Journal ArticleDOI
A Generic Intelligent Bearing Fault Diagnosis System Using Compact Adaptive 1D CNN Classifier
TLDR
Effectiveness and feasibility of the 1D CNN based fault diagnosis method is validated by applying it to two commonly used benchmark real vibration data sets and comparing the results with the other competing intelligent fault diagnosis methods.Abstract:
Timely and accurate bearing fault detection and diagnosis is important for reliable and safe operation of industrial systems. In this study, performance of a generic real-time induction bearing fault diagnosis system employing compact adaptive 1D Convolutional Neural Network (CNN) classifier is extensively studied. In the literature, although many studies have developed highly accurate algorithms for detecting bearing faults, their results have generally been limited to relatively small train/test data sets. As opposed to conventional intelligent fault diagnosis systems that usually encapsulate feature extraction, feature selection and classification as distinct blocks, the proposed system takes directly raw time-series sensor data as input and it can efficiently learn optimal features with the proper training. The main advantages of the 1D CNN based approach are 1) its compact architecture configuration (rather than the complex deep architectures) which performs only 1D convolutions making it suitable for real-time fault detection and monitoring, 2) its cost effective and practical real-time hardware implementation, 3) its ability to work without any pre-determined transformation (such as FFT or DWT), hand-crafted feature extraction and feature selection, and 4) its capability to provide efficient training of the classifier with limited size of training data set and limited number of BP iterations. Effectiveness and feasibility of the 1D CNN based fault diagnosis method is validated by applying it to two commonly used benchmark real vibration data sets and comparing the results with the other competing intelligent fault diagnosis methods.read more
Citations
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Journal ArticleDOI
Applications of machine learning to machine fault diagnosis: A review and roadmap
TL;DR: A review and roadmap to systematically cover the development of IFD following the progress of machine learning theories and offer a future perspective is presented.
Journal ArticleDOI
1D convolutional neural networks and applications: A survey
TL;DR: This paper presents a comprehensive review of the general architecture and principals of 1D CNNs along with their major engineering applications, especially focused on the recent progress in this field.
Posted Content
1D Convolutional Neural Networks and Applications: A Survey
TL;DR: A comprehensive review of the general architecture and principals of 1D CNNs along with their major engineering applications, especially focused on the recent progress in this field, is presented in this paper, where the benchmark datasets and the principal 1D convolutional neural network software used in those applications are also publically shared in a dedicated website.
Journal ArticleDOI
A comprehensive review on convolutional neural network in machine fault diagnosis
TL;DR: This work attempts to review and summarize the development of the Convolutional Network based Fault Diagnosis (CNFD) approaches comprehensively, and points out the characteristics of current development, facing challenges and future trends.
Journal ArticleDOI
A Survey on Wearable Technology: History, State-of-the-Art and Current Challenges
Aleksandr Ometov,Viktoriia Shubina,Lucie Klus,Justyna Skibinska,Salwa Saafi,Pavel Pascacio,Laura Flueratoru,Darwin Quezada Gaibor,Nadezhda Chukhno,Olga Chukhno,Asad Ali,Asma Channa,Ekaterina Svertoka,Ekaterina Svertoka,Waleed Bin Qaim,Raúl Casanova-Marqués,Raúl Casanova-Marqués,Sylvia Holcer,Sylvia Holcer,Joaquín Torres-Sospedra,Sven Casteleyn,Giuseppe Ruggeri,Giuseppe Araniti,Radim Burget,Jiri Hosek,Elena Simona Lohan +25 more
TL;DR: An extensive and diverse classification of wearables, based on various factors, a discussion on wireless communication technologies, architectures, data processing aspects, and market status, as well as a variety of other actual information on wearable technology are provided.
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