Process Valve Signal Preprocessing for Scalable Anomaly Detection
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Solution Overview
Problem
Existing methods for monitoring and detecting anomalies in process valve operations are inefficient due to high data transmission and computing requirements, making it difficult to monitor a large number of valve units effectively.
Innovation Solution
A method and device that pre-configure sensor signal parameters using previously configured functions, reducing data generation and transmission on the valve unit side, and utilize a machine-learned model, such as an artificial neural network, for anomaly detection, allowing for efficient and precise anomaly identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor signals are transmitted directly from process valve units, then complete data for anomaly detection is available, but transmission capacity and computing resources increase significantly
Solution Approach 1:
The system performs preliminary processing of sensor signals at the process valve unit by determining sensor signal parameters (such as RMS, peak values, zero-crossing rates) before transmission. This preliminary action reduces the data volume that needs to be transmitted while preserving the essential information needed for anomaly detection, thereby resolving the contradiction between detection precision and transmission volume.
Solution Approach 2:
The system extracts only the most relevant features from the raw sensor signals by determining specific sensor signal parameters that characterize the valve operation. Instead of transmitting complete sensor signals, only the extracted parameters (such as effective values, peak values, and spectral features) are transmitted to the central evaluation system, reducing transmission requirements while maintaining detection capability.
2Productivity
If pre-configured functions are used to determine sensor signal parameters, then transmission and computing requirements are reduced, but adaptability to different valve types decreases
Solution Approach 1:
The system employs dynamic configuration of determination parameters that can be adjusted based on the specific valve type and application. The pre-configured functions include configurable parameters such as frequency ranges, time windows, and threshold values that can be adapted to different valve characteristics, allowing the system to maintain both efficiency through preconfiguration and adaptability through parameter adjustment.
Solution Approach 2:
The system changes parameters of the pre-configured functions to adapt to different valve types and operating conditions. By modifying determination parameters (such as sampling rates, analysis windows, and feature selection), the system can optimize the pre-processing functions for specific applications while maintaining the computational efficiency provided by the pre-configured framework.
3Reliability
If a centralized evaluation model is used for anomaly detection, then consistent anomaly indicators are generated, but the system complexity and data transmission requirements increase
Solution Approach 1:
The system segments the anomaly detection process into two parts: local preprocessing at the valve unit (determining sensor signal parameters) and central evaluation (applying the evaluation model). This segmentation allows the complex model to remain centralized for consistency while keeping the distributed components simple, thereby maintaining reliability without proportionally increasing overall system complexity.
Data Source
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Figure 2a~2b
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AI summary
A method is provided which comprises: determining, by means of a recording function (102a-b), at least one sensor signal recording (RECa-b) associated with a process valve unit (100) as a function of a sensor signal (Sa-b) generated by at least one sensor (104a-b) permanently or temporarily assigned to the process valve unit (100); determining, by means of a plurality of preconfigured functions (106a-z), a plurality of sensor signal parameters (Paz) that characterize the at least one sensor signal (Sa-b) as a function of the at least one sensor signal recording (RECa-b); and transmitting, by means of an output interface (108), the plurality of sensor signal parameters (Pa-z).