A computer-implemented method of monitoring the operation of a cargo shipping reefer container
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Solution Overview
Problem
Conventional methods for monitoring refrigeration systems in cargo shipping reefer containers are unreliable, especially under varying ambient temperatures and humidity levels, leading to inefficiencies and increased energy consumption during pre-trip inspections, and inability to accurately detect faults in real-time.
Innovation Solution
A computer-implemented method using sensors and a simulation model that collects and processes data to estimate the operation of the refrigeration system, issuing alarm signals when predefined criteria are exceeded, allowing for reduced pre-trip inspections and reliable fault detection by adapting to current operating conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional pre-trip inspections with stress-tests are performed to ensure refrigeration system reliability, then fault detection capability is improved, but energy consumption and inspection time increase significantly
Solution Approach 1:
The system performs continuous monitoring and data collection during normal operation before the actual pre-trip inspection is needed. By gathering operational data, environmental conditions, and system performance metrics in advance, the simulation model can predict system behavior without requiring energy-intensive stress-tests during the inspection phase.
Solution Approach 2:
Instead of physically performing stress-tests on the actual refrigeration system, the invention creates a virtual copy through a simulation model that replicates system behavior. This digital twin allows fault detection by comparing simulated stress-test results against actual operational data, eliminating the need for physical energy-consuming tests while maintaining detection capability.
2Reliability
If continuous remote monitoring is implemented to detect faults in real-time, then reliability of cargo quality assurance is improved, but system complexity and cost increase
Solution Approach 1:
The monitoring system is designed to serve multiple functions: it collects operational data for current analysis, stores historical data for future reference, feeds information to the simulation model for predictive analysis, and provides alerts for immediate issues. This multi-functionality reduces the need for separate dedicated systems for each purpose, thereby managing complexity while enhancing reliability.
Solution Approach 2:
The simulation model acts as an intermediary between raw sensor data and fault detection decisions. Instead of directly analyzing complex sensor streams for faults, the system uses the simulation model to bridge the gap by comparing actual measurements against predicted behavior, simplifying the detection process while improving reliability through multiple layers of analysis.
3Measurement precision
If pre-trip inspections are performed frequently to ensure system performance, then detection of degradation is improved, but productivity and time efficiency deteriorate
Solution Approach 1:
Instead of performing complete stress-tests during every pre-trip inspection, the system applies partial monitoring focused on key parameters that indicate degradation. The continuous monitoring framework allows selective deep-dive analysis only when anomalies are detected, rather than routinely applying full inspection protocols to every container, thereby improving efficiency while maintaining detection accuracy.
Solution Approach 2:
The system replaces periodic discrete inspections with continuous monitoring that operates throughout the container's operational lifecycle. This continuous data collection provides constant oversight of system performance, enabling degradation detection to occur naturally over time rather than requiring repeated interruptive inspection events, thus improving both precision and productivity.
Data Source
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AI summary
A computer-implemented method of remotely monitoring the operation of a cargo shipping reefer container configured with a refrigeration system that has installed therewith a control computer coupled with sensors monitoring the operation of the refrigeration system. The method comprises: collecting, from the control computer (302), a first set of observation data (311) and a second set of observation data (312) comprising a respective first and second sequence of measurement values measured by sensors coupled to the control computer; running a simulation model (313) that receives the first set of observation data as its input and outputs simulated values; wherein the simulation model is configured to output the simulated values as estimates of the second set of the observations (314); computing an indicator value as a function of residual values (315) computed from the difference between the values of the second set of observations and the simulated values; and evaluating the indicator value (318) against a predefined criterion and issuing an alarm signal (319) in case the predefined criterion is exceeded.