RESONANCE 2023

Session

12 juil. 2023 à 10:40

Toulouse

Présidents de session

Survishno 8 / Fault Diagnostics and prognostics 2

• Renata Klein ()

Liste des contributions Ordre du jour

228. "FDP2#1 - Impact of low ring waviness orders on Hybrid ball bearing under high speeds"

Berrada Salma

12/07/2023 10:40

Deep groove ball bearings (DGBBs) have a crucial role in various rotating equipment applications, particularly in high-performance systems such as electric vehicle Powerpacks, where noise and vibration levels are of utmost importance. The roundness and surface shapes of the ring races significantly influence the performance and service life of DGBBs. But the fact remains that in the bearing...

229. "FDP2#2 - Orthogonal nonnegative matrix factorization as informative frequency band selector"

Gabor Mateusz

12/07/2023 11:00

One of the most common representations of acquired vibration signals from a faulty machine is the time-frequency representation in the form of a spectrogram matrix. Because the magnitude part of the spectrogram matrix consists only of non-negative elements, it can be decomposed using non-negative matrix factorization (NMF) into a base matrix and weight matrix, which represent the frequency and...

230. "FDP2#3 - Noise robust gearbox defect diagnosis"

Sekko Edgard

12/07/2023 11:20

"Vibratory analysis focused on the detection of periodically impulsive signatures caused by tooth crack of a gearbox in early stage is a great interest in industrial applications. This problem has received great attention in the last decades. A wide variety of model-based approaches requiring advanced methods for processing and analyzing the vibration signal for early fault detection have been...

231. "FDP2#4 - Modeling and identifying non-stationary long-term historical condition monitoring data in the presence of noise with non-Gaussian characteristics"

Shiri Hamid

12/07/2023 11:40

"The advancement of condition monitoring systems has led to an increase in the utilization of long-term monitoring data for diagnostics and prognostics. The effective utilization of such data, collected over extended periods ranging from months to years, is a critical factor in enabling accurate diagnosis and prognosis.
However, most of these industrial applications operate under time-varying...

232. "FDP2#5 - Robust and automatic diagnosis of rotating machine faults by long-term spectral analysis"

Andre Hugo

12/07/2023 12:00

"Although it is now recognised that the most effective methods for diagnosing rotating machines are based on the analysis of the cyclo-stationary content of the vibration signal, they can nevertheless be classified into two main families. The first group includes temporal indicators combining statistical moments of the signals, possibly filtered around the orders of interest. These methods,...

328. FDP2#6 -

12/07/2023 12:20