Order tracking signal processing for open rotor acoustics

2014 ◽  
Vol 333 (16) ◽  
pp. 3818-3830 ◽  
Author(s):  
David B. Stephens ◽  
Håvard Vold
Keyword(s):  
Signal Processing ◽  
Order Tracking ◽  
Open Rotor ◽  
Rotor Acoustics ◽  
Tracking Signal

Computed Order Tracking and its Application to NVH

2013 ◽  
Vol 321-324 ◽  
pp. 692-696
Author(s):  
Xiao Bing Wu
Keyword(s):  
Signal Processing ◽  
Engine Speed ◽  
Rotation Frequency ◽  
Vibration Signal ◽  
Acoustic Signals ◽  
Harmonic Order ◽  
Shaft Speed ◽  
Frequency Spectra ◽  
Order Tracking ◽  
Order Spectrum

Analysis of vibration or acoustic signals from rotating machines is often preferred in terms of order spectra rather than frequency spectra. An order spectrum gives the amplitude and/or the phase of the signal as a function of harmonic order of the rotation frequency. Order tracking requires sampling of the vibration signal at constant angular increments and hence at a rate proportional to the shaft speed. Most commercial software for signal processing has the built-in capability of computed order tracking, such as the 7702 order tracking software of B&K Pulse. The order tracking result of interior noise of cab is presented in this paper. From order tracking analysis on the noise of interior cab, we found that the noise mainly comes from the second order excitation of engine, and that the overall noise with respect to engine speed

scholarly journals Integrating Offline Object Tracking, Signal Processing, and Artificial Intelligence to Classify Relevant Events in Sawmilling Operations

Forests ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1333
Author(s):  
Stelian Alexandru Borz ◽  
Marius Păun
Keyword(s):  
Artificial Intelligence ◽  
Signal Processing ◽  
Object Tracking ◽  
Low Cost ◽  
Small Scale ◽  
Differential Signal ◽  
The Time Domain ◽  
Basic Level ◽  
Tracking Signal

Sawmilling operations are typically one of the most important cells of the wood supply chain as they take the log assortments as inputs to which they add value by processing lumber and other semi-finite products. For this kind of operations, and especially for those developed at a small scale, long-term monitoring data is a prerequisite to make decisions, to increase the operational efficiency and to enable the precision of operations. In many cases, however, collection and handling of such data is limited to a set of options which may come at high costs. In this study, a low-cost solution integrating offline object tracking, signal processing and artificial intelligence was tested to evaluate its capability to correctly classify in the time domain the events specific to the monitoring of wood sawmilling operations. Discrete scalar signals produced from media files by tracking functionalities of the Kinovea® software (13,000 frames) were used to derive a differential signal, then a filtering-to-the-root procedure was applied to them. Both, the raw and filtered signals were used as inputs in the training of an artificial neural network at two levels of operational detail: fully and essentially documented data. While the addition of the derived signal made sense because it improved the outcomes of classification (recall of 92–97%) filtered signals were found to add less contribution to the classification accuracy. The use of essentially documented data has improved substantially the classification outcomes and it could be an excellent solution in monitoring applications requiring a basic level of detail. The tested system could represent a good and cheap solution to monitor sawmilling facilities aiming to develop our understanding on their technical efficiency.

A new play-back method for R-DAT using 'non-tracking' signal processing

1991 ◽  
Vol 37 (4) ◽  
pp. 814-822 ◽  
Author(s):  
T. Fukami ◽  
S. Fukuda ◽  
K. Maruyama ◽  
H. Ino ◽  
K. Odaka
Keyword(s):  
Signal Processing ◽  
Tracking Signal

Sparse Image and Signal Processing

2009 ◽  
Author(s):  
Jean-Luc Starck ◽  
Fionn Murtagh ◽  
Jalal Fadili
Keyword(s):  
Signal Processing
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