Ball bearing multiple failure diagnosis using feature-selected autoencoder model

Cheng, Ren-Chi; Chen, Kuo-Shen

doi:10.1007/s00170-022-09054-x

Cited by 11 publications

(12 citation statements)

References 32 publications

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“…Although LVMs such as the VAEs are capable of processing many features and suit feature learning [7], a feature engineering analysis is recommended to select the pertinent measures. In particular, recent literature suggests that the latent space representation is enhanced by restricting the features to non-correlated measures with high informative power [48]. The offline phase of the proposed approach requires labeled data in two steps: first, training the VAE model (or similar) and then defining the NT.…”

Section: A Database Definition and Latent Space Representationmentioning

confidence: 99%

System Condition Monitoring Based on a Standardized Latent Space and the Nataf Transform

Oliveira-Filho,

Zemouri,

Pelletier

et al. 2024

IEEE Access

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This work introduces a new condition monitoring approach for complex systems based on a standardized latent space representation. Latent variable models such as the variational autoencoders are widely used to analyze systems described by a high-dimensional physical space. The encoding of such space defines a low-dimensional and physically representative latent space. Of note, however, the latent space obtained for complex systems operating under multiple conditions is often difficult to exploit in defining an efficient Health Index, thanks to the non-deterministic and hyperparameter-dependent nature of the latent space. In addition, the distribution of the healthy cluster is not known a priori. The original contribution of this paper is to use the Nataf isoprobabilistic transform to map the latent space into a standardized space. This normalizes the spatial structure of the latent space and relaxes the model's sensitivity to

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Section: A Database Definition and Latent Space Representationmentioning

confidence: 99%

System Condition Monitoring Based on a Standardized Latent Space and the Nataf Transform

Oliveira-Filho,

Zemouri,

Pelletier

et al. 2024

IEEE Access

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“…Current technology allows for early detection of the initial stages of bearing spalling, but most bearings fail prematurely due to improper lubrication (contaminants, inadequate lubrication and lubricant selection) [ 120 ]. Moreover, 90% of bearings fail to attain their expected life due to lubrication problems [ 121 ]. So it is highly important that appropriate lubrication is applied to the bearings.…”

Section: Research On Bearings Lubrication Technology Of Wind Turbinementioning

confidence: 99%

Review of Tribological Failure Analysis and Lubrication Technology Research of Wind Power Bearings

et al. 2022

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Wind power, being a recyclable and renewable resource, makes for a sizable portion of the new energy generation sector. Nonetheless, the wind energy industry is experiencing early failure of important components of wind turbines, with the majority of these issues also involving wind power bearings. Bearing dependability is directly tied to the transmission efficiency and work performance of wind turbines as one of its major components. The majority of wind turbine failures are due to bearings, and the vast majority of bearing failures are due to lubrication. The topic of improving the accuracy and life of wind power bearing motion is becoming increasingly essential as the wind power industry develops rapidly. This study examines the various constructions and types of wind turbines, as well as their bearings. We also examined the most typical causes of friction and lubrication failure. Furthermore, contemporary research on wind turbine bearings has been compiled, which mostly comprises the study and development of lubrication technology and other areas. Finally, a conclusion and outlook on current challenges, as well as future research directions, are offered.

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“…Two feature selection techniques, Relief and min redundancy max Relevance (mRMR), are used, and Self-Organizing Maps (SOM) is used for classification. In [15], using unsupervised machine learning algorithms, evaluation for multiple failure analysis is done for bearing faults using vibration signals to detect the various states of bearings like imbalance, lubrication, and others. The performance evaluation was done with optimal feature selection and a novel fault classification technique.…”

Section: Introductionmentioning

confidence: 99%

Optimizing bearing health condition monitoring: exploring correlation feature selection algorithm

Sharma,

Priya,

Naidu

2024

Eng. Res. Express

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Vibration signals are a critical source of information for detecting and diagnosing bearing faults, making this research particularly relevant to the field of condition monitoring of industrial machinery particularly bearings using vibration signal. This study delves into how feature selection can be done using Pearson’s Correlation Co-efficient within the context of monitoring bearing health conditions, utilizing two distinct approaches. Approach-1 involves feature selection without considering labels, while Approach-2 incorporates label consideration. Comparative analysis is conducted against outcomes obtained when all features are selected. The research scrutinizes the impact of feature selection on classifier performance, accuracy, and execution times, utilizing various machine learning algorithms such as Decision Tree (DT), K Nearest Neighbor (KNN), Support Vector Machine (SVM), and Naïve Bayes (NB). The findings underscore that feature selection significantly enhances classifier accuracy while reducing execution times. Specifically, when all features were considered, only DT and KNN with 50 neighbors achieved 100% accuracy. However, with feature selection using Approach-1 (without labels), DT, K Nearest Neighbors, Support Vector Machine (excluding 100 neighbors), and Naive Bayes (with normal/Gaussian kernel) attained 100% accuracy. Employing Approach-2 (with labeled features), DT with 0.7 and 0.9 thresholds, SVM-G with all thresholds (0.6, 0.7, and 0.9), KNN with all thresholds (except 100 neighbors), and NB-n (with all thresholds) achieved 100% accuracy. The study emphasizes the pivotal role of feature selection in enhancing machine learning classifier performance, offering promising avenues for future research and practical applications across diverse domains.

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Ball bearing multiple failure diagnosis using feature-selected autoencoder model

Cited by 11 publications

References 32 publications

System Condition Monitoring Based on a Standardized Latent Space and the Nataf Transform

System Condition Monitoring Based on a Standardized Latent Space and the Nataf Transform

Review of Tribological Failure Analysis and Lubrication Technology Research of Wind Power Bearings

Optimizing bearing health condition monitoring: exploring correlation feature selection algorithm

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