Incipient fault diagnosis in bearings under variable speed conditions using multiresolution analysis and a weighted committee machine

Tra, Viet; Khan, Sheraz Ali; Kim, Jong‐Myon

doi:10.1121/1.4991329

Cited by 24 publications

(24 citation statements)

References 11 publications

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“…In this point, it is necessary to highlight that the literature and the catalogues of bearing manufacturers consider incipient defects to those whose equivalent surface is between 2 and 5 mm 2 . 30 The radial and axial loads were 215 and 200 N, respectively.…”

Section: Experimental Data: Test Benchmentioning

confidence: 99%

Wavelet Packets Transform processing and Genetic Neuro-Fuzzy classification to detect faulty bearings

Hernández

Castejón

García-Prada

et al. 2019

Advances in Mechanical Engineering

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A great investment is made in maintenance of machinery in any industry. A big percentage of this is spent both in workers and in materials in order to prevent potential issues with said devices. In order to avoid unnecessary expenses, this article presents an intelligent method to detect incipient faults. Particularly, this study focuses on bearings due to the fact that they are the mechanical elements that are most likely to break down. In this article, the proposed method is tested with data collected from a quasi-real industrial machine, which allows for the measurement of the behaviour of faulty bearings with incipient defects. In a second phase, the vibrations obtained from healthy and defective pieces are processed with a multiresolution analysis with the purpose of extracting the most interesting characteristics. Particularly, a Wavelet Packets Transform processing is carried out. Finally, these parameters are used as Genetic Neuro-Fuzzy inputs; this way, once it has been trained, it will indicate whether the analyzed mechanical element is faulty or not.

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Section: Experimental Data: Test Benchmentioning

confidence: 99%

Wavelet Packets Transform processing and Genetic Neuro-Fuzzy classification to detect faulty bearings

Hernández

Castejón

García-Prada

et al. 2019

Advances in Mechanical Engineering

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show abstract

“…To validate our scheme, it was necessary to compare our model's classification performance with a model that uses another fault feature set. The rival in this experiment was a fault feature vector with different statistical measures of the time and frequency domain [15]. Table 4 presents the diagnostic performance of the proposed model and the fault diagnosis model with a different feature pool (i.e., different time and frequency features).…”

Section: Efficacy Of the Wavelet-based Featuresmentioning

confidence: 99%

“…We employed a grid search technique to decide the optimal combinations of these parameters in the SVM structure in terms of the classification performance. The grid search algorithm trained OAA MCSVMs with a pair (C, σ) in the cross product of the following two sets and evaluated their performances: C ∈ 2 −5 , 2 −3 , 2 −1 , 2, 2 3 , 2 5 , 2 7 , 2 9 , 2 11 , 2 13 , 2 15 and σ ∈ 2 −2 , 2 −1 , 2, 2 2 , 2 3 , 2 4 , 2 5 , 2 6 , 2 7 . Finally, the pair (C, σ), which helps OAA MCSVMs deliver the highest classification performance, was considered as the best combination of these parameters.…”

Section: Efficacy Of the Wavelet-based Featuresmentioning

confidence: 99%

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Improving the Performance of Storage Tank Fault Diagnosis by Removing Unwanted Components and Utilizing Wavelet-Based Features

Tra

Duong

Sohaib

et al. 2019

Entropy

Self Cite

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This paper proposes a reliable fault diagnosis model for a spherical storage tank. The proposed method first used a blind source separation (BSS) technique to de-noise the input signals so that the signals acquired from a spherical tank under two types of conditions (i.e., normal and crack conditions) were easily distinguishable. BSS split the signals into different sources that provided information about the noise and useful components of the signals. Therefore, an unimpaired signal could be restored from the useful components. From the de-noised signals, wavelet-based fault features, i.e., the relative energy (REWPN) and entropy (EWPN) of a wavelet packet node, were extracted. Finally, these features were used to train one-against-all multiclass support vector machines (OAA MCSVMs), which classified the instances of normal and faulty states of the tank. The efficiency of the proposed fault diagnosis model was examined by visualizing the de-noised signals obtained from the BSS method and its classification performance. The proposed fault diagnostic model was also compared to existing techniques. Experimental results showed that the proposed method outperformed conventional techniques, yielding average classification accuracies of 97.25% and 98.48% for the two datasets used in this study.

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“…State-of-the-art methods for the intelligent maintenance of rotary machines rely on the timely and accurate analysis of condition monitoring signals, such as acoustic emissions (AE) [1][2][3][4] and vibration acceleration signals [5,6]. AE signals are sampled at very high frequencies, typically 1 MHz, to capture ultrasonic sounds released during the initiation and propagation of cracks in machine components.…”

Section: Introductionmentioning

confidence: 99%

A Pipelined FFT Processor Using an Optimal Hybrid Rotation Scheme for Complex Multiplication: Design, FPGA Implementation and Analysis

et al. 2018

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Abstract:The fast Fourier transform (FFT) is the most prevalent algorithm for the spectral analysis of acoustic emission signals acquired at ultra-high sampling rates to monitor the condition of rotary machines. The complexity and cost of the associated hardware limit the use of FFT in real-time applications. In this paper, an efficient hardware architecture for FFT implementation is proposed based on the radix-2 decimation in frequency algorithm (R2DIF) and a feedback pipelined technique (FB) that allows effective sharing of storage between the input and output data at each stage of the FFT process via shift registers. The proposed design uses an optimal hybrid rotation scheme by combining the modified coordinate rotation digital computer (m-CORDIC) algorithm and a binary encoding technique based on canonical signed digit (CSD) for replacing the complex multipliers in FFT. The m-CORDIC algorithm, with an adaptive iterative monitoring process, improves the convergence of computation, whereas the CSD algorithm optimizes the multiplication of constants using a simple shift-add method. Therefore, the proposed design does not require the large memory typically entailed by existing designs to carry out twiddle factor multiplication in large-point FFT implementations, thereby reducing its area on the chip. Moreover, the proposed pipelined FFT processor uses only distributed logic resources and does not require expensive dedicated functional blocks. Experimental results show that the proposed design outperforms existing state-of-the-art approaches in speed by about 49% and in resource utilization by around 51%, while delivering the same accuracy and utilizing less chip area.

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Incipient fault diagnosis in bearings under variable speed conditions using multiresolution analysis and a weighted committee machine

Cited by 24 publications

References 11 publications

Wavelet Packets Transform processing and Genetic Neuro-Fuzzy classification to detect faulty bearings

Wavelet Packets Transform processing and Genetic Neuro-Fuzzy classification to detect faulty bearings

Improving the Performance of Storage Tank Fault Diagnosis by Removing Unwanted Components and Utilizing Wavelet-Based Features

A Pipelined FFT Processor Using an Optimal Hybrid Rotation Scheme for Complex Multiplication: Design, FPGA Implementation and Analysis

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