Compressed Air Monitoring for Threshold-Based Leak Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for detecting compressed air leaks in industrial processes are inefficient and costly, as they require ideal conditions for calibration and manual analysis by experts, which is impractical in real-world applications.
Innovation Solution
A computer-implemented method and device that automatically monitor compressed air consumption by detecting air flow, determining operating modes based on signal differences, and using a simple algorithm to differentiate between idle and operating states, applicable to machines with varying interfaces and ages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning methods are used for leakage detection, then detection accuracy is improved, but the requirement for ideal calibration conditions and expert knowledge increases, making it impractical for real-world applications
Solution Approach 1:
The system performs self-calibration by automatically establishing a reference profile from measured compressed air consumption data without requiring expert intervention or ideal calibration conditions. The method autonomously identifies operating modes and detects deviations, enabling practical deployment in real-world industrial environments.
Solution Approach 2:
The system collects and analyzes compressed air consumption data during normal operation to pre-establish a reference profile that captures typical consumption patterns. This preliminary action creates a baseline for future leak detection without requiring separate calibration phases or expert analysis.
2Measurement precision
If manual analysis by experts is performed to locate potential leak sources, then detection accuracy is improved, but time consumption and costs increase significantly
Solution Approach 1:
The system replaces manual expert analysis with an automated computer-implemented method that processes compressed air consumption data, identifies operating modes, and detects leaks algorithmically. This substitution eliminates the need for expert time while maintaining detection capability through automated pattern recognition.
Solution Approach 2:
The system creates a digital reference profile that copies and stores the characteristic compressed air consumption patterns of normal operation. This digital model serves as a surrogate for expert knowledge, enabling automated comparison and leak detection without requiring continuous expert involvement.
3Reliability
If compressed air flow data is continuously monitored to detect leaks during machine operation, then leak detection capability is improved, but the complexity of distinguishing leaks from normal consumption increases
Solution Approach 1:
The system segments the compressed air consumption data into distinct operating modes (e.g., idle, running, malfunctioning) based on characteristic consumption patterns. By dividing the continuous data stream into mode-specific segments, the system simplifies leak detection by comparing current consumption against mode-appropriate reference profiles rather than attempting to analyze all data uniformly.
Solution Approach 2:
The system changes the analysis parameter from raw compressed air flow values to operating mode classifications. By transforming the data into mode categories with distinct consumption characteristics, the system reduces complexity and enables more reliable leak detection through mode-specific threshold comparisons.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables efficient, automatic detection of leaks and optimization of compressed air consumption, reducing manual analysis time and costs, while being deterministic and requiring minimal computing resources, suitable for diverse industrial setups.
Implementation Method 1
Detecting at least one air flow at at least one compressed air line of the machine with a sensor, generating a signal representing the detected air flow through the sensor
Data Source
Figure 1
Figure 2
Figure 3~4
AI summary
The invention relates to a computer-implemented method for automatically monitoring compressed air consumption and determining the operating mode of a machine, comprising the steps of: detecting an air flow at a compressed air line of the machine with a sensor, generating a signal representing the detected air flow by the sensor, continuously reading and storing the signals in a computer unit, after a certain time interval, processing the stored signals into individual consumption values, each corresponding to the instantaneous consumption during the past time interval, determining a minimum and a maximum value from the consumption values, determining whether the difference between the maximum and minimum values is above or below a defined threshold, wherein the threshold was pre-learned in a learning process, and determining a first and a second operating mode of the machine via the difference.The first operating mode is determined when the difference is below the threshold value, and the second operating mode is determined when the difference is above the threshold value. The invention further relates to a corresponding device for carrying out this method.