Classification of a cracked-rotor system during start-up using Deep learning based on convolutional neural networks

Rezazadeh, Nima; Ashory, Mohammad-Reza; Fallahy, Shila

doi:10.21595/marc.2021.22030

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…If all the requirements met by an 𝑡 * are removed, it is added to 𝑇 . Enter the second step if the second coverage criterion is not fully covered [27][28]. Conversely, the last step continues until the test requirements in the first coverage criterion are completely covered.…”

Section: Fig 3 Algorithm Flow Of Adding Redundant Test Case Setmentioning

confidence: 99%

Test case simplification based on coupling metrics in software bug location

Hu¹

2023

J. meas. eng.

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Software test cases are one of the most critical aspects of software testing in the product development process. As software products are updated several times, the same test requirement may be covered by multiple test cases, so this aspect is often redundant, yet the approximate test case set has an impact on its error detection rate. This study proposes the idea of using redundant test cases in software error location, introduces a coupling metric, analyses its program slicing and establishes a second coverage criterion in order to balance the relationship between the reduced test suite and the false detection rate the test case set. The results show that the size of test set and the number of error detection by the Ruby On Rails (ROR) method used in this study are larger than those of other commonly used reduction algorithms. The test suite has the lowest error detection loss rate, with an average of 17.96 % across the six test case sets. The highest error detection capability of individual test cases was found in the reduced test set, with a mean value of 90.63 % in the test set. The method also has the highest average reduction efficiency of 91.05 %. Compared with other simplification methods, the research method has a better balance between the size and false detection rate of the reduced test suite and the advantages of simplification.

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Section: Fig 3 Algorithm Flow Of Adding Redundant Test Case Setmentioning

confidence: 99%

Test case simplification based on coupling metrics in software bug location

Hu¹

2023

J. meas. eng.

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“…Rezazadeh et al applied the convolutional neural networks (CNNs) to identify cracked rotating machinery concerning different crack depths. The scalogram of continuous wavelet transformation and spectrogram of short-time Fourier spectrogram of transient signals were brought forward as two separate training materials [31].…”

Section: State Of the Artmentioning

confidence: 99%

Unbalanced, cracked, and misaligned rotating machines: a comparison between classification procedures throughout the steady-state operation

Rezazadeh

Luca

Perfetto

2022

J Braz. Soc. Mech. Sci. Eng.

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This article compares two intelligent methods for automatic detection of unbalancing, cracks, and parallel misalignment in rotary machines. The finite element method is used to model the faults in a rotating system. The modeled system then operates virtually under different conditions in the steady-state operation; the vibrational responses are calculated numerically. To compare the accuracy of different manners in the classification of defective systems, firstly, four distinct types of features, i.e., statistical, frequency, time–frequency, and uncertainty are exploited. The T test process is utilized to test the extracted characteristics; the unreliable features are removed from feature vectors, then the remained ones are used in four supervised machine learning classifiers, i.e., support vector machine, k-nearest neighbors, Naive Bayes, and decision trees. In the following, as the convolution neural networks (CNNs) approach, the persistence spectrums of raw signals are plotted, and these graphs are introduced as input data. Comparing results of the different classification methods, it has been observed that although CNNs based on persistence spectrum graphs are computationally heavy and time-consuming, they provide more accurate results than the other classifiers. The results show that the proposed approach for rotor fault detection is effective, accurate, and robust and that it has promise for real engineering applications.

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“…In the feature extraction stage, wavelet transform and WTS are useful tools [28]. The continuous wavelet scalogram can also function as the visual input for feature extraction in a convolution layer [29].…”

Section: Introductionmentioning

confidence: 99%

Classification of Unbalanced and Bowed Rotors under Uncertainty Using Wavelet Time Scattering, LSTM, and SVM

et al. 2023

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A growing interest in intelligent fault detection may sometimes lead to practical issues when existing malfunctions reveal analogous indications and the number of observations is limited. This article addresses the classification problem of two identical malfunctions, i.e., unbalancing and shaft bow in rotary machines, where only 56 observations were utilized for the training. The faulty systems are modeled in ABAQUS/CAE; a data set for each fault is created by simulation under various physical and operational conditions employing the uncertainty concept. The wavelet time scattering (WTS) technique extracts low-variance presentations from signals. With respect to the classification procedure of the faulted rotor systems, two models are examined with the extracted features from WTS as the input. Initially, a long short-term memory (LSTM) network is trained and tested, and then, the capability of a support vector machine (SVM) model is inquired. Ultimately, the classification models are trained and tested using the raw time series data and the extracted features to compare the effectiveness of the suggested methods, i.e., WTS. The employed approach for feature extraction demonstrated remarkable effectiveness in addressing a potential hurdle in identifying faults in rotating systems: the ability to differentiate between unbalanced and bowed rotors, irrespective of the classification model utilized.

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Classification of a cracked-rotor system during start-up using Deep learning based on convolutional neural networks

Cited by 5 publications

References 20 publications

Test case simplification based on coupling metrics in software bug location

Test case simplification based on coupling metrics in software bug location

Unbalanced, cracked, and misaligned rotating machines: a comparison between classification procedures throughout the steady-state operation

Classification of Unbalanced and Bowed Rotors under Uncertainty Using Wavelet Time Scattering, LSTM, and SVM

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