Rotating Machinery Diagnosing in Non-Stationary Conditions with Empirical Mode Decomposition-Based Wavelet Leaders Multifractal Spectra

Komorska, Iwona; Puchalski, Andrzej

doi:10.3390/s21227677

Cited by 11 publications

(7 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, excessive vibrations in rotating systems cannot be ignored [ 6 ]. In this context, the study of vibrations can improve the control of a microturbine to obtain high efficiency in these systems [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine

Rodríguez-Abreo

Rodríguez‐Reséndiz

Montoya-Santiyanes

et al. 2021

Sensors

View full text Add to dashboard Cite

Machinery condition monitoring and failure analysis is an engineering problem to pay attention to among all those being studied. Excessive vibration in a rotating system can damage the system and cannot be ignored. One option to prevent vibrations in a system is through preparation for them with a model. The accuracy of the model depends mainly on the type of model and the fitting that is attained. The non-linear model parameters can be complex to fit. Therefore, artificial intelligence is an option for performing this tuning. Within evolutionary computation, there are many optimization and tuning algorithms, the best known being genetic algorithms, but they contain many specific parameters. That is why algorithms such as the gray wolf optimizer (GWO) are alternatives for this tuning. There is a small number of mechanical applications in which the GWO algorithm has been implemented. Therefore, the GWO algorithm was used to fit non-linear regression models for vibration amplitude measurements in the radial direction in relation to the rotational frequency in a gas microturbine without considering temperature effects. RMSE and R2 were used as evaluation criteria. The results showed good agreement concerning the statistical analysis. The 2nd and 4th-order models, and the Gaussian and sinusoidal models, improved the fit. All models evaluated predicted the data with a high coefficient of determination (85–93%); the RMSE was between 0.19 and 0.22 for the worst proposed model. The proposed methodology can be used to optimize the estimated models with statistical tools.

show abstract

Section: Introductionmentioning

confidence: 99%

Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine

Rodríguez-Abreo

Rodríguez‐Reséndiz

Montoya-Santiyanes

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Coefficients

of the discrete wavelet transform (DWT) and the basic wavelet with a compact support

of the function

, wavelet leaders

for the set of the largest coefficients

in the interval of

on any scale are described with dependencies (2) and (3), respectively [ 53 ]

where

are integers and

and

consists of the largest wavelet coefficient

computed at all finer scales

within a narrow time neighborhood

.…”

Section: Methodsmentioning

confidence: 99%

“…It is based on the method of wavelet leaders (WLMF). This method was used to diagnose multiple faults in a conventional gear transmission on a laboratory stand [ 53 ]. Damage symptoms are defined as multifractality level, span of dimensions and singularity with the greatest dimension.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosing of Cycloidal Gear Reducer Using Statistical Features of Vibration Signal and Multifractal Spectra

Komorska

Olejarczyk

Puchalski

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

The article presents a method for diagnosing cycloidal gear damage on a laboratory stand. The damage was simulated by removing the sliding sleeves from two adjacent external pins of the cycloidal gearbox. Damage to the sliding sleeves may occur under operating conditions and can lead to the destruction of the gear unit. Hence, early detection is essential. Signals from torque sensors, rotational speed sensors and vibration acceleration sensors of input and output shafts for various rotational speeds and transmission loads were recorded. The frequency analysis of these signals was carried out. Due to the fluctuation of the rotational speed, the frequency spectrum gives an approximate picture and is not useful in detecting this type of damage. The statistical characteristics of the signal were determined. However, only statistical moments of higher orders, such as kurtosis, are sensitive to the tested damage. Therefore, the use of multifractal analysis of the vibration signal using the wavelet leader method (WLMF) was considered. Then log-cumulants of the multifractal spectrum were selected as the new signal features.

show abstract

“…The terms FFT and DFT are sometimes used interchangeably. But the DFT is the only one of the four that can be applied to digital data since it consists of a finite number of discrete samples [23]. The Discrete Time Fourier Transform (DTFT) is the dual of the Fourier series, an integral projection from continuous periodic frequency to sampled data time as opposed to an integral projection from continuous periodic time to the sampled data frequency.…”

Section: Introductionmentioning

confidence: 99%

Health Monitoring of Milling Tool Inserts Using CNN Architectures Trained by Vibration Spectrograms

Patil¹,

Pardeshi²,

Patange³

2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

In-process damage to a cutting tool degrades the surface nish of the job shaped by machining and causes a signi cant nancial loss. This stimulates the need for Tool Condition Monitoring (TCM) to assist detection of failure before it extends to the worse phase. Machine Learning (ML) based TCM has been extensively explored in the last decade. However, most of the research is now directed toward Deep Learning (DL). The "Deep" formulation, hierarchical compositionality, distributed representation and end-to-end learning of Neural Nets need to be explored to create a generalized TCM framework to perform e ciently in a high-noise environment of cross-domain machining. With this motivation, the design of di erent CNN (Convolutional Neural Network) architectures such as AlexNet, ResNet-50, LeNet-5, and VGG-16 is presented in this paper. Real-time spindle vibrations corresponding to healthy and various faulty con gurations of milling cutter were acquired. This data was transformed into the time-frequency domain and further processed by proposed architectures in graphical form, i.e., spectrogram. The model is trained, tested, and validated considering di erent datasets and showcased promising results.

show abstract

Rotating Machinery Diagnosing in Non-Stationary Conditions with Empirical Mode Decomposition-Based Wavelet Leaders Multifractal Spectra

Cited by 11 publications

References 51 publications

Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine

Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine

Fault Diagnosing of Cycloidal Gear Reducer Using Statistical Features of Vibration Signal and Multifractal Spectra

Health Monitoring of Milling Tool Inserts Using CNN Architectures Trained by Vibration Spectrograms

Contact Info

Product

Resources

About