Szczegóły

Tytuł artykułu

A novelty detection approach to monitoring of epicyclic gearbox health

Tytuł czasopisma

Metrology and Measurement Systems

Wolumin

vol. 25

Numer

No 3

Autorzy publikacji

Słowa kluczowe

epicyclic gearbox ; soft computing ; auto-associative neural network ; novelty detection ; vibration signal

Wydział PAN

Nauki Techniczne

Zakres

459–473

Abstrakt

Reliable monitoring for detection of damage in epicyclic gearboxes is a serious concern for all industries

in which these gearboxes operate in a harsh environment and in variable operational conditions. In this

paper, autonomous multidimensional novelty detection algorithms are used to estimate the gearbox’ health

state based on vectors of features calculated from the vibration signal. The authors examine various feature

vectors, various sources of data and many different damage scenarios in order to compare novel detection

algorithms based on three different principles of operation: a distance in the feature space, a probability

distribution, and an ANN (artificial neural network)-based model reconstruction approach. In order to compensate

for non-deterministic results of training of neural networks, which may lead to different network

performance, the ensemble technique is used to combine responses from several networks. The methods are

tested in a series of practical experiments involving implanting a damage in industrial epicyclic gearboxes,

and acquisition of data at variable speed conditions.

Wydawca

Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation

Data

2018.10.01

Typ

Artykuły / Articles

Identyfikator

ISSN 0860-8229

DOI

10.24425/123896

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