Cold Chain Diagnostics Access for Predictive Refrigeration Maintenance
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Solution Overview
Problem
Conventional cold chain distribution systems lack efficient maintenance scheduling and data analysis capabilities, leading to potential inefficiencies and increased downtime in transport refrigeration units.
Innovation Solution
A system and method for analyzing transport refrigeration systems, including a diagnostics engine that determines and transmits descriptive, diagnostic, predictive, and prescriptive data based on stored transport parameters and customer licenses, enabling better maintenance planning and reducing unplanned downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional planned maintenance activities are performed at selected intervals, then maintenance is conducted systematically, but maintenance efficiency and reliability are reduced due to lack of real-time data analysis
Solution Approach 1:
The system performs preliminary data collection and analysis by continuously monitoring transport parameters and storing maintenance history before actual maintenance is needed. The diagnostics engine analyzes historical data and transport parameters in advance to predict potential failures, allowing maintenance to be scheduled proactively rather than reactively, thereby reducing unplanned downtime.
Solution Approach 2:
The system implements a feedback mechanism where transport parameters are continuously monitored, analyzed, and used to adjust maintenance schedules. The diagnostics engine processes real-time and historical data to provide feedback on system health, enabling dynamic optimization of maintenance timing based on actual condition rather than fixed intervals, thus improving reliability while minimizing downtime.
2Ease of operation
If multiple levels of data analysis are provided to different users, then information accessibility is improved, but system complexity increases due to license management and data segmentation
Solution Approach 1:
The system segments data and access rights into distinct levels (descriptive, diagnostic, predictive, prescriptive) with corresponding license types. Each user receives only the data levels they are licensed for, which simplifies their interface and reduces information overload. The backend systematically manages these segments through structured license verification, making the complexity transparent to users while maintaining ease of operation.
Solution Approach 2:
The license module acts as an intermediary between users and the diagnostics engine. It automatically verifies user licenses and filters appropriate data levels before transmission, shielding users from system complexity. This intermediary layer manages the complex license-data relationships centrally, allowing users to simply access their licensed data without understanding the underlying system complexity.
Data Source
AI summary
A system for analyzing a transport refrigeration system including: a storage device to store transport parameters associated with a transport refrigeration system and customer licenses; a diagnostics engine in electronic communication with the storage device, the diagnostics engine including: a license module to determine whether a user device has a customer license for at least one of descriptive data, diagnostic data, predictive data, and prescriptive data; a descriptive module to determine descriptive data in response to at least the transport parameters; a diagnostic module to determine diagnostic data of the transport refrigeration unit in response to at least the transport parameters; a predictive module to determine predictive data; and a prescriptive module to determine prescriptive data.