Robot Malfunction Data Storage With Variable Sampling Periods
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Solution Overview
Problem
Conventional methods for displaying robot malfunction occurrence history face limitations in memory capacity, making it difficult to store sufficient data on multiple malfunctions, which hampers accurate analysis.
Innovation Solution
A data storage device is configured to efficiently store data on multiple robot malfunctions by dynamically adjusting sampling modes and periods based on malfunction urgency, prioritizing higher urgency modes and updating data to manage memory capacity effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If sampling data is stored at a preset sampling period and preset sampling time, then the memory capacity is limited and data volume is constant, but it becomes difficult to store sufficient data on multiple robot malfunctions
Solution Approach 1:
The patent applies dynamics by making the sampling period variable rather than fixed. The control device dynamically adjusts the sampling period based on the type and severity of detected malfunctions. When a malfunction is detected, the sampling period is shortened to capture more detailed data; when no malfunction occurs, the sampling period is extended to reduce data volume. This dynamic adjustment allows the system to adaptively optimize memory usage for different malfunction scenarios.
Solution Approach 2:
The patent implements parameter changes by modifying the sampling period parameter according to malfunction conditions. The control device changes the sampling period parameter from a preset fixed value to a variable value based on detected malfunction types. This parameter change enables the system to store more relevant malfunction data while reducing storage of normal operation data, thereby increasing effective data storage capacity for multiple malfunction analysis.
2Measurement precision
If the sampling period is shortened to capture more malfunction data, then more detailed data can be stored, but the memory capacity is consumed faster
Solution Approach 1:
The patent applies local quality by applying different sampling periods to different time segments based on malfunction presence. During normal operation periods, a longer sampling period is used, while during malfunction periods, a shorter sampling period is applied. This localized quality adjustment ensures high measurement precision for malfunction data while maintaining efficient memory usage during normal operations.
Solution Approach 2:
The patent implements partial action by applying short sampling periods only when and where needed (during malfunction events) rather than continuously. This selective application of high-resolution sampling captures necessary malfunction details while avoiding excessive memory consumption during normal operation periods when high precision is not required.
3Quantity of substance
If the sampling period is extended to save memory, then more data can be retained, but the ability to capture detailed malfunction information is reduced
Solution Approach 1:
The system dynamically switches between long and short sampling periods based on malfunction detection. During normal operation, the longer sampling period preserves memory capacity and allows retention of more historical data. Upon malfunction detection, the system transitions to a shorter sampling period to capture detailed malfunction information with higher precision, thus resolving the trade-off between data retention volume and measurement precision.
4Reliability
If sampling is performed continuously at high frequency, then comprehensive data is captured, but the system complexity and processing load increase
Solution Approach 1:
The patent implements periodic action by using different sampling periods based on operational phases. During normal operation, sampling occurs at a longer periodic interval. Upon malfunction detection, the system switches to a shorter periodic interval to capture comprehensive malfunction data. This periodic action with variable periods maintains data completeness during critical events while reducing overall system complexity compared to continuous high-frequency sampling.
Data Source
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AI summary
First acquisition unit (10a) acquires first data on first malfunction of robot (20). Second acquisition unit (10b) acquires second data on second malfunction of robot (20). First determination unit (11a) determines whether or not to store the first data in accordance with the first data. Second determination unit (11b) determines whether or not to store the second data in accordance with the second data. Memory (12) stores the first data and the second data. Controller (13) stores the first data in memory (12) at a first period when first determination unit (11a) determines to store the first data. Controller (13) further stores the second data in memory (12) at a second period longer than the first period, when first determination unit (11a) determines not to store the first data and second determination unit (11b) determines to store the second data.