Analysis of signal noise reduction by using filters

Magsi, Hina; Sodhro, Ali Hassan; Chachar, Faheem Akhtar; Abro, Saeed A. Khan

doi:10.1109/icomet.2018.8346412

Cited by 18 publications

(4 citation statements)

References 9 publications

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“…But unlike the previous section results, in the experimental test, the EMA method is the best method for data mining and gives the most accurate results. Actually, the EMA method works like a low‐pass filter 32 and reduces the signal noise and thus causes less error in the identification process.…”

Section: Resultsmentioning

confidence: 99%

Data mining based excitation system parameters estimation considering DCS and PMU measurements using cubature Kalman filter

Mohseni

Doroudi

Karrari

2020

Int Trans Electr Energ Syst

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Summary The excitation system is an important system of power plants, which has an effective role in power system dynamic and stability. Consequently, developing an approach for the excitation system's model and parameter estimation is a necessary research goal. In real situations, it is often needed to identify and estimate unknown parameters of the excitation system by field recorded signals. The recorded signal can be internal from the distributed control system (DCS) or external from the phasor measurement unit (PMU). In this article, a novel method based on cubature Kalman filter and data mining is proposed to identify the parameters of a standard type excitation system. Firstly, the best available signals for parameters estimation are chosen. Secondly, a method is proposed to parameters estimation of excitation systems efficiently, when both the DCS and PMU with different sample rates are employed to record the measurement data. Data mining is performed at intervals that DCS data is missing (or unavailable), while PMU data is available. Experimental data are used for validation of the proposed approach.

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Section: Resultsmentioning

confidence: 99%

Data mining based excitation system parameters estimation considering DCS and PMU measurements using cubature Kalman filter

Mohseni

Doroudi

Karrari

2020

Int Trans Electr Energ Syst

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“…Once acquired, the signals are subject to different processing techniques: filtering, noise reduction and compression. If these techniques are not applied, information may be lost due to noise interference [10].…”

Section: Data Acquisitionmentioning

confidence: 99%

Review of Fault Diagnosis Methods for Induction Machines in Railway Traction Applications

Issa,

Clerc,

Hologne-Carpentier

et al. 2024

Energies

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Induction motors make up approximately 80% of the electric motors in the railway sector due to their robustness, high efficiency, and low maintenance cost. Nevertheless, these motors are subject to failures which can lead to costly downtime and service interruptions. In recent years, there has been a growing interest in developing fault diagnosis systems for railway traction motors using advanced non-invasive detection and data analysis techniques. Implementing these methods in railway applications can prove challenging due to variable speed and low-load operating conditions, as well as the use of inverter-fed motor drives. This comprehensive review paper summarizes general methods of fault diagnosis for induction machines. It details the faults seen in induction motors, the most relevant signals measured for fault detection, the signal processing techniques for fault extraction as well as some classification algorithms for diagnosis purposes. By giving the advantages and drawbacks of each technique, it helps select the appropriate method that could address the challenges of railway applications.

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“…The suggested methodology is heavily dependent on filters such as Kalman filter, Butter Worth, Chebyshev, Elliptical Filters, and so on. In the medical sector, ECG (electrocardiogram) signals are also cleaned using filters such as low pass filter [4]. Apart from these, deep learning methodologies [5] and non-linear diffusion [6] have also been considered.…”

Section: Related Workmentioning

confidence: 99%

Towards Unsupervised Learning based Denoising of Cyber Physical System Data to Mitigate Security Concerns

Akter

Shahriar

2022

2022 IEEE Conference on Communications and Network Security (CNS)

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A dataset, collected under an industrial setting, often contains a significant portion of noises. In many cases, using trivial filters is not enough to retrieve useful information i.e., accurate value without the noise. One such data is timeseries sensor readings collected from moving vehicles containing fuel information. Due to the noisy dynamics and mobile environment, the sensor readings can be very noisy. Denoising such a dataset is a prerequisite for any useful application and security issues. Security is a primitive concern in present vehicular schemes. The server side for retrieving the fuel information can be easily hacked. Providing the accurate and noise free fuel information via vehicular networks become crutial. Therefore, it has led us to develop a system that can remove noise and keep the original value. The system is also helpful for vehicle industry, fuel station, and powerplant station that require fuel. In this work, we have only considered the value of fuel level, and we have come up with a unique solution to filter out the noise of high magnitudes using several algorithms such as interpolation, extrapolation, spectral clustering, agglomerative clustering, wavelet analysis, and median filtering. We have also employed peak detection and peak validation algorithms to detect fuel refill and consumption in charge-discharge cycles. We have used the R-squared metric to evaluate our model, and it is 98 percent In most cases, the difference between detected value and real value remains within the range of ±1L.

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Analysis of signal noise reduction by using filters

Cited by 18 publications

References 9 publications

Data mining based excitation system parameters estimation considering DCS and PMU measurements using cubature Kalman filter

Data mining based excitation system parameters estimation considering DCS and PMU measurements using cubature Kalman filter

Review of Fault Diagnosis Methods for Induction Machines in Railway Traction Applications

Towards Unsupervised Learning based Denoising of Cyber Physical System Data to Mitigate Security Concerns

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