Method for testing the functional stability of a refrigerating machine
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Solution Overview
Problem
Refrigerating machine controllers with reduced processing capacity face challenges in detecting system regression following software or hardware modifications, leading to adverse operational effects.
Innovation Solution
A method for testing functional stability involves connecting the controller to a diagnostic unit for data exchange, implementing a recording step to capture reference data, and an execution step to compare output variables, allowing timely detection of system divergence and adaptation of the control algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If software or hardware modifications are performed to improve functionality, then new features or performance improvements are achieved, but system regression may occur leading to divergence from programmed behavior
Solution Approach 1:
The patent applies preliminary action by performing regression tests before deploying modified software or hardware to production. The method records reference data from the original system behavior, then compares it against behavior after modifications to detect regressions before they affect actual operation. This advance testing prevents unreliable modifications from reaching the operational system.
Solution Approach 2:
The patent implements feedback by continuously comparing current system behavior against recorded reference data. The diagnostic unit monitors output variables and status variables, and when deviations exceed predefined thresholds, the system provides feedback indicating a regression condition. This feedback loop enables automatic detection and reporting of system divergence caused by modifications.
2Reliability
If comprehensive regression testing is implemented to detect system divergence, then reliability is improved, but processing time and computational resources increase
Solution Approach 1:
The patent extracts only the essential data needed for regression detection: specific output variables and status variables that indicate system behavior. Rather than analyzing all possible system parameters, the method selectively records and compares only those variables that are critical for detecting regression conditions. This extraction approach reduces testing overhead while maintaining detection accuracy.
Solution Approach 2:
The patent applies parameter changes by adjusting the granularity and scope of testing parameters based on system priorities. The method allows configuration of which variables to monitor, threshold values for regression detection, and timing parameters for tests. This flexibility enables optimization of testing parameters to achieve adequate reliability with minimal time loss for the specific application.
3Reliability
If frequent regression tests are performed to ensure early detection, then system stability is maintained, but processing capacity of the controller is consumed
Solution Approach 1:
The patent applies partial action by performing regression tests at selective intervals rather than continuously. The method can be configured to run tests at specific milestones (e.g., after scheduled maintenance, following software updates, or at predefined time intervals). This partial testing approach provides adequate regression detection without consuming excessive controller processing capacity during normal operation.
4Measurement precision
If detailed data recording and comparison is performed to detect regression, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies universality by designing a diagnostic unit that can serve multiple functions: recording reference data, comparing current behavior against references, detecting regression conditions, and reporting results. The same hardware and software components perform all these functions, avoiding the need for separate specialized systems for each function. This multi-functionality reduces overall system complexity while maintaining measurement precision.
Data Source
AI summary
Method for testing the functional stability of a controller of a refrigerating machine where the controller performs control cycles. The testing method comprises a step (100) of recording a set of current values of input variables, status variables and output variables of the controller, and a subsequent execution step (200), in which the values of the status variables and input variables recorded in the recording step are forced in the controller as inputs and values of the output variables are recorded in order to compare them with the values of the output variables recorded in the recording step (100).