How to determine if a 2D force sensor has failed?
Release Time:
2025-11-01
A fixed bias fault in a 2D force sensor occurs when the sensor's measured value consistently differs from the true value by a constant, fixed amount. In contrast, a failure fault refers to a sudden loss of functionality in the sensor, resulting in a measurement that remains stuck at a single, constant value. Impact force sensor faults can be categorized into two types: soft faults and hard faults. Soft faults typically include issues such as data drift, bias shifts, and a decline in accuracy levels. Since soft faults are relatively rare, they often go unnoticed—precisely because they’re difficult to detect—and thus tend to be overlooked, potentially leading to significant harm. On the other hand, hard faults in impact force sensors usually arise from structural changes or damage caused by external factors, often manifesting as abrupt failures. When a hard fault occurs, the sensor’s output either drops abruptly to zero or spikes to a much higher-than-normal value.
Fault diagnosis isn’t reliant on data-driven models. As technology advances and control systems grow increasingly complex, it’s becoming increasingly difficult to interpret data models through these systems. When errors occur, diagnosis methods based on data patterns are prone to missed detections. On the other hand, model-free diagnostic approaches don’t demand high precision from the system being analyzed—but they do come with their own drawbacks, such as complex structures that can be hard to uncover. Currently, the most commonly used model-free diagnostic methods include discrete-event-based techniques, data-driven approaches, and knowledge-based methods, each of which has its unique advantages and limitations.
Sensor fault diagnosis, conducted based on data models, primarily includes methods such as state estimation, parameter estimation, and the equivalent space approach. Technological data-model-based fault diagnosis has been evolving for quite some time and is widely adopted due to its ease of analysis and ability to detect faults promptly—diagnoses can be performed at any time. However, this method requires handling large volumes of data, leading to significant computational challenges and complexity. Additionally, it’s prone to certain errors and can be easily affected by external factors, such as noise.
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