Author
D. Abboud
Bio: D. Abboud is an academic researcher from Institut national des sciences Appliquées de Lyon. The author has contributed to research in topics: Envelope (motion) & Cyclostationary process. The author has an hindex of 3, co-authored 3 publications receiving 138 citations.
Papers
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TL;DR: A model of rotating machine signals is introduced which sheds light on the various components to be expected in the squared envelope spectrum, and a critical comparison is made of three sophisticated methods, namely, the improved synchronous average, the cepstrum prewhitening, and the generalized synchronousaverage, used for suppressing the deterministic part.
125 citations
TL;DR: A new estimator of the OFSC based on the cyclic modulation spectrum (CMS) is proposed and compared with the ACP in terms of resolution, statistical performance and computational cost and the optimality of the “order-frequency spectral coherence” in revealing cyclic components according to their SNR is demonstrated.
62 citations
01 Jun 2016
5 citations
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TL;DR: A systemic and pertinent state-of-art review on WT planetary gearbox condition monitoring techniques on the topics of fundamental analysis, signal processing, feature extraction, and fault detection is provided.
312 citations
TL;DR: In this paper, a reweighted singular value decomposition (RSVD) strategy is proposed for signal denoising and weak feature enhancement in a two-stage gearbox as well as train bearings.
219 citations
TL;DR: In this paper, a matching synchrosqueezing transform (MSST) was proposed to improve the readability of the TF representation of nonstationary signals composed of multiple components with slow varying instantaneous frequency (IF).
141 citations
TL;DR: A two-layered bearing fault diagnosis scheme is proposed for the identification of fault pattern and crack size for a given fault type and demonstrates that it outperforms other state-of-the-art algorithms, i.e., support vector machines (SVMs) and backpropagation neural networks (BPNNs).
Abstract: Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure of rotary machines. Though widely investigated in the past couple of decades, continued advancement is still desirable to improve upon existing fault diagnosis techniques. Vibration acceleration signals collected from machine bearings exhibit nonstationary behavior due to variable working conditions and multiple fault severities. In the current work, a two-layered bearing fault diagnosis scheme is proposed for the identification of fault pattern and crack size for a given fault type. A hybrid feature pool is used in combination with sparse stacked autoencoder (SAE)-based deep neural networks (DNNs) to perform effective diagnosis of bearing faults of multiple severities. The hybrid feature pool can extract more discriminating information from the raw vibration signals, to overcome the nonstationary behavior of the signals caused by multiple crack sizes. More discriminating information helps the subsequent classifier to effectively classify data into the respective classes. The results indicate that the proposed scheme provides satisfactory performance in diagnosing bearing defects of multiple severities. Moreover, the results also demonstrate that the proposed model outperforms other state-of-the-art algorithms, i.e., support vector machines (SVMs) and backpropagation neural networks (BPNNs).
139 citations
TL;DR: Wang et al. as discussed by the authors proposed an underdamped multistable stochastic resonance (SR) method with stable-state matching for bearing fault diagnosis, which is able to suppress the multiscale noise.
130 citations