Embedded Field Device Diagnostics for Low-Bandwidth Automation
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Solution Overview
Problem
Current automation systems face challenges in interpreting and utilizing raw data from embedded equipment due to complex structures and limited communication bandwidth, leading to a high manual engineering effort for diagnostic systems.
Innovation Solution
A smart embedded control system with a diagnostic application interface, signal evaluation component, complex event processing component, and diagnostic reasoning component that analyzes signal data, identifies events, and derives causal dependencies to predict impacts and root causes, enabling improved diagnostic functionality on-field.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is transferred to external systems for analysis, then diagnostic capability is improved, but communication bandwidth requirements increase and manual engineering effort increases
Solution Approach 1:
The patent segments the diagnostic processing function by dividing it between the embedded control system (which performs signal evaluation and event identification) and external systems (which perform complex event processing and diagnostic reasoning). This segmentation allows the system to transfer only processed event data rather than raw sensor data, significantly reducing communication bandwidth requirements while maintaining diagnostic capability.
Solution Approach 2:
The embedded control system performs preliminary signal evaluation and event identification before transferring data to external systems. By preprocessing the raw sensor data into meaningful events and patterns locally, the system reduces the volume of data that needs to be transmitted and processed externally, thereby reducing communication bandwidth requirements while preserving diagnostic accuracy.
2Measurement precision
If complete sensor data is transferred for analysis, then diagnostic accuracy is improved, but communication bandwidth requirements increase
Solution Approach 1:
The patent extracts only the essential diagnostic information from raw sensor data by performing signal evaluation and event identification in the embedded system. Instead of transferring complete sensor data, only processed events and patterns are extracted and transferred to external systems, maintaining diagnostic accuracy while minimizing communication bandwidth usage.
3Ease of operation
If manual engineering is used to interpret raw data, then diagnostic systems can be implemented, but manual engineering effort increases
Solution Approach 1:
The embedded control system performs self-service by automatically evaluating signals, identifying events, and applying signal analytics information locally. This automation of preliminary diagnostic processing reduces the manual engineering effort required for data interpretation while maintaining diagnostic system implementation capability.
4Reliability
If signal analytics information is processed externally, then diagnostic reasoning is improved, but response time increases
Solution Approach 1:
The embedded control system performs preliminary signal evaluation and event identification before transferring data to external systems. This preliminary processing reduces the complexity of subsequent diagnostic reasoning tasks, enabling faster response times while maintaining diagnostic accuracy through distributed processing.
Data Source
AI summary
An embedded control system for a field device of an automation system includes: a diagnostic application interface to a backend server for signal analytics information, complex event pattern information, and diagnostic information; a physical process interface to a signal source for transferring signal data; a signal evaluation component for comparing received signal analytics information with received signal data to identify a first and a second event; an event processing component for applying received event pattern information to the first and second identified events to identify a first classified event; and a diagnostic reasoning component for deriving causal dependencies between the first classified event and a further classified event with regard to diagnostic information to identify a root cause for the first classified event or predict an impact of the first classified event.