Interview Questions for Pressure Sensitivity

Pressure sensors come in various types, each suited for specific applications. The choice depends on factors like pressure range, accuracy requirements, cost, and environmental conditions.

• Capacitive Pressure Sensors: These use changes in capacitance to measure pressure. They are known for their high accuracy and stability, often found in medical and aerospace applications.
• Piezoresistive Pressure Sensors: These rely on the change in electrical resistance of a material under pressure. They’re cost-effective and widely used in automotive and industrial settings.
• Piezoelectric Pressure Sensors: These generate an electrical charge proportional to the applied pressure. They are suitable for dynamic pressure measurements, like those in microphones or accelerometers.
• Strain Gauge Pressure Sensors: These employ strain gauges to detect minute changes in dimensions due to pressure. Their robustness and relatively simple design make them common in various industrial applications.
• Optical Pressure Sensors: These use light to measure pressure changes. They are often preferred in harsh environments due to their resistance to electromagnetic interference.

For example, a capacitive pressure sensor might be used in a medical blood pressure monitor for its high accuracy, while a piezoresistive sensor could be used in a tire pressure monitoring system due to its lower cost and robustness.

A capacitive pressure sensor works by measuring changes in capacitance. Imagine two conductive plates separated by a small gap. This gap acts as a dielectric. When pressure is applied, the distance between the plates changes, directly affecting the capacitance. This change in capacitance is then measured and converted into a pressure reading.

The capacitance (C) is inversely proportional to the distance (d) between the plates: C = εA/d, where ε is the permittivity of the dielectric and A is the area of the plates. As pressure increases, ‘d’ decreases, and the capacitance ‘C’ increases proportionally. A sensor circuit then measures this capacitance change and translates it into a pressure value.

Piezoresistive pressure sensors utilize the principle of piezoresistivity. Certain materials, like silicon, exhibit a change in electrical resistance when subjected to mechanical stress or pressure. A piezoresistive sensor typically incorporates a small silicon diaphragm with a Wheatstone bridge configuration of piezoresistive elements etched onto it.

When pressure is applied to the diaphragm, it deforms, causing a change in the resistance of the piezoresistive elements. This resistance change unbalances the Wheatstone bridge, producing a measurable voltage that’s directly proportional to the applied pressure. This voltage is then amplified and processed to provide a pressure reading.

Each pressure sensor technology presents its own set of advantages and disadvantages:

• Capacitive: Advantages: High accuracy, good stability, long lifespan. Disadvantages: More expensive, can be sensitive to temperature changes.
• Piezoresistive: Advantages: Low cost, simple design, good sensitivity. Disadvantages: Lower accuracy than capacitive sensors, temperature sensitivity can be an issue.
• Piezoelectric: Advantages: Excellent for dynamic measurements, high sensitivity. Disadvantages: Prone to drift, limited lifespan.
• Strain Gauge: Advantages: Robust, reliable, wide pressure range. Disadvantages: Relatively low sensitivity, susceptible to temperature effects.
• Optical: Advantages: Immune to electromagnetic interference, suitable for harsh environments. Disadvantages: More complex and expensive.

The best choice depends heavily on the specific application and its requirements. For example, a high-precision medical application might necessitate the higher accuracy of a capacitive sensor, while a simple industrial pressure monitoring system might opt for the cost-effectiveness of a piezoresistive sensor.

Hysteresis in a pressure sensor refers to the difference in output for the same pressure value when the pressure is increasing versus decreasing. Imagine pushing a button; it might require more force to push it down than to push it back up. That difference is analogous to hysteresis.

It’s caused by internal friction and non-linearity within the sensor’s material or mechanism. A high hysteresis value indicates a less precise sensor, as the output reading is not solely determined by the current pressure but also by the pressure history. Sensor specifications will typically provide a hysteresis value, often expressed as a percentage of the full-scale pressure range.

Calibrating a pressure sensor involves establishing a precise relationship between the sensor’s output and the actual pressure. This is typically done using a known pressure source, like a deadweight tester or a calibrated pressure gauge.

The process generally involves:

1. Zeroing: Measuring the output at zero pressure.
2. Span Calibration: Applying a known pressure (e.g., full-scale pressure) and measuring the corresponding output.
3. Linearity Check: Applying multiple known pressures across the sensor’s range to verify the linear relationship between pressure and output. Any deviations are noted.
4. Calibration Curve Generation: Based on the collected data, a calibration curve is generated, often using a polynomial fit to represent the relationship between pressure and output. This curve is used to correct future readings.

Calibration ensures accuracy and traceability. The frequency of calibration depends on the application and sensor requirements, ranging from daily to yearly calibrations.

Several factors can introduce errors in pressure measurements:

• Temperature Effects: Temperature changes can affect the sensor’s sensitivity and output. Temperature compensation techniques are crucial for accurate measurements.
• Hysteresis: As discussed earlier, hysteresis leads to discrepancies depending on the pressure’s direction of change.
• Non-linearity: Sensor output might not perfectly correlate linearly with pressure across its range.
• Zero Drift: The sensor’s zero-pressure output can shift over time or due to environmental conditions.
• Installation Effects: Improper installation, such as vibrations or leaks in the system, can affect the readings.
• Sensor Degradation: Sensors degrade over time, leading to decreased accuracy.

Addressing these error sources through proper calibration, compensation techniques, and careful installation procedures is essential for obtaining reliable pressure measurements.

Signal noise in pressure sensor data is unavoidable. It’s essentially unwanted variations in the sensor’s output that aren’t related to actual pressure changes. These fluctuations can stem from various sources, including electronic interference, vibrations, temperature variations, and even the inherent limitations of the sensor itself. Handling this noise is crucial for accurate pressure measurements.

Several techniques exist to mitigate noise. Filtering is a cornerstone approach. This involves using either hardware filters (e.g., analog filters in the sensor’s signal conditioning circuit) or software filters (digital filters applied to the digitized data). Common digital filters include moving averages, Kalman filters, and various wavelet transforms. The choice depends on the characteristics of the noise and the desired response time. For instance, a moving average smooths out high-frequency noise but can lag behind rapid pressure changes. Kalman filters excel in scenarios with unpredictable noise and dynamic systems.

Calibration is another critical step. Regular calibration against known pressure standards helps establish a baseline and correct for systematic errors or sensor drift. This ensures the sensor output reflects true pressure values despite the noise. For instance, a zero-pressure offset can be calibrated out.

Data averaging is a simple yet effective technique to reduce random noise. By averaging multiple readings, the random fluctuations tend to cancel each other out. The more readings averaged, the smoother the data becomes, but the response time may decrease.

Finally, proper shielding and grounding of the sensor and its associated circuitry can minimize external electronic interference, a significant source of noise.

Sensor linearity refers to how closely the sensor’s output matches a straight line when plotted against the actual input pressure. A perfectly linear sensor would produce an output that increases proportionally with increasing pressure. High linearity is paramount for accuracy because it simplifies data interpretation and reduces errors.

A non-linear sensor produces a curved relationship between input and output. This nonlinearity introduces errors in the measurement because a simple linear equation cannot accurately represent the relationship. The further the sensor’s response deviates from linearity, the greater the potential for significant inaccuracies, particularly at the higher and lower ends of its measurement range. For example, in a medical application monitoring blood pressure, nonlinearity could lead to misdiagnosis or improper treatment.

To quantify linearity, manufacturers usually specify a linearity error, often expressed as a percentage of the full-scale range. Lower linearity error indicates better linearity and, therefore, higher accuracy. The impact of nonlinearity on accuracy can be mitigated through careful calibration and the use of look-up tables or sophisticated algorithms that model the sensor’s nonlinear behavior.

Pressure sensor resolution refers to the smallest increment of pressure change the sensor can detect and reliably report. It is usually expressed in units of pressure (e.g., Pascal, psi). A higher resolution means the sensor can distinguish finer pressure differences.

The significance of resolution depends heavily on the application. For example, in a high-precision application like measuring the pressure inside a scientific instrument, high resolution is crucial to ensure accurate readings. In contrast, a lower resolution sensor might suffice for less demanding applications such as monitoring simple pneumatic systems.

Imagine trying to weigh flour for a delicate pastry versus weighing gravel for a construction project. High resolution is vital for the pastry to ensure the precise amount of flour is measured. A lower resolution scale is acceptable for the gravel, where minor variations in weight are less critical. Similarly, pressure sensor resolution must match the demands of the specific application.

Signal conditioning for pressure sensors involves preparing the raw sensor signal to make it suitable for further processing or display. This often includes amplification, filtering, and linearization.

Amplification boosts a weak sensor signal to a level suitable for an analog-to-digital converter (ADC) or other data acquisition systems. This is particularly important for sensors with low output sensitivity.

Filtering, as previously discussed, removes unwanted noise. The type of filter used depends on the nature of the noise and the system’s response requirements.

Linearization compensates for nonlinear behavior in the sensor output, ensuring a more accurate and predictable relationship between pressure and sensor reading. This can be achieved through various methods, including the use of look-up tables, polynomial fitting, and specialized linearization circuits.

Excitation is often a crucial part of signal conditioning; some sensors require a specific voltage or current source to operate correctly. The stability and accuracy of this excitation source directly influence the overall accuracy of the measurement.

Example: A wheatstone bridge circuit is commonly used for signal conditioning in strain gauge-based pressure sensors. The bridge output, which is proportional to pressure, is then amplified and filtered to obtain a clean and usable signal.

Choosing the right pressure sensor involves careful consideration of several factors:

• Pressure range: The sensor’s maximum and minimum pressure measurement capabilities must encompass the expected pressure range of the application. Choosing a sensor with a range significantly exceeding the application’s needs is unnecessary and adds expense, whereas a too-narrow range limits accuracy and may damage the sensor.
• Accuracy and linearity: The required accuracy dictates the choice of sensor. High-precision applications demand sensors with low linearity error and high accuracy.
• Resolution: The needed resolution depends on the sensitivity of the application to small pressure changes. High-resolution sensors are needed where subtle pressure variations need to be detected.
• Sensor type: Different sensor technologies (e.g., piezoresistive, capacitive, piezoelectric) have different strengths and weaknesses. The choice depends on factors like cost, sensitivity, response time, and environmental conditions.
• Operating temperature and environment: The sensor must be able to withstand the operating temperature and other environmental factors (humidity, vibration, etc.) of the application.
• Output signal: Consider whether the sensor’s output signal is compatible with the data acquisition system (e.g., analog voltage, digital output).
• Size and mounting: The sensor’s physical size and mounting requirements should be compatible with the application’s constraints.
• Cost: Balancing performance requirements with budgetary constraints is crucial.

For instance, a high-accuracy, high-resolution capacitive sensor might be suitable for medical applications, whereas a simpler, lower-cost piezoresistive sensor might suffice for industrial process monitoring.

A pressure transducer is a device that converts pressure into an electrical signal. It’s essentially a pressure sensor with built-in signal conditioning circuitry. This makes it easier to integrate into data acquisition systems because the output is a ready-to-use electrical signal, often a voltage or current, rather than a raw sensor signal that requires further conditioning.

Different types of pressure transducers use various sensing elements to convert pressure into an electrical signal. Common elements include:

• Strain gauges: These measure the deformation of a diaphragm or other elastic element under pressure, converting this deformation into a change in electrical resistance.
• Capacitive sensors: These rely on the change in capacitance between two plates due to pressure-induced displacement of one plate.
• Piezoresistive sensors: These utilize changes in the electrical resistance of a semiconductor material due to pressure.
• Piezoelectric sensors: These generate an electrical charge proportional to the applied pressure.

Pressure transducers are ubiquitous in many industries, including automotive, aerospace, process control, and medical applications, to measure pressure in diverse systems.

Working with high-pressure sensors presents significant safety challenges. The high pressures involved pose risks of injury or equipment damage if proper precautions aren’t taken.

Key safety considerations include:

• Pressure relief valves: Incorporating pressure relief valves is crucial to prevent over-pressurization and potential failures. These valves release excess pressure to avoid hazardous situations.
• Proper sensor selection: Ensuring the sensor’s pressure rating significantly exceeds the maximum anticipated pressure is vital. Selecting a sensor with an inadequate pressure rating can lead to catastrophic failure.
• Burst protection: In critical applications, consider using burst discs or other protective mechanisms to contain pressure in case of sensor failure.
• System integrity: Regular inspection and maintenance of the entire pressure measurement system are essential to identify potential weaknesses before they cause accidents. This includes checking for leaks, corrosion, or other signs of wear and tear.
• Personal protective equipment (PPE): Using appropriate PPE, such as safety glasses, gloves, and pressure suits (depending on the pressure level), is essential to protect personnel from potential injury.
• Emergency procedures: Establish clear emergency procedures for handling pressure-related incidents. Personnel should be trained on these procedures.
• Safety interlocks: Employing interlocks to prevent system operation if pressure levels exceed safe limits is a vital safeguard.

Ignoring these safety considerations can result in serious injuries, equipment damage, and environmental hazards. Safety should always be the top priority when working with high-pressure systems.

Troubleshooting a malfunctioning pressure sensor involves a systematic approach. First, I’d verify the sensor’s power supply and connections, ensuring proper voltage and grounding. A simple multimeter check can often reveal loose wires or power issues. Next, I’d examine the sensor’s output signal. For analog sensors, this might involve using an oscilloscope to check for expected voltage changes with applied pressure. Digital sensors, on the other hand, would require checking the digital communication protocol (e.g., SPI, I2C) for correct data transmission. If the signal is erratic or absent, the sensor itself might be faulty and require replacement.

However, sometimes the problem lies elsewhere. A clogged pressure port, for instance, can prevent accurate readings. I’d inspect the port for obstructions. Environmental factors also play a crucial role. Extreme temperatures or vibrations can affect sensor accuracy and longevity; these conditions need to be considered. Finally, I’d compare the sensor’s readings to a known accurate pressure source (e.g., a calibrated pressure gauge) to validate the sensor’s accuracy. This helps differentiate between a sensor malfunction and a calibration drift. If all else fails, a careful review of the sensor’s specifications and the system’s documentation may offer additional clues.

I have extensive experience with both analog and digital pressure sensor interfaces. Analog sensors provide a continuous voltage or current output that’s proportional to the applied pressure. This is simpler to implement but can be more susceptible to noise and requires analog-to-digital conversion (ADC) for digital processing. I’ve worked extensively with potentiometric and capacitive sensors that fall into this category, utilizing ADCs to translate their analog signals into digital readings for analysis and control. For example, in a project involving a hydraulic system, I used a potentiometric pressure sensor, whose output was fed into a microcontroller via an ADC, allowing precise control of the system’s pressure.

Digital sensors, in contrast, provide a digital output directly, usually via communication protocols like I2C or SPI. These offer improved accuracy, noise immunity, and simplify data acquisition. My experience includes using digital pressure sensors based on piezoresistive technology in applications requiring high precision and reliable communication, such as in medical devices where precise blood pressure monitoring is critical. The digital interface streamlines data transfer and reduces the need for signal conditioning, making the system more robust and efficient. The choice between analog and digital depends heavily on the application’s specific requirements regarding accuracy, cost, and complexity.

Pressure mapping involves the simultaneous measurement of pressure at multiple points on a surface. This generates a spatial representation of pressure distribution. Think of it like a topographical map, but instead of elevation, we’re visualizing pressure variations. This technique is incredibly valuable in diverse fields.

For instance, in the medical field, pressure mapping is extensively used in podiatry to analyze pressure distribution on the feet, assisting in the diagnosis and treatment of foot ulcers in diabetic patients. In sports, pressure mapping sensors embedded in shoes or sports equipment provide insights into athlete performance and injury prevention by revealing pressure points during movement. In manufacturing, this technology finds applications in optimizing product design, for example, in designing more comfortable seating by identifying and reducing pressure hotspots. The applications are vast and expanding rapidly as sensor technology continues to improve and become more accessible.

Ensuring the accuracy and reliability of pressure measurements is paramount. This involves a multi-pronged approach starting with the selection of the appropriate sensor for the application. The sensor’s specifications, including its accuracy, range, and stability, need to be carefully considered to match the measurement requirements. Calibration is crucial; regularly calibrating sensors against known standards is vital to maintain accuracy over time and compensate for any drift.

Environmental factors significantly impact readings, so careful consideration of temperature, humidity, and vibration is necessary. Appropriate compensation techniques or environmental control might be needed. Data acquisition systems should be properly designed to minimize noise and interference, and signal conditioning techniques (e.g., filtering) can enhance signal quality. Regular maintenance and periodic sensor replacements are important. Furthermore, employing redundancy or using multiple sensors in parallel can provide a cross-check and improve the overall reliability of measurements.

Key Performance Indicators (KPIs) for pressure sensors vary based on application, but some common ones include:

• Accuracy: The closeness of the measured value to the true value. This is often expressed as a percentage of full-scale output.
• Precision: The repeatability of measurements under identical conditions. It reflects the sensor’s consistency.
• Linearity: How closely the sensor’s output follows a straight line across its operating range. Deviations indicate non-linear behavior.
• Hysteresis: The difference in output for the same input when approached from opposite directions. A low hysteresis value indicates better stability.
• Response Time: How quickly the sensor reacts to changes in pressure.
• Stability: How consistent the sensor’s output remains over time and under varying conditions.
• Temperature Sensitivity: How much the sensor’s output changes with temperature variations.
• Operating Range: The range of pressures the sensor can accurately measure.

These KPIs guide the selection, evaluation, and performance monitoring of pressure sensors in various applications.

My experience with data acquisition systems for pressure sensors encompasses a wide range of hardware and software tools. I’ve used both general-purpose data acquisition (DAQ) systems from vendors like NI (National Instruments) and specialized embedded systems, depending on the specific needs of the application. These systems handle signal conditioning, analog-to-digital conversion, data logging, and often incorporate software for real-time analysis and visualization of pressure data.

In one project involving the monitoring of a large-scale industrial process, I utilized an NI DAQ system to collect data from numerous pressure sensors distributed throughout the facility. The system’s software allowed for real-time monitoring, data logging, and automated alerts for pressure deviations outside specified limits, enabling proactive intervention and preventing potential hazards. In more resource-constrained applications, I’ve developed custom embedded systems employing microcontrollers (e.g., Arduino, STM32) to acquire and process pressure sensor data, reducing costs and enhancing system integration. The choice of DAQ system always depends on factors such as the number of sensors, sampling rate, required processing power, and overall budget.

Pressure sensor packaging significantly impacts their performance, reliability, and suitability for specific environments. I’ve worked with various types, each with its advantages and disadvantages.

For example, chip-on-board (COB) packaging offers miniaturization and cost-effectiveness but limited environmental protection. Surface-mount packages are suitable for automated assembly but might require additional protection against harsh environments. Hermetically sealed packages offer superior protection against moisture, dust, and other environmental factors but are usually more expensive. Stainless steel housings provide robust protection in demanding industrial applications. The selection depends on factors such as the application’s environmental conditions, size constraints, and budget. In a medical implant application, for instance, biocompatibility and hermetic sealing are crucial, while in a simple industrial setting, a more cost-effective, less protective packaging may suffice.

Interpreting pressure sensor data involves understanding the raw output signal and converting it into meaningful pressure units. This process often requires considering several factors.

• Calibration Data: Every pressure sensor needs calibration data that relates the raw sensor output (e.g., voltage, resistance) to the actual pressure. This data is crucial for accurate interpretation. Without it, the raw signal is meaningless.
• Sensor Type: Different sensors (e.g., piezoresistive, capacitive) have different output characteristics. Understanding the specific sensor type is key to proper data interpretation.
• Environmental Factors: Temperature, humidity, and other environmental factors can affect sensor readings. Compensation algorithms are frequently employed to account for these influences.
• Signal Processing: Raw sensor signals often contain noise and drift. Signal processing techniques, like filtering and averaging, are used to improve data quality before interpretation.

For example, if a piezoresistive sensor outputs a voltage of 2.5V, I would consult its calibration curve to determine the corresponding pressure. Let’s say the curve shows 2.5V corresponds to 100 kPa. However, if the ambient temperature is significantly different from the calibration temperature, I’d apply temperature compensation to refine this reading for greater accuracy.

My experience with pressure sensor testing and validation involves a rigorous process designed to ensure accuracy and reliability. It typically includes:

• Static Calibration: Applying known pressures across the sensor’s operating range and recording the corresponding outputs. This data is used to create a calibration curve.
• Dynamic Testing: Subjecting the sensor to varying pressures at different frequencies to assess its response time and stability.
• Environmental Testing: Evaluating the sensor’s performance under various temperature, humidity, and vibration conditions.
• Hysteresis and Drift Analysis: Assessing the sensor’s repeatability and long-term stability.
• Uncertainty Analysis: Quantifying the uncertainties associated with the sensor readings.

In one project, I worked on validating a pressure sensor for use in a high-pressure hydraulic system. We employed a deadweight tester to apply precise pressures and compared the sensor readings to the known values. We then meticulously documented the results, including uncertainties and any observed drift. This thorough validation ensured the sensor met the system’s stringent accuracy requirements.

The difference between absolute, gauge, and differential pressure sensors lies in their reference point:

• Absolute Pressure Sensor: Measures pressure relative to a perfect vacuum (0 absolute pressure). It’s often used in applications where atmospheric pressure fluctuations could affect measurements. Think altimeters or weather balloons.
• Gauge Pressure Sensor: Measures pressure relative to atmospheric pressure. A gauge pressure of 0 indicates that the pressure is the same as the surrounding atmosphere. Tire pressure gauges are a common example.
• Differential Pressure Sensor: Measures the difference in pressure between two points. It’s used in many industrial and medical applications, such as monitoring pressure drops across filters or measuring blood pressure.

Imagine you have a balloon inflated to 2 atm (atmospheres). An absolute pressure sensor would read 2 atm + 1 atm (atmospheric pressure) = 3 atm. A gauge pressure sensor would read 2 atm, and a differential pressure sensor, if comparing the inside of the balloon to the outside, would also read 2 atm.

Integrating pressure sensors into embedded systems involves careful consideration of several aspects:

• Sensor Selection: Choosing a sensor with appropriate pressure range, accuracy, size, and interface compatibility with the microcontroller.
• Signal Conditioning: Amplifying, filtering, and converting the analog sensor signal into a digital format that the microcontroller can understand using Analog-to-Digital Converters (ADCs).
• Power Management: Designing power circuitry that meets the sensor’s power requirements efficiently.
• Software Integration: Developing firmware that reads data from the sensor, processes it, and performs any necessary calculations or compensation.

In a past project, I integrated a MEMS pressure sensor into a weather monitoring station. I used an ADC to convert the sensor’s analog output to a digital value, which was then processed by an Arduino microcontroller to calculate altitude using barometric pressure. The code included temperature compensation algorithms to correct for ambient temperature variations and ensure accurate altitude readings. // Example code snippet (Arduino): int pressureReading = analogRead(sensorPin); // ... processing and compensation ...

Temperature compensation is crucial in pressure sensors because temperature significantly affects sensor output. Most pressure sensors exhibit a change in sensitivity and offset with temperature variations. Without compensation, this temperature-induced error can lead to significant inaccuracies in pressure readings.

Temperature compensation typically involves either using a temperature sensor in conjunction with the pressure sensor and applying a mathematical model to correct for temperature effects or employing sensors that have built-in temperature compensation. This correction is essential for maintaining accuracy and reliability, particularly in applications where temperature fluctuations are expected.

For instance, a pressure sensor deployed outdoors will experience substantial temperature swings throughout the day. Without temperature compensation, these swings would lead to errors in pressure readings that could be several times larger than the desired accuracy. The compensation algorithm ensures the final reading is highly accurate, regardless of temperature.

Designing a pressure sensing system for a specific application requires a systematic approach:

• Defining Requirements: Determining the necessary pressure range, accuracy, response time, and environmental conditions.
• Sensor Selection: Choosing a sensor that meets the defined requirements, considering factors like cost, size, and power consumption.
• Signal Conditioning: Designing the circuitry to amplify, filter, and convert the sensor signal.
• Data Acquisition: Choosing an appropriate data acquisition system (e.g., microcontroller, data logger).
• Calibration and Compensation: Developing calibration procedures and compensation algorithms to account for temperature and other factors.
• System Integration and Testing: Integrating all components and rigorously testing the system under various conditions.

For example, designing a system to measure blood pressure requires selecting a sensor with a highly sensitive pressure range, fast response time, and biocompatibility. The signal conditioning circuitry should be designed to minimize noise and artifacts. Calibration and compensation are especially crucial to ensure accurate and reliable blood pressure measurements.

My experience with pressure sensor data analysis and interpretation involves a range of techniques:

• Data Cleaning: Removing outliers and noise from the raw data using filtering techniques.
• Calibration Curve Application: Using the calibration data to convert raw sensor output into meaningful pressure units.
• Statistical Analysis: Analyzing the data to determine trends, variability, and other statistical properties.
• Signal Processing: Employing techniques such as Fourier transforms to extract useful information from the sensor signals.
• Model Development: Developing mathematical models to predict pressure based on other sensor data.

In one project, I analyzed pressure sensor data from a pipeline to detect leaks. By applying a combination of filtering, statistical analysis, and time series analysis, we were able to identify subtle pressure variations indicative of leaks that were previously undetectable. This led to early detection and preventive maintenance, significantly reducing potential environmental damage and cost.