Response to ?comment on ?quantum formalism via signal analysis??

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

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Example of linear vs. non-linear approaches to EEG-signal analysis - assessment of influence of EMF generated by cellular phones on human brain

Frontiers in Neuroscience ◽

10.3389/conf.fnins.2010.05.00025 ◽

1900 ◽

Vol 4 ◽

Author(s):

Klonowski W

Keyword(s):

Human Brain ◽

Signal Analysis ◽

Cellular Phones ◽

Eeg Signal ◽

Non Linear ◽

Eeg Signal Analysis

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Diagnostic System of Gravitational Solid Flow Based on Weight and Accelerometer Signal Analysis Using Wireless Data Transmission Technology

Image Processing & Communications ◽

10.2478/v10248-012-0061-8 ◽

2012 ◽

Vol 17 (4) ◽

pp. 319-326 ◽

Cited By ~ 1

Author(s):

Zbigniew Chaniecki ◽

Krzysztof Grudzień ◽

Tomasz Jaworski ◽

Grzegorz Rybak ◽

Andrzej Romanowski ◽

...

Keyword(s):

Signal Analysis ◽

Scale Up ◽

Three Dimensional ◽

Diagnostic System ◽

Wireless Data ◽

Stick Slip ◽

Wireless Data Transmission ◽

Accelerometer Signal ◽

Flow Investigation ◽

Material Information

Abstract The paper presents results of the scale-up silo flow investigation in based on accelerometer signal analysis and Wi-Fi transmission, performed in distributed laboratory environment. Prepared, by the authors, a set of 8 accelerometers allows to measure a three-dimensional acceleration vector. The accelerometers were located outside silo, on its perimeter. The accelerometers signal changes allowed to analyze dynamic behavior of solid (vibrations/pulsations) at silo wall during discharging process. These dynamic effects are caused by stick-slip friction between the wall and the granular material. Information about the material pulsations and vibrations is crucial for monitoring the interaction between silo construction and particle during flow. Additionally such spatial position of accelerometers sensor allowed to collect information about nonsymmetrical flow inside silo.

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Acoustic emission signal analysis for crane slewing bearing with no defect under different conditions

Industrial Electronics and Engineering ◽

10.2495/iciee140661 ◽

2014 ◽

Author(s):

Yang Jiao ◽

Shang Meng ◽

Guanghai Li

Keyword(s):

Acoustic Emission ◽

Acoustic Emission Signal ◽

Signal Analysis ◽

Slewing Bearing ◽

Emission Signal

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Withdrawal: Radiation and Signal Analysis of the Falcon Solid-state Energetic Electron Detector (FalconSEED)

2018 AIAA Aerospace Sciences Meeting ◽

10.2514/6.2018-0934.c1 ◽

2018 ◽

Author(s):

Carlos A. Maldonado

Keyword(s):

Solid State ◽

Signal Analysis ◽

Energetic Electron ◽

Electron Detector

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Extending Acoustic Microscopy for Comprehensive Failure Analysis Applications

ISTFA 2010: Conference Proceedings from the 36th International Symposium for Testing and Failure Analysis ◽

10.31399/asm.cp.istfa2010p0084 ◽

2010 ◽

Author(s):

Sebastian Brand ◽

Matthias Petzold ◽

Peter Czurratis ◽

Peter Hoffrogge

Keyword(s):

Image Processing ◽

Failure Analysis ◽

Signal Analysis ◽

Flip Chip ◽

Fault Isolation ◽

Acoustic Microscopy ◽

Specific Information ◽

Full Potential ◽

Time Frequency ◽

Non Destructive

Abstract In industrial manufacturing of microelectronic components, non-destructive failure analysis methods are required for either quality control or for providing a rapid fault isolation and defect localization prior to detailed investigations requiring target preparation. Scanning acoustic microscopy (SAM) is a powerful tool enabling the inspection of internal structures in optically opaque materials non-destructively. In addition, depth specific information can be employed for two- and three-dimensional internal imaging without the need of time consuming tomographic scan procedures. The resolution achievable by acoustic microscopy is depending on parameters of both the test equipment and the sample under investigation. However, if applying acoustic microscopy for pure intensity imaging most of its potential remains unused. The aim of the current work was the development of a comprehensive analysis toolbox for extending the application of SAM by employing its full potential. Thus, typical case examples representing different fields of application were considered ranging from high density interconnect flip-chip devices over wafer-bonded components to solder tape connectors of a photovoltaic (PV) solar panel. The progress achieved during this work can be split into three categories: Signal Analysis and Parametric Imaging (SA-PI), Signal Analysis and Defect Evaluation (SA-DE) and Image Processing and Resolution Enhancement (IP-RE). Data acquisition was performed using a commercially available scanning acoustic microscope equipped with several ultrasonic transducers covering the frequency range from 15 MHz to 175 MHz. The acoustic data recorded were subjected to sophisticated algorithms operating in time-, frequency- and spatial domain for performing signal- and image analysis. In all three of the presented applications acoustic microscopy combined with signal- and image processing algorithms proved to be a powerful tool for non-destructive inspection.

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