivery from multiple sensors and distributed CPLD chips for efficient data acquisition and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is collected by human beings using dedicated data collectors and recorded on media for later analysis, then data collection is simple and equipment is basic, but the analysis time scale is weeks or months and productivity is low
Solution Approach 1:
The patent replaces manual data collection methods with automated electronic sensor systems and digital processing. Sensors continuously collect data and transmit it through electronic networks to processing systems, eliminating the mechanical process of manual recording and enabling real-time or near-real-time analysis, thus dramatically improving productivity and reducing time loss.
Solution Approach 2:
The patent implements continuous data collection and processing operations. Sensors continuously monitor parameters and data streams are continuously processed and analyzed, rather than using batch processing methods. This continuous operation eliminates idle time between data collection cycles and maintains constant productivity improvement.
2Adaptability or versatility
If the range of available data is limited in industrial environments, then device complexity is reduced, but the effectiveness of smart solutions for optimization and diagnosis is limited
Solution Approach 1:
The patent creates a universal data processing platform that handles multiple data types from various sensors uniformly. The system processes data from vibration sensors, temperature sensors, pressure sensors, and other industrial sensors through a common architecture, enabling the same smart solutions to be applied across different data sources and improving adaptability without proportionally increasing complexity.
Solution Approach 2:
The patent introduces intermediate processing layers including edge computing devices and data preprocessing modules that aggregate and prepare data before it reaches the central analysis system. These intermediaries simplify the complexity by performing initial filtering, aggregation, and normalization, making the system more adaptable to diverse sensor inputs while managing complexity at distributed levels.
3Productivity
If batches of specific sensor data are recorded on media for later analysis, then storage requirements are manageable, but the data collection method is time-consuming and inefficient
Solution Approach 1:
The patent extracts only the most relevant and valuable data features using intelligent preprocessing and filtering algorithms. Instead of storing and processing all raw sensor data, the system identifies and extracts key parameters and anomalies, significantly reducing the volume of data that requires extensive processing while maintaining high productivity through focused analysis of critical information.
Solution Approach 2:
The patent performs preliminary data processing, filtering, and aggregation at the edge devices and sensor nodes before data is transmitted to central systems. This preliminary action reduces the raw data volume early in the process, making subsequent processing more efficient and productive while managing storage requirements through selective data retention of only the most valuable processed information.
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, and a neural net expert system using intelligent management of data collection bands.