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JournalISSN: 1070-9622

Shock and Vibration 

Hindawi Publishing Corporation
About: Shock and Vibration is an academic journal published by Hindawi Publishing Corporation. The journal publishes majorly in the area(s): Vibration & Finite element method. It has an ISSN identifier of 1070-9622. It is also open access. Over the lifetime, 5243 publications have been published receiving 46879 citations.


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Journal ArticleDOI
TL;DR: In this paper, a non-iterative frequency-domain parameter estimation method is proposed based on a weighted least-squares approach and uses multiple-input-multiple-output frequency response functions as primary data.
Abstract: Recently, a new non-iterative frequency-domain parameter estimation method was proposed. It is based on a (weighted) least-squares approach and uses multiple-input-multiple-output frequency response functions as primary data. This so-called "PolyMAX" or polyreference least-squares complex frequency-domain method can be implemented in a very similar way as the industry standard polyreference (time-domain) least-squares complex exponential method: in a first step a stabilisation diagram is constructed containing frequency, damping and participation information. Next, the mode shapes are found in a second least- squares step, based on the user selection of stable poles. One of the specific advantages of the technique lies in the very stable identification of the system poles and participation factors as a function of the specified system order, leading to easy-to-interpret stabilisation diagrams. This implies a potential for automating the method and to apply it to "difficult" estimation cases such as high-order and/or highly damped systems with large modal overlap. Some real-life automotive and aerospace case studies are discussed. PolyMAX is compared with classical methods concerning stability, accuracy of the estimated modal parameters and quality of the frequency response function synthesis.

667 citations

Journal ArticleDOI
TL;DR: An implementation of deep learning algorithm convolutional neural network used for fault identification and classification in gearboxes using different combinations of condition patterns based on some basic fault conditions indicates that the proposed approach is highly reliable and applicable in fault diagnosis of industrial reciprocating machinery.
Abstract: Vibration signals of gearbox are sensitive to the existence of the fault. Based on vibration signals, this paper presents an implementation of deep learning algorithm convolutional neural network (CNN) used for fault identification and classification in gearboxes. Different combinations of condition patterns based on some basic fault conditions are considered. 20 test cases with different combinations of condition patterns are used, where each test case includes 12 combinations of different basic condition patterns. Vibration signals are preprocessed using statistical measures from the time domain signal such as standard deviation, skewness, and kurtosis. In the frequency domain, the spectrum obtained with FFT is divided into multiple bands, and the root mean square (RMS) value is calculated for each one so the energy maintains its shape at the spectrum peaks. The achieved accuracy indicates that the proposed approach is highly reliable and applicable in fault diagnosis of industrial reciprocating machinery. Comparing with peer algorithms, the present method exhibits the best performance in the gearbox fault diagnosis.

322 citations

Journal ArticleDOI
TL;DR: The proposed CNN architecture achieves better results with less learnable parameters than similar architectures used for fault detection, including cases with experimental noise.
Abstract: Traditional feature extraction and selection is a labor-intensive process requiring expert knowledge of the relevant features pertinent to the system. This knowledge is sometimes a luxury and could introduce added uncertainty and bias to the results. To address this problem a deep learning enabled featureless methodology is proposed to automatically learn the features of the data. Time-frequency representations of the raw data are used to generate image representations of the raw signal, which are then fed into a deep convolutional neural network (CNN) architecture for classification and fault diagnosis. This methodology was applied to two public data sets of rolling element bearing vibration signals. Three time-frequency analysis methods (short-time Fourier transform, wavelet transform, and Hilbert-Huang transform) were explored for their representation effectiveness. The proposed CNN architecture achieves better results with less learnable parameters than similar architectures used for fault detection, including cases with experimental noise.

303 citations

Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
202381
2022405
2021969
2020613
2019516
2018523