The total energy and the total entropy of force signals — new parameters for monitoring oblique turning operations

Chungchoo, Chatchapol; Saini, Devi P

doi:10.1016/s0890-6955(00)00032-8

“…The data for each 4 s period were analysed to yield particular features, in both the time and frequency domains, using statistically-based signal processing techniques [14][15][16]. In the time domain the variance of the signal, i.e.…”

Section: Analysis Of the Sensor Signalsmentioning

confidence: 99%

“…The features of the signals, which were extracted in the frequency domain, also included the 'probability' or proportion of the power density occurring in particular frequency ranges. For the IR signal the fre- In addition the entropy of the PSD [16], frequency centroid (the average shift of the PSD over the whole frequency range) and the shape factor (the ratio of the standard deviation to the mean of the PSD over the whole frequency range) were also found to be appropriate. The IR signal was therefore represented by a total of 13 features and the microphone signal by 15 features.…”

Section: Analysis Of the Sensor Signalsmentioning

confidence: 99%

Efficient Low-Driving-Voltage Blue Phosphorescent Homojunction Organic Light-Emitting Devices

Cai¹,

Su

²

,

Chiba³

et al. 2011

Jpn. J. Appl. Phys.

View full text Add to dashboard Cite

This paper is concerned with the development of a system to detect and monitor slag growth in the near burner region in a pulverized-fuel (pf) fired combustion rig. These slag deposits are commonly known as 'eyebrows' and can markedly affect the stability of the burner. The study thus involved a series of experiments with two different coals over a range of burner conditions using a 150 kW pf burner fitted with simulated eyebrows. These simulated eyebrows consisted of annular refractory inserts mounted immediately in front of the original burner quarl. Data obtained by monitoring the infra-red radiation and sound emitted by the flame were processed to yield time and frequency-domain features, which were then used to train and test a hybrid neural network. This hybrid 'intelligent' system was based on self organizing map and radial-basis-function neural networks. This system was able to classify different sized eyebrows with a success rate of at least 99.5%. Consequently, it is possible not only to detect the presence of an eyebrow by monitoring the flame, but also the network can provide an estimate of the size of the deposit, over a reasonably large range of conditions.

show abstract

“…The data for both sensors were analyzed for each 4 s period to yield particular 'features', in the time and frequency domains, using statistically-based, signal processing techniques [8][9][10]. In the time domain, these included the variance which represents the a.c. (alternating-current) power of the signal and the skewness and kurtosis which measures asymmetry about the mean and 'flatness' of the distribution, respectively, as well as the number of zero crossings which characterizes the time structure of the signal, and is a measure of how noisy the signal is.…”

Section: Feature Extractionmentioning

confidence: 99%

An intelligent monitoring system for the detection of slag deposition on a pulverized coal fired burner

Tan

¹

,

Wilcox

²

,

Ward

³

et al. 2005

Proceedings of the Institution of Mechanical Engineers, Part A:

View full text Add to dashboard Cite

The objective of this paper is to describe the further development of a monitoring system to detect the presence of so-called burner eyebrows, i.e. relatively large deposits of slag around the burner quarl in pulverized coal fired boilers. Experiments were undertaken with a range of coals and with various artificial eyebrows constructed from cast refractory inserts. The system uses a microphone to detect combustion noise and an infrared sensor which measures flame radiation, and the signals from these cheap, easily installed sensors were analyzed by a hybrid neural network. In tests with two coals, the system was able to distinguish the different eyebrows with a high degree of accuracy if representative data were used to train the network for each particular coal. In further tests with a range of six different coals, the system was able to distinguish between a clean burner and one fitted with a particular sized eyebrow. In this case, it proved to be possible to use only the features from three of the coals in the training process and the data from the remaining fuels for validation. The monitoring system, therefore, appears to be relatively independent of changes to the coal fired by the burner if trained with a representative range of coals. Finally, this paper presents a possible method to detect burner eyebrows via the evaluation of so-called ‘eyebrow indices’ using a self-organizing map which is trained solely using clean burner sensor patterns.

show abstract

“…For the IR signal the fre- ) were employed. In addition the entropy of the PSD [16], frequency centroid (the average shift of the PSD over the whole frequency range) and the shape factor (the ratio of the standard deviation to the mean of the PSD over the whole frequency range) were also found to be appropriate. The IR signal was therefore represented by a total of 13 features and the microphone signal by 15 features.…”

Section: Analysis Of the Sensor Signalsmentioning

confidence: 99%

Monitoring near burner slag deposition with a hybrid neural network system

Tan

¹

,

Wilcox

²

,

Ward

³

et al. 2003

Meas. Sci. Technol.

View full text Add to dashboard Cite

This paper is concerned with the development of a system to detect and monitor slag growth in the near burner region in a pulverized-fuel (pf) fired combustion rig. These slag deposits are commonly known as 'eyebrows' and can markedly affect the stability of the burner. The study thus involved a series of experiments with two different coals over a range of burner conditions using a 150 kW pf burner fitted with simulated eyebrows. These simulated eyebrows consisted of annular refractory inserts mounted immediately in front of the original burner quarl. Data obtained by monitoring the infra-red radiation and sound emitted by the flame were processed to yield time and frequency-domain features, which were then used to train and test a hybrid neural network. This hybrid 'intelligent' system was based on self organizing map and radial-basis-function neural networks. This system was able to classify different sized eyebrows with a success rate of at least 99.5%. Consequently, it is possible not only to detect the presence of an eyebrow by monitoring the flame, but also the network can provide an estimate of the size of the deposit, over a reasonably large range of conditions.

show abstract

The total energy and the total entropy of force signals — new parameters for monitoring oblique turning operations

Cited by 8 publications

References 29 publications

Efficient Low-Driving-Voltage Blue Phosphorescent Homojunction Organic Light-Emitting Devices

Efficient Low-Driving-Voltage Blue Phosphorescent Homojunction Organic Light-Emitting Devices

An intelligent monitoring system for the detection of slag deposition on a pulverized coal fired burner

Monitoring near burner slag deposition with a hybrid neural network system

Contact Info

Product

Resources

About