Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification
01 Jan 2017-Signal Processing (Elsevier North-Holland, Inc.PUB555New York, NY, USA)-Vol. 130, Iss: 130, pp 377-388
TL;DR: An effective and reliable deep learning method known as stacked denoising autoencoder (SDA), which is shown to be suitable for certain health state identifications for signals containing ambient noise and working condition fluctuations, is investigated.
About: This article is published in Signal Processing.The article was published on 2017-01-01. It has received 591 citations till now. The article focuses on the topics: Robustness (computer science) & Deep learning.
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TL;DR: The applications of deep learning in machine health monitoring systems are reviewed mainly from the following aspects: Auto-encoder and its variants, Restricted Boltzmann Machines, Convolutional Neural Networks, and Recurrent Neural Networks.
1,569 citations
TL;DR: A new CNN based on LeNet-5 is proposed for fault diagnosis which can extract the features of the converted 2-D images and eliminate the effect of handcrafted features and has achieved significant improvements.
Abstract: Fault diagnosis is vital in manufacturing system, since early detections on the emerging problem can save invaluable time and cost. With the development of smart manufacturing, the data-driven fault diagnosis becomes a hot topic. However, the traditional data-driven fault diagnosis methods rely on the features extracted by experts. The feature extraction process is an exhausted work and greatly impacts the final result. Deep learning (DL) provides an effective way to extract the features of raw data automatically. Convolutional neural network (CNN) is an effective DL method. In this study, a new CNN based on LeNet-5 is proposed for fault diagnosis. Through a conversion method converting signals into two-dimensional (2-D) images, the proposed method can extract the features of the converted 2-D images and eliminate the effect of handcrafted features. The proposed method which is tested on three famous datasets, including motor bearing dataset, self-priming centrifugal pump dataset, and axial piston hydraulic pump dataset, has achieved prediction accuracy of 99.79%, 99.481%, and 100%, respectively. The results have been compared with other DL and traditional methods, including adaptive deep CNN, sparse filter, deep belief network, and support vector machine. The comparisons show that the proposed CNN-based data-driven fault diagnosis method has achieved significant improvements.
1,240 citations
Additional excerpts
...ing auto-encoder [14], [15], and sparse filtering [16]....
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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.
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TL;DR: A comprehensive survey of commonly used deep learning algorithms and discusses their applications toward making manufacturing “smart”, including computational methods based on deep learning that aim to improve system performance in manufacturing.
1,025 citations
TL;DR: An end-to-end method that takes raw temporal signals as inputs and thus doesn’t need any time consuming denoising preprocessing and can achieve high accuracy when working load is changed is proposed.
805 citations
References
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TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
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