Journal ArticleDOI
Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods
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TLDR
In this paper, a new scheme for the prediction of a ball bearing's remaining useful life based on self-organizing map (SOM) and back propagation neural network methods is presented.About:
This article is published in Mechanical Systems and Signal Processing.The article was published on 2007-01-01. It has received 502 citations till now. The article focuses on the topics: Ball bearing & Self-organizing map.read more
Citations
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Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications
TL;DR: A comprehensive review of the PHM field is provided, followed by an introduction of a systematic PHM design methodology, 5S methodology, for converting data to prognostics information, to enable rapid customization and integration of PHM systems for diverse applications.
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Machinery health prognostics: A systematic review from data acquisition to RUL prediction
TL;DR: A review on machinery prognostics following its whole program, i.e., from data acquisition to RUL prediction, which provides discussions on current situation, upcoming challenges as well as possible future trends for researchers in this field.
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Rotating machinery prognostics: State of the art, challenges and opportunities
TL;DR: In this article, the authors synthesize and place these individual pieces of information in context, while identifying their merits and weaknesses, and discuss the identified challenges, and in doing so, alerts researchers to opportunities for conducting advanced research in the field.
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Remaining useful life estimation in prognostics using deep convolution neural networks
Xiang Li,Qian Ding,Jian-Qiao Sun +2 more
TL;DR: A new data-driven approach for prognostics using deep convolution neural networks (DCNN) using time window approach is employed for sample preparation in order for better feature extraction by DCNN.
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A recurrent neural network based health indicator for remaining useful life prediction of bearings
TL;DR: A recurrent neural network based health indicator for RUL prediction of bearings with fairly high monotonicity and correlation values is proposed and it is experimentally demonstrated that the proposed RNN-HI is able to achieve better performance than a self organization map based method.
References
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Book
Self-Organizing Maps
TL;DR: The Self-Organising Map (SOM) algorithm was introduced by the author in 1981 as mentioned in this paper, and many applications form one of the major approaches to the contemporary artificial neural networks field, and new technologies have already been based on it.
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Vibration monitoring of rolling element bearings by the high-frequency resonance technique — a review
P.D. McFadden,J D Smith +1 more
TL;DR: In this article, the authors reviewed the use of high-frequency resonance for vibration monitoring of rolling element bearings by the highfrequency resonance technique and showed that the procedures for obtaining the spectrum of the envelope signal are well established, but that there is an incomplete understanding of the factors which control the appearance of this spectrum.
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Artificial neural network based fault diagnostics of rolling element bearings using time-domain features
TL;DR: The proposed procedure requires only a few features extracted from the measured vibration data either directly or with simple preprocessing, leading to faster training requiring far less iterations making the procedure suitable for on-line condition monitoring and diagnostics of machines.
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Bearing fault diagnosis based on wavelet transform and fuzzy inference
Xinsheng Lou,Kenneth A. Loparo +1 more
TL;DR: In this article, a new scheme for the diagnosis of localised defects in ball bearings based on the wavelet transform and neuro-fuzzy classification was proposed. But this scheme was only applied to a single motor-driven experimental system, and the results demonstrate that the method can reliably separate different fault conditions under the presence of load variations.