Mechanical Systems and Signal Processing

Contains the raw experimental data (in .mat format) for the case study presented in section 6 of the paper 'On the completeness of interface descriptions and the consistency of blocked forces obtained in-situ'. An additional .m file is included with data descriptions.

These modules are the simulations shown in paper: Redundant localization system for automatic vehicles, published by Elsevier Mechanical Systems and Signal Processing journal. Authors: Jose Angel Flores Granados, Jordi Mongay Batalla (corresponding author) and Cengiz Togay.

The dataset contains raw data and processed data.

The PyFEST application written in Python language allows a simple and accurate frequency estimation of generated or measured signals. Our focus is on analyzing the vibration response of structures to detect cracks in an early state.

The data include the mechanisms in stp format and matlab code as well as calculation results by ANSYS.

First file: matrices used in rotor equations 2(e) for section 4 case study; H_VP is the modal matrix in eq. (4) augmented over 20 points that have z coordinates defined in the 20 by 1 vector z. Second file: matrices used in the FFSMM equations (10(a)) for section 5 case study; the names of the Matlab variables follow the nomenclature of reference [30] (Bin Hassan, Bonello (2017) "A new modal-based approach...", Journal of Sound and Vibration). Third file: matrices used in rotor equations 2(e) for section 6 case study; H_VP is the modal matrix in eq. (4) augmented over 20 points that have z coordinates defined in the 20 by 1 vector z.

Measurements results for an adaptive structures test bench. A scaled building model was subject to different excitations such as earthquakes, chirp signals and noise. Measurements include those of strain gauge sensors, laser Doppler vibrometers and an an optical camera system.

This zipped folder contains the measurment data used for the redaction of the manuscript number MSSP19-711 untitled “Inverse identification of the acoustic pressure inside a U-shaped pipe line based on acceleration measurements”. A "ReadMe" text file provides informations about the various data.

Source code for Example 3.5 from the paper "Determining Lyapunov exponents of non-smooth systems: perturbation vectors approach" (Balcerzak, Stefański, Dąbrowski, Błażejczyk-Okolewska)

(1) The data file contains the MATLAB codes and feature data used to implement the results in "A New Intelligent Fault Identification Method Based on Transfer Locality Preserving Projection for Actual Diagnosis Scenario of Rotating Machinery". (2) Only feature data are given. The original data are provided by the following references: [1] PHM 09 Data Challenge Data. https://www.phmsociety.org/competition/PHM/09/apparatus. [2] CWRU bearing data center. http://csegroups.case.edu/bearingdatacenter/pages/12k-drive-end-bearing-fault-data [3] Eric Bechhoefer, MFPT Bearing Fault Data Sets. http://mfpt.org/fault-data-sets/. (3) The toolbox used in the codes are listed below: [1] libsvm_3.22. https://www.csie.ntu.edu.tw/~cjlin/libsvm/ [2] DeepLearnToolbox-master. https://github.com/rasmusbergpalm/DeepLearnToolbox [3] minFunc_2012. https://www.cs.ubc.ca/~schmidtm/Software/minFunc.html (4) The supported platform should have a Windows system, meanwhile the MATLAB version should be R2017b or later version (R2018a is also tested).