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

Kód výsledku v 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>

Výsledek na webu

<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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Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

R - Software

CEP obor

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OECD FORD obor

20204 - Robotics and automatic control

Projekt

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

Návaznosti

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

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Interní identifikační kód produktu

ANN dataset generator 1.0

Technické parametry

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)

Ekonomické parametry

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

IČO vlastníka výsledku

00216305

Název vlastníka

Vysoké učení technické v Brně