Multi Resolution Analysis compared Savitzky - Golay and Moving Average Filter by Noise Reduction

2020 ◽  
Author(s):  
Enrique San Juan ◽  
David Valenzuela ◽  
Pablo Adasme ◽  
Ismael Soto ◽  
Lucio Canete ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Moving Average ◽  
Moving Average Filter ◽  
Multi Resolution Analysis ◽  
Resolution Analysis ◽  
Average Filter

Adaptive Myriad Filter for Improved Gas Turbine Condition Monitoring Using Transient Data

2005 ◽  
Vol 127 (2) ◽  
pp. 329-339 ◽  
Author(s):  
Vellore P. Surender ◽  
Ranjan Ganguli
Keyword(s):  
Noise Reduction ◽  
Gas Turbine ◽  
Health Monitoring ◽  
Moving Average ◽  
Acceleration Process ◽  
Outlet Temperature ◽  
Outlier Removal ◽  
Moving Average Filter ◽  
Average Filter

The removal of noise and outliers from measurement signals is a major problem in jet engine health monitoring. In this study, we look at the myriad filter as a substitute for the moving average filter that is widely used in the gas turbine industry. The three ideal test signals used in this study are the step signal that simulates a single fault in the gas turbine, while ramp and quadratic signals simulate long term deterioration. Results show that the myriad filter performs better in noise reduction and outlier removal when compared to the moving average filter. Further, an adaptive weighted myriad filter algorithm that adapts to the quality of incoming data is studied. The filters are demonstrated on simulated clean and deteriorated engine data obtained from an acceleration process from idle to maximum thrust condition. This data was obtained from published literature and was simulated using a transient performance prediction code. The deteriorated engine had single component faults in the low pressure turbine and intermediate pressure compressor. The signals are obtained from T2 (IPC total outlet temperature) and T6 (LPT total outlet temperature) engine sensors with their nonrepeatability values that were used as noise levels. The weighted myriad filter shows even greater noise reduction and outlier removal when compared to the sample myriad and a FIR filter in the gas turbine diagnosis. Adaptive filters such as those considered in this study are also useful for online health monitoring, as they can adapt to changes in quality of incoming data.

Adaptive Myriad Filter for Improved Gas Turbine Condition Monitoring Using Transient Data

2004 ◽  
Author(s):  
Vellore P. Surender ◽  
Ranjan Ganguli
Keyword(s):  
Noise Reduction ◽  
Gas Turbine ◽  
Health Monitoring ◽  
Moving Average ◽  
Acceleration Process ◽  
Outlet Temperature ◽  
Outlier Removal ◽  
Moving Average Filter ◽  
Average Filter

The removal of noise and outliers from measurement signals is a major problem in jet engine health monitoring. In this study, we look at the myriad filter as a substitute for the moving average filter which is widely used in the gas turbine industry. The three ideal test signals used in this study are the step signal which simulates a single fault in gas turbine, while ramp and quadratic signals simulate long term deterioration. Results show that the myriad filter performs better in noise reduction and outlier removal when compared to the moving average filter. Further, an adaptive weighted myriad filter algorithm that adapts to the quality of incoming data is studied. The filters are demonstrated on simulated clean and deteriorated engine data obtained from an acceleration process from idle to maximum thrust condition. This data was obtained from published literature and was simulated using a transient performance prediction code. The deteriorated engine had single component faults in the low pressure turbine and intermediate pressure compressor. The signals are obtained from T2 (IPC total outlet temperature) and T6 (LPT total outlet temperature) engine sensors with their non-repeatability values which were used as noise levels. The weighted myriad filter shows even greater noise reduction and outlier removal when compared to the sample myriad and FIR filter in the gas turbine diagnosis. Adaptive filters such as those considered in this study are also useful for online health monitoring as they can adapt to changes in quality of incoming data.

Single and three phase moving average filter PLLs: Digital controller design recipe

2014 ◽  
Vol 116 ◽  
pp. 276-283 ◽  
Author(s):  
Naji Rajai Nasri Ama ◽  
Wilson Komatsu ◽  
Lourenco Matakas Junior
Keyword(s):  
Controller Design ◽  
Moving Average ◽  
Digital Controller ◽  
Moving Average Filter ◽  
Three Phase ◽  
Average Filter

scholarly journals Low Resource Complexity R-peak Detection Based on Triangle Template Matching and Moving Average Filter

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3997 ◽  
Author(s):  
Tam Nguyen ◽  
Xiaoli Qin ◽  
Anh Dinh ◽  
Francis Bui
Keyword(s):  
Template Matching ◽  
Moving Average ◽  
Detection Algorithm ◽  
Peak Detection ◽  
Moving Average Filter ◽  
Ecg Signals ◽  
Low Resource ◽  
Peak Detection Algorithm ◽  
Robustness To Noise ◽  
Average Filter

A novel R-peak detection algorithm suitable for wearable electrocardiogram (ECG) devices is proposed with four objectives: robustness to noise, low latency processing, low resource complexity, and automatic tuning of parameters. The approach is a two-pronged algorithm comprising (1) triangle template matching to accentuate the slope information of the R-peaks and (2) a single moving average filter to define a dynamic threshold for peak detection. The proposed algorithm was validated on eight ECG public databases. The obtained results not only presented good accuracy, but also low resource complexity, all of which show great potential for detection R-peaks in ECG signals collected from wearable devices.

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