Dataset generators for training and validation of classification models for fault diagnosis based on artificial neural networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F21%3APR34494" target="_blank" >RIV/00216305:26620/21:PR34494 - isvavai.cz</a>

Result on the web

<a href="https://ai4di.ceitec.cz/vysledky/ann_dataset_generator" target="_blank" >https://ai4di.ceitec.cz/vysledky/ann_dataset_generator</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Dataset generators for training and validation of classification models for fault diagnosis based on artificial neural networks

Original language description

Software generators of the data for training and validation of the neural networks for vibrodiagnostics of rotating machines comprise three separate solutions for generating training and validation datasets. The first generator is created in the MATLAB environment and generates vibration acceleration signals, which consist of a defined number of harmonic components with random frequency, amplitude and random amount of additive noise. Software generates two classes of signals with different bandwidth. The first class is limited by a 1st order filter with a cutoff frequency of 1 kHz, the second with a cutoff frequency of 11 kHz. Generated signals are exported to a * .CSV file and can be further used for training and validation of the neural network. The second generator is implemented in the LabVIEW environment and uses the * .CSV output from the first generator and transforms a short section of the generated signal into an infinite length signal. This is then sent to a vibration exciter, on which a smart diagnostic sensor is located, which senses mechanical signals with two accelerometers. The generator communicates with the sensor via the digital bus and stores the data captured by sensors with different bandwidths. This real data reflecting actual mechanical properties of the sensing system is then used as the input data for neural network training and validation. The third generator is also implemented in the LabVIEW environment and generates a clearly user-defined frequency spectrum with additive noise and a given bandwidth using a low-pass 1st order filter with a user-defined cutoff frequency. The generator output is *. CSV file, which can be imported e.g. into the Python environment for training and validation of the neural network.

Czech name

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Czech description

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Type

R - Software

CEP classification

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OECD FORD branch

20204 - Robotics and automatic control

Project

<a href="/en/project/8A19001" target="_blank" >8A19001: Artificial Intelligence for Digitizing Industry</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Internal product ID

ANN dataset generator 1.0

Technical parameters

Formát výstupních dat: *.CSV soubor Frekvenční rozsah generovaného signálu: 10 Hz – 11 kHz Amplitudový rozsah generovaného signálu: normovaný (0 … 1) Počet vzorků jednoho signálu: 4000 (1. generátor) / 2048 (2. generátor) / 10k (3. generátor) Počet generovaných signálů: 2000 (1. generátor) / 4000 (2. generátor) / 2000 (3. generátor)

Economical parameters

Softwarové generátory datových sad pro učení a ověřování klasifikačních modelů jsou prozatím využívány výhradně pro další výzkum a vývoj, komerční využití se zatím nepředpokládá.

Owner IČO

00216305

Owner name

Vysoké učení technické v Brně