Hazard Function-Based Failure Prediction for Data Collection Devices
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Solution Overview
Problem
Data collection devices, such as IoT sensors, are prone to failure over time, leading to potential data inaccuracies and disruptions in monitoring critical parameters like building maintenance, fire detection, and emergency situations, as existing systems lack effective proactive failure prediction and seamless failover mechanisms.
Innovation Solution
A computer-implemented method that detects failure events in primary data collection devices, discontinues their use, selects and switches to alternate devices, and updates a hazard function based on failure data to predict future failures, ensuring continuous and accurate data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data collection devices are deployed in harsh environments, then data collection coverage is improved, but device reliability deteriorates due to susceptibility to damage over time
Solution Approach 1:
The system performs preliminary actions by continuously monitoring failure-related data and proactively predicting device failures before they occur using hazard functions. This allows the system to switch to alternate devices in advance, preventing data loss and maintaining continuous operation despite harsh environmental conditions
Solution Approach 2:
The system changes parameters by dynamically updating hazard function parameters based on monitored failure-related data. This allows the prediction model to adapt to changing environmental conditions and device aging patterns, improving reliability predictions while maintaining broad device deployment
2Measurement precision
If proactive failure prediction is implemented, then data accuracy is improved by identifying failing devices, but system complexity increases due to hazard function generation and monitoring requirements
Solution Approach 1:
The system implements self-service by automatically generating hazard functions from collected failure-related data without requiring manual intervention. The hazard functions autonomously update and refine predictions based on incoming data, reducing operational complexity while maintaining high prediction accuracy
Solution Approach 2:
The system uses feedback mechanisms where failure-related data from monitored devices continuously updates the hazard function parameters. This closed-loop feedback improves prediction accuracy over time while the automated nature of the process prevents complexity from escalating
3Reliability
If alternate data collection devices are maintained as backup, then system reliability is improved through failover capability, but loss of substance increases due to having redundant devices
Solution Approach 1:
The system performs preliminary switching to alternate devices only when failure prediction indicates a primary device will fail. This on-demand failover approach maintains system reliability while avoiding the continuous resource consumption associated with maintaining permanent redundant devices in harsh environments
Data Source
AI summary
A computer-implemented method includes: detecting, by a computing device, a failure event for a primary data collection device that is currently collecting and providing data; discontinuing, by the computing device, use of the primary data collection device based on the detecting the failure event; selecting, by the computing device, an alternate data collection device based on the discontinuing the use of the primary data collection device; receiving, by the computing device, data collected by the alternate data collection device; receiving, by the computing device, failure related data associated with the primary data collection device after discontinuing use of the primary data collection device; and updating, by the computing device, a hazard function based on the failure related data, wherein the hazard function is used to detect future failures of a same type of primary data collection device.