What are the best techniques for filtering noisy sensor data in control systems?

Noisy sensor data can be detrimental to the performance and stability of your control system. To filter out unwanted noise and preserve useful information, there are several techniques you can use. Choosing the right sensor type and location is a great place to start. You can also apply low-pass, high-pass, band-pass, or notch filters, or use adaptive filters or Kalman filters. Moving average, median, or mode filters are also effective, as well as outlier detection and removal methods. With the right filtering techniques, you can ensure your control system is running smoothly and accurately.

1 Sensor type and location

The first step to reduce noise in your sensor data is to choose the right sensor type and location for your application. Different sensors have different characteristics, such as sensitivity, accuracy, resolution, bandwidth, and noise level. You should select a sensor that matches your control system requirements and minimizes the interference from external sources. Moreover, you should place your sensor as close as possible to the variable you want to measure and avoid any sources of vibration, heat, or electromagnetic noise.

Add your perspective

Víctor M.
(edited)
Report contribution
One of the most common techniques for filtering noisy sensor data is to use frequency domain filters, such as low-pass, high-pass, band-pass, or notch filters. These filters allow you to remove or attenuate the noise components that are outside the frequency range of interest. Another way to reduce sensor data noise is to apply noise reduction algorithms to the sensor data after collection. This can include smoothing, averaging, median filtering, or more advanced methods, such as wavelet denoising, principal component analysis, or independent component analysis.

Like

2 Frequency domain filters

One of the most common techniques for filtering noisy sensor data is to use frequency domain filters, such as low-pass, high-pass, band-pass, or notch filters. These filters allow you to remove or attenuate the noise components that are outside the frequency range of interest. For example, a low-pass filter can eliminate high-frequency noise, such as white noise or measurement noise, while preserving the low-frequency signal, such as a steady-state value or a slow trend. You can design and implement frequency domain filters using various methods, such as Butterworth, Chebyshev, Bessel, or elliptic filters.

Add your perspective

3 Adaptive filters or Kalman filters

Another technique for filtering noisy sensor data is to use adaptive filters or Kalman filters. These filters are able to adjust their parameters dynamically based on the characteristics of the signal and the noise. For example, an adaptive filter can update its coefficients using a feedback mechanism, such as the least mean square (LMS) algorithm, to minimize the error between the desired and the actual output. A Kalman filter can estimate the state of a dynamic system using a mathematical model and a set of measurements, while accounting for the uncertainty and noise in both the model and the measurements.

Add your perspective

4 Time domain filters

A third technique for filtering noisy sensor data is to use time domain filters, such as moving average, median, or mode filters. These filters operate on a window of data points and apply a statistical function to obtain a smoothed output. For example, a moving average filter can calculate the mean of the data points within the window and use it as the output. A median filter can find the median of the data points within the window and use it as the output. A mode filter can find the most frequent value of the data points within the window and use it as the output. These filters can reduce the effects of random noise, outliers, or spikes in the data.

Add your perspective

5 Outlier detection and removal methods

Outlier detection and removal is a fourth technique for filtering noisy sensor data. This method looks for data points that are significantly different from the rest of the data, such as due to sensor faults, transmission errors, or environmental disturbances. Various methods can be used for outlier detection and removal, including statistical tests like z-score, interquartile range, or Grubbs' test; clustering techniques like k-means, hierarchical, or density-based clustering; and machine learning techniques like neural networks, support vector machines, or random forests. These methods can help identify and eliminate the abnormal data points.

Add your perspective

Alejandro Ena Bernad

Signal Processing Engineer en Indivi
Report contribution
Uno de los mejores métodos en relación complejidad/resultado es el llamado Hampel Filter. Es un tipo de filtro que utiliza el MAD(Median Absolute Deviation), en el cuál en ventanas de tiempo se calcula el MAD para cada valor y si es n veces mayor que el predefinido ese valor se considera un "outlier". El tamaño de la ventana también es configurable. Por tanto, con dos parámetros puedes controlar el filtro. Este filtro funciona bastante bien debido a su baja dificultad técnica y la facilidad para establecer sus parámetros, por lo que es adecuado cuando se está haciendo el tratamiento de una señal en la que los "outliers" sobresalen con claridad

Translated

Like

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

Add your perspective

Víctor M.
Report contribution
A moving average filter is a basic technique that can be used to remove noise (random interference) from a signal. It is a simplified form of a low-pass filter. Running a signal through this filter will remove higher frequency information from the output.

Like

What are the best techniques for filtering noisy sensor data in control systems?

1

2

3

4

5

6

1 Sensor type and location

2 Frequency domain filters

3 Adaptive filters or Kalman filters

4 Time domain filters

5 Outlier detection and removal methods

6 Here’s what else to consider

Control Engineering

Rate this article

Thanks for your feedback

More articles on Control Engineering

More relevant reading