Interview Questions for Gyroscope Software Troubleshooting

Gyroscope errors can stem from various sources, broadly categorized as systematic and random errors. Systematic errors are predictable and repeatable, often stemming from inherent sensor limitations or biases. Random errors, on the other hand, are unpredictable fluctuations caused by noise. Let’s explore specific types:

• Bias: A constant offset in the measured angular rate. Think of it like a slightly inaccurate clock – it’s always off by a consistent amount. This can be caused by manufacturing imperfections, temperature variations, or even the Earth’s magnetic field affecting the sensor.
• Drift: A gradual change in the measured angular rate over time. Imagine a slowly drifting boat – its course changes subtly but persistently. This is often due to temperature fluctuations, aging components, or internal sensor noise accumulating over time.
• Noise: Random, unpredictable fluctuations in the measured angular rate. These are like small, erratic jolts affecting the measurement. Noise is inherent to all sensors and is often due to electronic noise in the circuitry.
• Scale Factor Error/Non-linearity: A discrepancy between the actual angular rate and the reported value. This is like having a ruler where the markings aren’t evenly spaced. The error is often proportional to the input value.
• Cross-axis Sensitivity: A measurement of rotation around one axis is affected by rotation around another axis. Think of this like measuring weight on a scale that’s slightly tilted – the weight measurement is influenced by the tilt angle.

Understanding these error types is crucial for effective gyroscope data processing and compensation.

Gyroscope calibration is essential for minimizing systematic errors. My experience encompasses various techniques, primarily focusing on in-situ and factory calibration. In-situ calibration, performed after the gyroscope is integrated into the system, involves techniques like:

• Zero-rate bias calibration: This involves holding the gyroscope stationary and averaging the sensor output to determine the bias. The calculated bias is then subtracted from subsequent readings.
• Scale factor calibration: This usually involves rotating the gyroscope at known angular rates and comparing the output to determine the scale factor. This often involves specialized equipment such as a high-precision turntable.
• Temperature compensation: Many gyroscopes have temperature sensors built-in. Calibration often involves collecting data at varying temperatures and establishing a temperature-dependent correction profile to compensate for drift due to temperature changes.

For factory calibration, more sophisticated methods involving robotic arms and precise rotation stages are employed to achieve higher accuracy. My experience also includes using calibration routines provided by gyroscope manufacturers, often involving proprietary algorithms and specific procedures for the specific gyroscope model.

Drift is a common gyroscope issue. Troubleshooting involves a systematic approach:

1. Verify Calibration: Ensure the gyroscope is properly calibrated. Re-calibrate if necessary, as described earlier.
2. Check for Temperature Effects: Gyroscope drift is highly sensitive to temperature variations. Monitor the gyroscope’s temperature and look for correlation between temperature changes and drift. If present, consider adding temperature compensation techniques, possibly employing a thermistor for accurate temperature reading.
3. Examine Power Supply: Fluctuations or noise in the power supply can induce drift. Ensure a stable and clean power source.
4. Inspect for Physical Stress: Vibrations, shocks, or improper mounting can contribute to drift. Secure the gyroscope firmly and isolate it from external vibrations if possible.
5. Investigate Software Issues: Incorrect data processing or filtering algorithms can exacerbate drift. Carefully review the software implementation.
6. Sensor Degradation: In some cases, sensor aging or damage may lead to increased drift. Consider replacing the sensor if other troubleshooting steps don’t resolve the issue.

A combination of these steps typically addresses the issue. The key is careful observation and a methodical approach to eliminate potential causes one by one.

Noise reduction in gyroscope data is crucial for accuracy. Common methods include:

• Averaging: Simple averaging of multiple consecutive readings effectively reduces random noise.
• Median Filtering: Replaces each data point with the median of its neighboring points, effectively removing outliers caused by noise spikes.
• Kalman Filtering (discussed in more detail below): A powerful algorithm that combines predictions with sensor measurements to estimate the true value, minimizing noise and drift.
• Moving Average Filters: Similar to simple averaging, but the averaging window can be adjusted to prioritize responsiveness or noise reduction.
• Butterworth Filters: Analog or digital filters that attenuate noise outside of a specified frequency band. Useful for removing high-frequency noise components.

The choice of method depends on the specific noise characteristics and the application’s requirements. For example, real-time applications might prefer computationally lighter methods like averaging or median filtering, while higher-accuracy applications may benefit from Kalman filtering.

Kalman filtering is a powerful technique for estimating the state of a dynamic system, which is ideal for processing gyroscope data. It recursively estimates the state by combining a prediction based on a model of the system’s dynamics with noisy measurements from the gyroscope. This process effectively reduces noise and drift, providing a smoother and more accurate estimate of the angular rate and orientation.

My experience with Kalman filtering involves implementing extended Kalman filters (EKFs) and unscented Kalman filters (UKFs) for integrating gyroscope data with other sensors such as accelerometers and magnetometers (often in an Inertial Measurement Unit or IMU). The EKF linearizes the non-linear system model, making it computationally efficient, but it has limitations when the non-linearities are significant. UKFs provide better accuracy but are computationally more expensive.

For example, in a robotics project involving a mobile robot, I implemented an EKF to fuse gyroscope and accelerometer data for accurate pose estimation. The Kalman filter’s ability to handle noise and drift was crucial for reliable navigation.

Choosing between EKF and UKF depends on the application’s computational constraints and the level of non-linearity in the system. Often, simulating both approaches is essential for finding the best balance between accuracy and computational cost.

Communication errors between a gyroscope and a microcontroller can stem from various sources. Diagnosing and resolving these issues requires a systematic approach:

1. Check Wiring: Ensure proper connection between the gyroscope and the microcontroller, verifying correct pin assignments and secure connections. Look for loose wires, shorts, or broken traces.
2. Verify Power Supply: Ensure the gyroscope is receiving sufficient and stable power. Use a multimeter to check voltage levels.
3. Inspect Communication Protocol: Verify the configuration of the communication protocol (SPI or I2C). Consult the gyroscope datasheet and verify clock speeds, data lines, and chip select signals.
4. Check for Data Integrity: Use a logic analyzer or oscilloscope to monitor the communication lines and observe for missing data or noise. This helps to pinpoint timing issues.
5. Review Software: Ensure correct implementation of the communication protocol in the microcontroller’s firmware. Incorrect initialization, data transfer routines, or error handling can cause communication failures.
6. Check Pull-up/Pull-down Resistors (I2C): If using I2C, verify that the necessary pull-up resistors are correctly installed on the data lines. This ensures stable communication levels.
7. Gyroscope’s Internal Registers: Consult the datasheet to access status registers within the gyroscope that may indicate communication errors or issues like read/write failures.

By systematically checking each component and aspect of the communication system, the root cause of the error can typically be identified and resolved.

My experience includes working extensively with both SPI (Serial Peripheral Interface) and I2C (Inter-Integrated Circuit) protocols for gyroscope communication. SPI offers higher speed and simpler hardware implementation but requires more lines. I2C is more power-efficient, uses fewer lines, and supports multiple devices on the same bus. The choice between the two depends on the specific application’s needs.

SPI: I’ve used SPI in applications requiring high data throughput, such as applications with demanding real-time constraints. I’m comfortable with configuring SPI parameters like clock speed, data order, and chip select, and troubleshooting issues related to these parameters. For example, I once resolved an issue in a high-speed data acquisition system by carefully adjusting the SPI clock speed to avoid data loss.

I2C: I’ve implemented I2C in applications where power consumption is critical, such as battery-powered embedded systems. My experience includes handling I2C bus arbitration and managing multiple devices on the same bus, as well as error handling associated with I2C acknowledgements and bus collisions. For example, in a low-power wearable sensor system, I utilized I2C to efficiently collect data from multiple sensors, including a gyroscope.

In both cases, I rely on datasheets and application notes to ensure correct configuration and proper handling of error conditions.

Gyroscope data, typically expressed as angular rates around three axes (roll, pitch, yaw), reveals how fast an object is rotating. To determine orientation, we integrate these rates over time. Imagine a spinning top: its gyroscope measures how quickly it’s spinning around each axis. Integrating these rates gives us the cumulative rotation around each axis since the start. However, this simple integration method accumulates drift over time—the longer the integration, the greater the error. This is why we often combine gyroscope data with other sensor data, such as accelerometers and magnetometers, through sensor fusion (discussed in the next question).

For example, if the gyroscope measures a constant angular rate of 10 degrees per second around the yaw axis for 5 seconds, a simple integration would indicate a 50-degree rotation. Realistically, this needs refinement using calibration and sensor fusion.

Sensor fusion is critical for accurate orientation estimation. I’ve extensively used Kalman filtering and complementary filtering. Kalman filtering is a powerful technique that combines data from multiple sensors, weighting them based on their respective noise characteristics and uncertainties. It’s like a sophisticated averaging process that minimizes error. Complementary filtering is a simpler approach that weights gyroscope data for short-term accuracy (gyroscopes are good at detecting quick changes) and accelerometer/magnetometer data for long-term stability (they’re better at estimating absolute orientation, but prone to noise).

In a recent project involving a drone’s stabilization system, I employed a Madgwick filter, a type of complementary filter, to seamlessly combine data from a gyroscope, accelerometer, and magnetometer. This resulted in a highly stable and accurate estimation of the drone’s orientation, even in the presence of significant vibrations or external disturbances. The choice of filter depends on the application’s specific requirements, computational resources, and sensor characteristics. I’ve worked with different implementations, adjusting parameters like process noise and measurement noise covariances to fine-tune the filter’s performance.

Outliers in gyroscope data can be caused by sensor noise, mechanical shocks, or glitches. I employ several strategies to handle them. First, I apply simple moving averages or median filters to smooth out short-term fluctuations. This is effective for removing noise spikes. For more substantial outliers, I use statistical methods to identify and reject data points that fall outside a predefined range. This range is typically determined based on the sensor’s specifications and historical data. If the outlier’s deviation is too extreme, I might simply interpolate the missing data using neighboring points. For more sophisticated applications, robust estimation techniques like RANSAC (Random Sample Consensus) can be applied.

For example, if I detect a sudden, unrealistic spike in the gyroscope reading (e.g., a 1000 degrees/second rotation when the expected range is 0-100 degrees/second), I’d flag it as an outlier and either replace it with an interpolated value or use a robust estimation algorithm to handle the anomaly without unduly influencing the overall orientation estimation.

My experience with gyroscope firmware debugging often involves analyzing sensor communication protocols (e.g., I2C, SPI), examining sensor registers for calibration data and error flags, and validating the firmware’s implementation of algorithms (e.g., sensor fusion). Debugging typically involves using logic analyzers to monitor the communication between the microcontroller and the gyroscope, inspecting memory dumps, and using debuggers to step through the firmware code. I’ve encountered instances of incorrect initialization, faulty data interpretation, or timing issues within the firmware that caused inaccurate or erratic sensor readings. For instance, a recent project involved a malfunctioning gyroscope due to a misinterpretation of a specific bit in the sensor’s status register. The problem was discovered using a logic analyzer, which revealed the firmware wasn’t correctly handling a particular error flag.

A systematic approach, incorporating unit testing and well-structured firmware code, is essential for effective debugging. The use of appropriate debugging tools and a thorough understanding of the sensor’s datasheet and communication protocol are also vital.

Verifying the accuracy and precision of a gyroscope involves both static and dynamic testing. Static testing involves placing the sensor on a stable platform and measuring the drift in its readings over time. A low drift rate indicates high accuracy. Precision is assessed by observing the noise level and repeatability of the measurements. Dynamic testing usually involves rotating the sensor at known rates and comparing the measured angular rates to the actual rates. This can be done using a precisely controlled turntable or robotic arm. A discrepancy between measured and actual rates reveals inaccuracy. The data acquisition and analysis software play a critical role in this process. Furthermore, I’ll compare the measured values to the specifications given in the sensor datasheet to determine if the sensor is performing within its specified limits.

Calibration is a crucial step. Many gyroscopes require bias and scale factor calibration to compensate for inherent offsets and nonlinearities. These calibrations can be performed using various techniques, including manual calibration using a known reference position or through more sophisticated automatic calibration routines.

Several sources contribute to errors in gyroscope measurements:

• Bias: A constant offset in the output even when the sensor is stationary. This is often temperature-dependent.
• Noise: Random fluctuations in the output, arising from electronic noise or mechanical vibrations.
• Scale Factor Error: Inaccuracy in the conversion from angular rate to the sensor’s output signal.
• Temperature Sensitivity: Changes in bias and scale factor due to temperature variations.
• Anisoelasticity: Unequal elasticity in different directions, leading to errors during rotation.
• Axis Misalignment: Imperfect alignment of the sensor’s axes, causing cross-axis sensitivities.

Understanding these sources of error is crucial for accurate data interpretation and sensor compensation techniques.

Rate gyroscopes and MEMS gyroscopes both measure angular velocity but differ significantly in their technology and characteristics:

• Rate Gyroscopes (also known as RLGs or FOGs): These are typically larger, more expensive, and higher-precision devices. They utilize mechanical principles (like spinning rotors or fiber-optic interferometry) to measure rotation. They generally offer superior accuracy and stability but are bulkier and consume more power. These are often used in high-precision applications like navigation systems.
• MEMS (Microelectromechanical Systems) Gyroscopes: These are miniaturized, low-cost devices fabricated using microfabrication techniques. They’re based on the Coriolis effect, where a vibrating element experiences a force perpendicular to its motion when rotated. They offer lower accuracy and are more susceptible to noise and drift but are smaller, lighter, and cheaper, making them ideal for consumer electronics and many robotics applications.

The choice between them depends on the application’s performance requirements, size constraints, and budget.

Testing the integrity of gyroscope data involves verifying its accuracy, precision, and consistency. We can’t simply trust the raw data; we need to validate it against known references or expected behavior.

A common approach is to perform a static test. This involves holding the gyroscope perfectly still and analyzing the output. Ideally, the readings should be very close to zero, with only minor variations due to noise. Significant drift or bias indicates a problem. We then look at its dynamic response by rotating it at known rates and comparing the measured angular velocity to the expected value. Any substantial deviations signal issues with the sensor’s calibration or internal mechanisms.

Data consistency checks are crucial. We look for sudden jumps or spikes in the data which might indicate glitches or faulty signal transmission. We use statistical methods such as calculating the standard deviation of the data to determine its variability. High standard deviation could point to noise or inconsistent performance. Finally, comparing the gyroscope data with data from other sensors (like accelerometers) provides cross-validation and helps identify inconsistencies or errors. For example, if the gyroscope reports a rotation while other sensors indicating stillness, we know there is an issue with the gyroscope data.

Real-time processing of gyroscope data is essential in applications demanding immediate feedback, such as robotics, autonomous vehicles, and virtual reality. My experience involves developing and implementing algorithms that handle data streams at high frequencies (often hundreds of Hz or even kHz) with minimal latency. This demands optimized code and efficient data structures.

I’ve worked extensively with embedded systems programming (using languages like C/C++) and have experience using real-time operating systems (RTOS) to ensure deterministic behavior and timely data processing. For instance, I developed a system for a drone where real-time gyroscope data was crucial for stability control; processing delays could lead to instability or crashes. To manage this, we used Kalman filtering techniques for noise reduction and sensor fusion, which smoothed the data and produced more reliable estimates of orientation. Efficient buffering and data pre-processing significantly reduced computational demands. Regular performance testing and optimization were crucial to maintain low latency and high throughput.

Troubleshooting a multi-sensor system requires a systematic approach. I start with a visual inspection of the system’s physical components, checking for loose connections, damaged wiring, or any obvious physical damage. This is followed by a review of the system’s software, ensuring data acquisition and communication protocols are functioning as intended.

Next, I examine the data from each sensor individually. This often involves plotting the data and comparing it to expected values or patterns. Discrepancies could indicate faults in a specific sensor. I then use a process of elimination. If one sensor seems consistently problematic, I isolate it for further investigation, using calibration procedures and comparing its readings with other sensors. If issues remain, I analyze the data transfer between sensors and the processing pipeline, looking for errors, delays, or data corruption. Using logging tools and debugging techniques helps trace the flow of data through the system. Finally, I will employ simulation techniques to model the system and identify potential problems, allowing me to isolate the root cause before implementing corrective measures.

Pinpointing the root cause of a gyroscope malfunction involves a combination of diagnostic steps. I begin by checking the gyroscope’s power supply and signal integrity. Loose connections or power fluctuations can lead to erratic behavior. Next, I examine the gyroscope’s calibration. If the calibration is off, the readings will be inaccurate. I then consider the possibility of sensor drift. This is a gradual change in the output over time. Drift can result from temperature changes, aging of components, or internal damage. Furthermore, environmental factors can play a role; strong magnetic fields or vibrations can disrupt the sensor’s operation.

Through a combination of data analysis (looking for patterns in faulty readings), sensor-specific diagnostic routines, and potentially even swapping the gyroscope with a known good unit, I methodically eliminate possibilities to identify the precise fault. For example, if data analysis shows a repeatable, predictable error correlated with temperature, this strongly suggests a thermal sensitivity issue. If the problem disappears when replacing the gyroscope, the fault lies in the hardware. This process often requires using specialized tools and software for detailed analysis.

My gyroscope software troubleshooting toolkit includes a range of tools and software. These extend beyond simple diagnostic software to advanced analysis tools.

• Data acquisition software: This is used to capture and store the gyroscope data streams. Examples include LabVIEW, DASYLab, and custom-written applications.
• Data visualization tools: Software like MATLAB, Python (with libraries like Matplotlib and SciPy), and specialized data loggers are essential for visualizing the sensor data, allowing me to identify anomalies or patterns.
• Debugging tools: Debuggers (like GDB or embedded system debuggers) are critical for identifying software bugs in the firmware or drivers that interact with the gyroscope.
• Sensor calibration software: Specialized software helps calibrate the gyroscope using known reference points, improving the accuracy of the readings.
• Signal processing software: This is needed for filtering noise, compensating for drift, and performing other signal processing tasks. Examples include libraries within MATLAB and Python.

Data logging and analysis are crucial for understanding gyroscope behavior. I typically use dedicated data loggers or custom-built applications to capture timestamped data from the gyroscope. The choice depends on factors like data rate, storage capacity, and the need for real-time monitoring. The data is then exported for analysis.

My analysis typically begins with visualization—plotting the angular velocity, acceleration, and other relevant parameters against time. I look for trends, drifts, spikes, or any other irregularities. Statistical analysis provides valuable insights. Calculating the mean, standard deviation, and other statistical measures helps characterize the data’s quality and identify outliers. Advanced techniques such as Fast Fourier Transforms (FFT) can be used to detect periodic patterns or frequencies that might indicate problematic behavior. Finally, correlating the gyroscope data with data from other sensors provides context and allows for a more comprehensive diagnosis.

Assessing gyroscope performance involves evaluating key metrics. These metrics differ based on the application, but common ones include:

• Bias: The average offset of the measured angular velocity from its true value when stationary. A high bias suggests a calibration problem.
• Drift: The rate at which the bias changes over time. High drift limits the accuracy of long-term measurements.
• Noise: Random fluctuations in the measured angular velocity. High noise reduces the precision of the data.
• Scale Factor Error: The difference between the actual angular velocity and the measured angular velocity, often expressed as a percentage error. This indicates whether the sensor’s reported values are consistent with the actual values.
• Linearity: The consistency of the sensor’s response across its range of motion. Non-linearity will cause the reported values to be inaccurate at different angular velocities.
• Bandwidth: The range of frequencies the gyroscope can accurately measure. A high bandwidth allows for the capture of fast movements.

By carefully analyzing these metrics and comparing them to the gyroscope’s specifications, we can determine if the device meets the requirements for the intended application. For example, a high-precision application like navigation might demand extremely low bias and drift, whereas a less critical application might tolerate higher noise levels.

Integrating gyroscope data with other sensor data is crucial for accurate and robust motion tracking in many applications, from robotics to virtual reality. My experience involves using sensor fusion techniques to combine gyroscope data (measuring angular velocity) with accelerometer data (measuring linear acceleration) and magnetometer data (measuring magnetic field direction). This is often done using complementary filters like Kalman filters or extended Kalman filters. These filters intelligently weigh the data from different sensors based on their accuracy and reliability, effectively mitigating the individual sensor noise and drift.

For example, in a drone navigation system, I’ve worked on integrating gyroscope data with GPS and barometer readings. The gyroscope provides precise short-term angular rate information, while the GPS and barometer offer slower but more accurate positional data. The Kalman filter combines these inputs to produce a highly accurate estimate of the drone’s position and orientation, even in challenging environments.

Another example involves using a similar approach for a wearable device that tracks human movement. Combining gyroscope data with accelerometer and magnetometer data allows for accurate tracking of hand gestures or limb movements, despite the inherent noise and movement artifacts in the individual sensor readings.

Inconsistent or unreliable gyroscope data can stem from various sources, including sensor noise, drift, and mechanical issues. My approach involves a multi-pronged strategy. First, I’d thoroughly investigate the data for outliers and unusual patterns using visualization tools and statistical analysis. This helps identify potential problems.

Next, I employ filtering techniques like moving averages, median filters, or more sophisticated Kalman filters to smooth out the noisy data. The choice of filter depends on the specific noise characteristics and application requirements. For example, a simple moving average might suffice for minor noise, while a Kalman filter provides better performance in the presence of systematic errors or drift.

If filtering alone is insufficient, I would investigate the sensor’s physical integrity, checking for loose connections, vibrations, or other mechanical problems. In some cases, data validation and rejection techniques might be needed to eliminate clearly erroneous data points. Finally, sensor calibration— discussed in more detail below—is crucial to correct for systematic errors.

Gyroscope biases are systematic errors that cause a non-zero output even when the gyroscope is stationary. These biases can be categorized into several types, including:

• Bias instability: Slow, random changes in the bias over time.
• Bias temperature sensitivity: Bias changes with temperature fluctuations.
• Scale factor error: Inaccuracy in the gyroscope’s sensitivity to angular rate.
• Non-linearity: Deviation from a linear relationship between angular rate and output.

Compensating for these biases involves a combination of techniques. Calibration is critical. This often involves a static phase where the sensor is stationary, allowing for the estimation of the bias. Temperature compensation is done by measuring the temperature and using a pre-determined model (often from factory data sheets or through empirical testing) to adjust the readings based on temperature variations. Advanced filtering techniques like Kalman filters can dynamically estimate and compensate for bias and other errors over time.

For instance, during the calibration phase, I’d place the gyroscope in multiple orientations to get a comprehensive understanding of the bias in each axis. Using these values, I would then subtract the bias from the gyroscope reading in real time.

Debugging gyroscope-related issues in embedded systems requires a systematic approach. I start by carefully examining the system’s hardware and software. I’d use logic analyzers and oscilloscopes to examine the signals going into and out of the gyroscope to see if they are in the expected range and if there are any signal integrity problems. I’d also verify correct initialization, power supply stability and communication with the sensor.

On the software side, I employ debugging tools such as print statements, logging, and debugging breakpoints to trace the flow of data and identify problematic code sections. Inspecting the raw data for anomalies is also extremely important. I pay close attention to the frequency of data acquisition and ensure appropriate synchronization with other sensors.

A common approach is to isolate the gyroscope from the rest of the system. If possible, I’d test the sensor standalone to ensure that the problem originates from the sensor itself and not from other parts of the embedded system. Unit testing and integration testing are essential steps to validate sensor functionality and data processing within the larger software architecture.

Additionally, simulation can be a very powerful tool, allowing me to reproduce the problem and test different hypotheses under controlled conditions.

Temperature compensation is vital for achieving high accuracy from gyroscopes, as temperature changes can significantly affect their bias and scale factor. My experience involves using various methods for temperature compensation, depending on the gyroscope and application. This typically involves obtaining the gyroscope’s temperature sensitivity parameters from the sensor’s datasheet or through extensive calibration.

A common approach is to use a lookup table that maps temperature to bias and scale factor adjustments. The gyroscope’s temperature is measured, and this value is used to index the lookup table to obtain appropriate compensation parameters. More sophisticated methods involve using mathematical models to predict the temperature effects and perform real-time compensation based on the model.

For example, I’ve worked on a project where the gyroscope’s temperature sensitivity was characterized and a second-order polynomial model was developed to fit the data. This model was then implemented in the embedded system’s firmware to compensate for temperature variations during operation. The choice of approach will depend on the accuracy requirements and computational resources available.

Troubleshooting intermittent errors in a gyroscope requires a methodical and patient approach. These errors can be difficult to diagnose because they are not consistently reproducible. My strategy typically begins by systematically analyzing the data, looking for patterns or correlations between error occurrences and other system parameters such as temperature, power supply fluctuations, and external factors. Data logging and visualization can be invaluable for identifying trends.

Next, I’d focus on creating a controlled environment to try and reproduce the errors consistently. This involves testing under different conditions (temperature, power supply variations, load conditions) and monitoring for correlations with error occurrence. Using a simulator and controlled data inputs is another important method to isolate the problem.

If the problem is not software-related, I’d consider the possibility of hardware failures, such as intermittent connection problems or sensor degradation. Thorough hardware inspection, including visual inspection of solder joints and connectors, might be necessary. In some cases, replacing the sensor might be the most efficient solution.