A Bearing Fault Diagnosis Method Based on Wavelet Denoising and Machine Learning

Fu, Shaojun; Wu, Yang; Wang, Rundong; Mao, Mingzhi

doi:10.3390/app13105936

Cited by 14 publications

(6 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [39], authors use five machine learning models, including K-means clustering, decision tree, random forest, and support vector machine to classify bearing faults.…”

Section: Support Vector Machinementioning

confidence: 99%

Using Triple Modular Redundancy for Threshold Determination in DDOS Intrusion Detection Systems

Maksimović,

Nikolić,

Vidojević

et al. 2024

IEEE Access

View full text Add to dashboard Cite

This paper describes an Intrusion Detection System (IDS) which uses several existing known IDS algorithms and Triple Modular Redundancy (TMR) algorithm to make decision about eventual existing attack by majority voting in one constructed ensemble model which solves practically the problem of binary classification. Proposed novel model belongs to so called stacking ensemble methods of machine learning algorithms which uses exactly four algorithms from the group of best binary classification algorithms: Decision trees, Naive Bayes, Support Vector Machine, k-nearest neighbors, logistic regression and AdaBoost and is applicable for any similar problem. Using proposed method, we get a more precisely determined threshold than it is case using whatever of in method individual applied algorithm as well as those algorithms that are the state of the art in the field of binary classification. Besides that, one of the main disadvantages of classic TMR used for classification network traffic, which is the problem of bad over-voting was successfully avoided by improving classic TMR with a new algorithm proposed by the authors. Today a denial of service attacks (DoS) and distributed denial of service (DDoS) are one of most present type on Internet and that is why the authors in this paper paid special attention to them and because of that the authors of proposed method chose to use known KAGGLE dataset which contains data of these type of attacks for the examination of quality of proposed IDS method implemented in suitable software. Obtained results showed that IDS software with implemented proposed method worked precise and timely, which means alarms was trigger properly and efficiently with better results of quality of classification in most important measures than the individual included algorithms who are the state of the art in binary classification.

show abstract

“…In [39], authors use five machine learning models, including K-means clustering, decision tree, random forest, and support vector machine to classify bearing faults.…”

Section: Support Vector Machinementioning

confidence: 99%

Using Triple Modular Redundancy for Threshold Determination in DDOS Intrusion Detection Systems

Maksimović,

Nikolić,

Vidojević

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In this work, denoising is executed through several steps of wavelet denoising algorithms. These algorithms, including Beylkin, Best-localized Daubechies, Symlets, Coiflets, Daubechies, Fejer-Korovkin, Morris minimum-bandwidth orthogonal, and Vaidyanathan, are applied to each vibration signal independently [25,26]. During the denoising process, each algorithm operates by decomposing the vibration signal into its constituent wavelet components, effectively separating the signal from noise.…”

Section: Denoisingmentioning

confidence: 99%

UBO-EREX: Uncertainty Bayesian-Optimized Extreme Recurrent EXpansion for Degradation Assessment of Wind Turbine Bearings

Berghout,

Benbouzid

2024

Electronics

View full text Add to dashboard Cite

Maintenance planning is crucial for efficient operation of wind turbines, particularly in harsh conditions where degradation of critical components, such as bearings, can lead to costly downtimes and safety threats. In this context, prognostics of degradation play a vital role, enabling timely interventions to prevent failures and optimize maintenance schedules. Learning systems-based vibration analysis of bearings stands out as one of the primary methods for assessing wind turbine health. However, data complexity and challenging conditions pose significant challenges to accurate degradation assessment. This paper proposes a novel approach, Uncertainty Bayesian-Optimized Extreme Recurrent EXpansion (UBO-EREX), which combines Extreme Learning Machines (ELM), a lightweight neural network, with Recurrent Expansion algorithms, a recently advanced representation learning technique. The UBO-EREX algorithm leverages Bayesian optimization to optimize its parameters, targeting uncertainty as an objective function to be minimized. We conducted a comprehensive study comparing UBO-EREX with basic ELM and a set of time-series adaptive deep learners, all optimized using Bayesian optimization with prediction errors as the main objective. Our results demonstrate the superior performance of UBO-EREX in terms of approximation and generalization. Specifically, UBO-EREX shows improvements of approximately 5.1460 ± 2.1338% in the coefficient of determination of generalization over deep learners and 5.7056% over ELM, respectively. Moreover, the objective search time is significantly reduced with UBO-EREX with 99.7884 ± 0.2404% over deep learners, highlighting its effectiveness in real-time degradation assessment of wind turbine bearings. Overall, our findings underscore the significance of incorporating uncertainty-aware UBO-EREX in predictive maintenance strategies for wind turbines, offering enhanced accuracy, efficiency, and robustness in degradation assessment.

show abstract

“…Over the years, considerable attention has been devoted to reducing noise interference and extracting meaningful fault features [9][10][11]. Various signal denoising techniques have been explored, including empirical mode decomposition (EMD) [12,13], variational mode decomposition (VMD) [14,15], wavelet analysis (WT) [16][17][18], and threshold denoising [19,20]. Despite their usefulness, these methods often lack adaptability, particularly in noisy environments.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Vibration Signal Denoising Based on Adaptive Denoising Autoencoder

Lu,

Zhou,

2024

Electronics

View full text Add to dashboard Cite

Vibration signal analysis is regarded as a fundamental approach in diagnosing faults in rolling bearings, and recent advancements have shown notable progress in this domain. However, the presence of substantial background noise often results in the masking of these fault signals, posing a significant challenge for researchers. In response, an adaptive denoising autoencoder (ADAE) approach is proposed in this paper. The data representations are learned by the encoder through convolutional layers, while the data reconstruction is performed by the decoder using deconvolutional layers. Both the encoder and decoder incorporate adaptive shrinkage units to simulate denoising functions, effectively removing interfering information while preserving sensitive fault features. Additionally, dropout regularization is applied to sparsify the network and prevent overfitting, thereby enhancing the overall expressive power of the model. To further enhance ADAE’s noise resistance, shortcut connections are added. Evaluation using publicly available datasets under scenarios with known and unknown noise demonstrates that ADAE effectively enhances the signal-to-noise ratio in strongly noisy backgrounds, facilitating accurate diagnosis of faults in rolling bearings.

show abstract

A Bearing Fault Diagnosis Method Based on Wavelet Denoising and Machine Learning

Cited by 14 publications

References 25 publications

Using Triple Modular Redundancy for Threshold Determination in DDOS Intrusion Detection Systems

Using Triple Modular Redundancy for Threshold Determination in DDOS Intrusion Detection Systems

UBO-EREX: Uncertainty Bayesian-Optimized Extreme Recurrent EXpansion for Degradation Assessment of Wind Turbine Bearings

Bearing Fault Vibration Signal Denoising Based on Adaptive Denoising Autoencoder

Contact Info

Product

Resources

About