Fault diagnosis of rotating machinery with a novel statistical feature extraction and evaluation method

Li, Wenyuan; Zhu, Zhencai; Jiang, Fan; Zhou, Gongbo; Chen, Guoan

doi:10.1016/j.ymssp.2014.05.034

Cited by 110 publications

(57 citation statements)

References 26 publications

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“…These proposed statistical-time features and their corresponding equations are listed in Table 1. Moreover, due to the potentiality to analyze trends of signals and the high-performance source of information, advantageous and accurate results have been successfully obtained by including the proposed set of statistical-time features in condition monitoring schemes for fault identification in electromechanical systems [7], [21], [24].…”

Section: Diagnosis Methodologymentioning

confidence: 99%

“…As aforementioned, in the proposed work, the acquired vibrations signals belong to those vibrations in the perpendicular plane of the gearbox rotating axis since some studies has reported that the occurrence of perpendicular vibrations on the rotating axis is related to the inappropriate working conditions of rotational machines [5], [13], [24]. Regarding the proposed methodology, the data acquisition is performed by carrying out different experiments at different operating frequencies for driving the induction motor: 5 Hz, 15 Hz and 50 Hz.…”

Section: Validation Of the Methodsmentioning

confidence: 99%

“…For that reason, different strategies or procedures related to feature reduction have been considered in condition monitoring schemes. In this regard, independently of whether condition monitoring schemes are used to assess electrical or mechanical machines, the main included feature reduction strategies are feature selection and feature extraction [24]- [26].…”

Section: Feature Reduction Approachesmentioning

confidence: 99%

“…The vibration signals can be characterized extending their analysis into frequency and time-frequency domains [16], [22]- [23]. Even though the most well-known frequency and time-frequency domain techniques such as fast Fourier transform, Wavelet analysis or HilbertHuang transform among others, have been satisfactory applied to condition monitoring schemes, the simplicity and low computational cost of statistical time-domain features represents a suitable characterization solution, mainly, for considering its capability to estimate general trends from signals [24].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Diagnosis methodology for identifying gearbox wear based on statistical time feature reduction

Saucedo-Dorantes

Delgado-Prieto

Osornio-Ríos

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part C:

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Strategies for condition monitoring are relevant to improve the operation safety and to ensure the efficiency of all the equipment used in industrial applications. The feature selection and feature extraction are suitable processing stages considered in many condition monitoring schemes to obtain high performance. Aiming to address this issue, this work proposes a new diagnosis methodology based on a multi-stage feature reduction approach for identifying different levels of uniform wear in a gearbox. The proposed multi-stage feature reduction approach involves a feature selection and a feature extraction ensuring the proper application of a high-performance signal processing over a set of acquired measurements of vibration. The methodology is performed successively; first, the acquired vibration signals are characterized by calculating a set of statistical time-based features. Second, a feature selection is done by performing an analysis of the Fisher score. Third, a feature extraction is realized by means of the Linear Discriminant Analysis technique. Finally, fourth, the diagnosis of the considered faults is done by means of a Fuzzy-based classifier. The effectiveness and performance of the proposed diagnosis methodology is evaluated by considering a complete dataset of experimental test, making the proposed methodology suitable to be applied in industrial applications with power transmission systems.

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Section: Diagnosis Methodologymentioning

confidence: 99%

Section: Validation Of the Methodsmentioning

confidence: 99%

Section: Feature Reduction Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Diagnosis methodology for identifying gearbox wear based on statistical time feature reduction

Saucedo-Dorantes

Delgado-Prieto

Osornio-Ríos

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part C:

View full text Add to dashboard Cite

show abstract

“…The data processing method simplifies the computational expense and benefits the improvement of the generation performance. Some typical feature extraction methods, such as wavelet packet transform (WPT) [7][8][9][10], empirical mode decomposition (EMD) [11], time-domain statistical features (TDSF) [12,13] and independent component analysis (ICA) [14][15][16][17] have been proved to be equivalent to a large-scale matrix factorization problem (i.e., there may be still some irrelevant or redundant noise in the extracted features) [18]. In order to resolve this problem, a feature selection method could be employed to wipe off irrelevant and redundant information so that the dimension of extracted feature is reduced.…”

Section: Introductionmentioning

confidence: 99%

Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach

2016

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Reliable and quick response fault diagnosis is crucial for the wind turbine generator system (WTGS) to avoid unplanned interruption and to reduce the maintenance cost. However, the conditional data generated from WTGS operating in a tough environment is always dynamical and high-dimensional. To address these challenges, we propose a new fault diagnosis scheme which is composed of multiple extreme learning machines (ELM) in a hierarchical structure, where a forwarding list of ELM layers is concatenated and each of them is processed independently for its corresponding role. The framework enables both representational feature learning and fault classification. The multi-layered ELM based representational learning covers functions including data preprocessing, feature extraction and dimension reduction. An ELM based autoencoder is trained to generate a hidden layer output weight matrix, which is then used to transform the input dataset into a new feature representation. Compared with the traditional feature extraction methods which may empirically wipe off some "insignificant' feature information that in fact conveys certain undiscovered important knowledge, the introduced representational learning method could overcome the loss of information content. The computed output weight matrix projects the high dimensional input vector into a compressed and orthogonally weighted distribution. The last single layer of ELM is applied for fault classification. Unlike the greedy layer wise learning method adopted in back propagation based deep learning (DL), the proposed framework does not need iterative fine-tuning of parameters. To evaluate its experimental performance, comparison tests are carried out on a wind turbine generator simulator. The results show that the proposed diagnostic framework achieves the best performance among the compared approaches in terms of accuracy and efficiency in multiple faults detection of wind turbines.

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Spectral Analysis of Nonlinear Vibration Effects Produced by Worn Gears and Damaged Bearing in Electromechanical Systems: A Condition Monitoring Approach

Saucedo-Dorantes

Delgado-Prieto

Osornio-Ríos

et al. 2019

Nonlinear Structural Dynamics and Damping

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Fault diagnosis of rotating machinery with a novel statistical feature extraction and evaluation method

Cited by 110 publications

References 26 publications

Diagnosis methodology for identifying gearbox wear based on statistical time feature reduction

Diagnosis methodology for identifying gearbox wear based on statistical time feature reduction

Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach

Spectral Analysis of Nonlinear Vibration Effects Produced by Worn Gears and Damaged Bearing in Electromechanical Systems: A Condition Monitoring Approach

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