Journal ArticleDOI
A feature extraction and visualization method for fault detection of marine diesel engines
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TLDR
In this article, an automatic vibration-source extraction and feature visualization method is proposed for fault detection of marine diesel engines, in which the Stockwell transform was used to construct a time-frequency reference signal to guide the separation process of the kernel ICA.About:
This article is published in Measurement.The article was published on 2018-02-01. It has received 57 citations till now. The article focuses on the topics: Fault detection and isolation & Feature extraction.read more
Citations
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Journal ArticleDOI
Detection of Deterioration of Three-phase Induction Motor using Vibration Signals
Adam Glowacz,W. Głowacz,J. Kozik,Krzysztof Piech,Miroslav Gutten,Wahyu Caesarendra,Liu Hui,Frantisek Brumercik,Muhammad Irfan,Z. Faizal Khan +9 more
TL;DR: Rotor fault diagnostic techniques of a three-phase induction motor (TPIM) were presented and a method of the feature extraction of vibration signals Method of Selection of Amplitudes of Frequencies – MSAF-12 was described.
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DTCNNMI: A deep twin convolutional neural networks with multi-domain inputs for strongly noisy diesel engine misfire detection
Chengjin Qin,Yanrui Jin,Jianfeng Tao,Dengyu Xiao,Honggan Yu,Chao Liu,Gang Shi,Lei Junbo,Chengliang Liu +8 more
TL;DR: A deep twin convolutional neural networks with large first-layer kernels for extracting multi-domain information of vibration signals and resist influence of environmental noise and the change of operating conditions on the final diagnosis results is presented.
Journal ArticleDOI
Combustion machine learning: Principles, progress and prospects
TL;DR: A review of data sources, data-driven techniques, and concepts for combustion machine learning can be found in this article , focusing on interpretability, uncertainty quantification, robustness, consistency, creation and curation of benchmark data, and the augmentation of ML methods with prior combustion domain knowledge.
Journal ArticleDOI
A Data-Driven-Based Fault Diagnosis Approach for Electrical Power DC-DC Inverter by Using Modified Convolutional Neural Network With Global Average Pooling and 2-D Feature Image
TL;DR: A novel convolutional neural network namely the modified CNN-GAP model is proposed for fast fault diagnosis of the DC-DC inverter and could greatly reduce the model parameter quantity of the traditional CNN more than 80%, which is more suitable for rapid fault diagnosis in electronic devices.
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Decision support system for safety improvement: An approach using multiple correspondence analysis, t-SNE algorithm and K-means clustering
Krantiraditya Dhalmahapatra,Rohan Shingade,Harshawardhan Mahajan,Abhishek Verma,Jhareswar Maiti +4 more
TL;DR: The DSS is applied to analysing near miss incidents occurred in electric overhead traveling crane operations in a steel plant and provides a logical approach of dimension reduction in a form called ‘funnel diagram’.
References
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Visualizing Data using t-SNE
TL;DR: A new technique called t-SNE that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map, a variation of Stochastic Neighbor Embedding that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map.
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Nonlinear dimensionality reduction by locally linear embedding.
Sam T. Roweis,Lawrence K. Saul +1 more
TL;DR: Locally linear embedding (LLE) is introduced, an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs that learns the global structure of nonlinear manifolds.
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Extreme learning machine: Theory and applications
TL;DR: A new learning algorithm called ELM is proposed for feedforward neural networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs which tends to provide good generalization performance at extremely fast learning speed.
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Laplacian Eigenmaps for dimensionality reduction and data representation
Mikhail Belkin,Partha Niyogi +1 more
TL;DR: In this article, the authors proposed a geometrically motivated algorithm for representing high-dimensional data, based on the correspondence between the graph Laplacian, the Laplace Beltrami operator on the manifold and the connections to the heat equation.
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Localization of the complex spectrum: the S transform
TL;DR: The S transform is shown to have some desirable characteristics that are absent in the continuous wavelet transform, and provides frequency-dependent resolution while maintaining a direct relationship with the Fourier spectrum.