Load Cell Status Detection via Anomaly Calculation
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Solution Overview
Problem
Existing sensor detection methods fail to accurately locate faulty load cells in weighing systems, leading to measurement errors and user dissatisfaction due to the inability to specifically identify anomalous characteristic sensing data.
Innovation Solution
A status detection method and apparatus for load cells that collect characteristic sensing data, calculate anomalies using various statistical methods such as median, sigma distribution, standard score, and Fast Fourier transform, and output specific load cell information when anomalies are detected, issuing reminders or alarms based on deviation thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If intelligent diagnostic technologies are used to determine whether sensor data is normal as a whole, then the overall system reliability is improved, but the ability to specifically locate faulty sensors is lost
Solution Approach 1:
The patent divides the sensing data from multiple load cells into individual data sets, allowing separate analysis of each sensor's output. By segmenting the data, the system can identify which specific load cell is faulty while still maintaining overall system reliability monitoring.
Solution Approach 2:
The patent applies different analysis methods to different load cell data based on their individual characteristics. Each load cell's sensing data is evaluated independently using statistical parameters, enabling localized fault detection while preserving the ability to assess overall system health.
2Measurement precision
If characteristic sensing data is collected and displayed for all load cells, then the measurement precision is improved, but the ease of operation deteriorates due to user inability to identify anomalous data
Solution Approach 1:
The patent implements automatic feedback mechanisms where the system analyzes sensing data, compares it against statistical parameters, and provides feedback information about anomalies. This feedback includes identifying which specific load cells have abnormal readings, eliminating the need for users to manually interpret raw data.
Solution Approach 2:
The patent introduces an intermediary diagnostic system that processes the raw sensing data from multiple load cells and translates it into meaningful fault information. This intermediary layer performs statistical analysis and presents results in an easily interpretable format, bridging the gap between precise measurement and user comprehension.
3Measurement precision
If statistical analysis methods are applied to detect anomalies in load cell data, then the measurement precision is improved, but the device complexity increases
Solution Approach 1:
The patent transforms raw sensing data into statistical parameters (mean, standard deviation, variance) to detect anomalies. By changing the representation of data from raw values to statistical measures, the system achieves precise fault detection while using computationally efficient methods that don't require complex algorithms.
Data Source
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AI summary
The present invention provides a method and apparatus for detecting the status of a load cell. The method for detecting the status of a load cell that is applied in a multi-point weighing system, the method comprising the steps of: collecting characteristic sensing data of the load cells; calculating an anomaly in the collected characteristic sensing data; and acquiring and outputting a corresponding load cell information when the anomaly exists. The method and apparatus for detecting the status of a load cell can help customers accurately locate a faulty sensor or a potential faulty sensor, so as to avoid a measurement error due to sensor problems and improve user satisfaction.