Robotic Task Log Analysis for Proactive Failure Detection
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Solution Overview
Problem
Existing robot-cloud interaction systems lack effective methods to proactively detect potential failures in robotic devices, which can lead to hazardous situations due to component malfunctions or wear and tear, resulting in unplanned downtime and maintenance costs.
Innovation Solution
A method and system that analyze task logs from multiple robotic devices to identify hazardous situations and contextual data, providing alerts for potential failures by comparing the data with historical failure patterns, enabling proactive maintenance and reducing downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional robot-cloud interaction systems are used without proactive failure detection, then system simplicity is maintained, but reliability deteriorates due to undetected component failures and hazardous situations
Solution Approach 1:
The system performs preliminary analysis of task logs to identify hazardous situations and potential failures before they actually occur. By analyzing patterns in historical data and comparing current task logs against known failure modes, the system proactively detects components that may fail, allowing preventive maintenance before actual failure happens. This resolves the contradiction by improving reliability through early detection without requiring complex real-time monitoring of each component.
Solution Approach 2:
The patent introduces an intermediary analysis layer that processes task logs between the robotic devices and the cloud system. This intermediary layer extracts meaningful patterns and hazardous situation indicators from raw task logs, then compares them against databases of known failure patterns. This intermediary processing step improves reliability by enabling intelligent failure detection while keeping the overall system architecture relatively simple by avoiding direct complex monitoring of individual components.
2Reliability
If proactive failure detection systems are implemented, then reliability is improved through early warning, but loss of time increases due to additional data processing and analysis requirements
Solution Approach 1:
The system performs preliminary pattern recognition and hazardous situation identification during normal operation by analyzing task logs as they are generated. Rather than waiting for failures to occur or performing comprehensive real-time analysis, the system proactively identifies potential failures by comparing current task patterns against historical failure data. This allows early warning to be generated with minimal additional processing time, as the analysis leverages existing log data rather than requiring new sensing or measurement infrastructure.
Solution Approach 2:
The patent replaces complex real-time mechanical or electronic monitoring systems with a software-based analysis approach that processes task logs. Instead of installing additional sensors or monitoring hardware to detect component degradation, the system uses computational analysis of existing task log data to infer component health and predict failures. This substitution reduces processing time requirements while maintaining high reliability, as analyzing text logs is computationally less intensive than continuous real-time sensor monitoring.
3Measurement precision
If comprehensive task log analysis is performed to identify hazardous situations, then measurement precision of failure detection is improved, but device complexity increases due to additional processing requirements
Solution Approach 1:
The system extracts only the critical and relevant information from comprehensive task logs that is necessary for failure detection. Rather than analyzing every detail of task log data, the patent identifies and extracts specific hazardous situation indicators and patterns that are most predictive of component failures. This extraction approach maintains high measurement precision for failure detection while reducing processing complexity by focusing computational resources on the most informative aspects of the task logs.
Solution Approach 2:
The analysis system applies different levels of scrutiny and processing to different portions of task log data based on their relevance to failure detection. Critical parameters and hazardous situation indicators receive more detailed analysis, while less relevant information receives minimal processing. This local quality approach optimizes the balance between detection precision and processing complexity by concentrating analytical resources where they provide the most value for predicting component failures.
Data Source
AI summary
Methods and systems for proactively preventing hazardous or other situations in a robot-cloud interaction are provided. An example method includes receiving information associated with task logs for a plurality of robotic devices. The task logs may include information associated with tasks performed by the plurality of robotic devices. The method may also include a computing system determining information associated with hazardous situations based on the information associated with the task logs. For example, the hazardous situations may comprise situations associated with failures of one or more components of the plurality of robotic devices. According to the method, information associated with a contextual situation of a first robotic device may be determined, and when the information associated with the contextual situation is consistent with information associated with the one or more hazardous situations, an alert indicating a potential failure of the first robotic device may be provided.