Decision fusion scheme for bearing defects diagnosis in induction motors

Agahi, Hamed; Mahmoodzadeh, Azar

doi:10.1007/s00202-020-01024-4

Cited by 12 publications

(7 citation statements)

References 33 publications

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“…The results of the networks were then combined using Dempster-Shafer rules, and the fusion improved the individual performance of the sensors per 10%. Late fusion was also used in [148] to improve the diagnosis of bearing defects in induction motors. The authors first combined the features extracted from the vibration sensors, after removing the redundant ones.…”

Section: Data Fusion Solutions In the Manufacturing Sectormentioning

confidence: 99%

A Review of Multisensor Data Fusion Solutions in Smart Manufacturing: Systems and Trends

Tsanousa

Bektsis

Kyriakopoulos

et al. 2022

Sensors

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Manufacturing companies increasingly become “smarter” as a result of the Industry 4.0 revolution. Multiple sensors are used for industrial monitoring of machines and workers in order to detect events and consequently improve the manufacturing processes, lower the respective costs, and increase safety. Multisensor systems produce big amounts of heterogeneous data. Data fusion techniques address the issue of multimodality by combining data from different sources and improving the results of monitoring systems. The current paper presents a detailed review of state-of-the-art data fusion solutions, on data storage and indexing from various types of sensors, feature engineering, and multimodal data integration. The review aims to serve as a guide for the early stages of an analytic pipeline of manufacturing prognosis. The reviewed literature showed that in fusion and in preprocessing, the methods chosen to be applied in this sector are beyond the state-of-the-art. Existing weaknesses and gaps that lead to future research goals were also identified.

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Section: Data Fusion Solutions In the Manufacturing Sectormentioning

confidence: 99%

A Review of Multisensor Data Fusion Solutions in Smart Manufacturing: Systems and Trends

Tsanousa

Bektsis

Kyriakopoulos

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The reviews in the literature summarize that to obtain information on the state of a low-speed bearing, several indicators can be calculated [7,16,17,[77][78][79][80][81][82][83]. From all indicators found in the literature, a specific group is regularly used, which are referred to the remainder of this article as CIs.…”

Section: Classic Indicatorsmentioning

confidence: 99%

Entropy Indicators: An Approach for Low-Speed Bearing Diagnosis

Sandoval

Leturiondo

Vidal

et al. 2021

Sensors

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To increase the competitiveness of wind energy, the maintenance costs of offshore floating and fixed wind turbines need to be reduced. One strategy is the enhancement of the condition monitoring techniques for pitch bearings, because their low operational speed and the high loads applied to them make their monitoring challenging. Vibration analysis has been widely used for monitoring the bearing condition with good results obtained for regular bearings, but with difficulties when the operational speed decreases. Therefore, new techniques are required to enhance the capabilities of vibration analysis for bearings under such operational conditions. This study proposes the use of indicators based on entropy for monitoring a low-speed bearing condition. The indicators used are approximate, dispersion, singular value decomposition, and spectral entropy of the permutation entropy. This approach has been tested with vibration signals acquired in a test rig with bearings under different health conditions. The results show that entropy indicators (EIs) can discriminate with higher-accuracy damaged bearings for low-speed bearings compared with the regular indicators. Furthermore, it is shown that the combination of regular and entropy-based indicators can also contribute to a more reliable diagnosis.

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“…The presence of an advanced and efficient feature extraction algorithm is essential for distilling the useful features in the images ( 34 , 35 ). Without them, the discrimination and learning capacity of the machine learning would be undermined, causing it to fail to distinguish the defects from the background compounded by its failure to delineate the important features in the input images ( 36 , 37 ). In this context, more attention should be paid to the implementation of a feature extraction algorithm in an attempt to improve the learning capacity of the machine-learning model.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Elman Neural Network and an Invasive Weed Optimization Method for Bridge Defect Recognition

Abdelkader

Moselhi

Marzouk

et al. 2020

Transportation Research Record: Journal of the Transportation R

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Existing bridges are aging and deteriorating, raising concerns for public safety and the preservation of these valuable assets. Furthermore, the transportation networks that manage many bridges face budgetary constraints. This state of affairs necessitates the development of a computer vision-based method to alleviate shortcomings in visual inspection-based methods. In this context, the present study proposes a three-tier method for the automated detection and recognition of bridge defects. In the first tier, singular value decomposition ([Formula: see text]) is adopted to formulate the feature vector set through mapping the most dominant spatial domain features in images. The second tier encompasses a hybridization of the Elman neural network ([Formula: see text]) and the invasive weed optimization (I[Formula: see text]) algorithm to enhance the prediction performance of the ENN. This is accomplished by designing a variable optimization mechanism that aims at searching for the optimum exploration–exploitation trade-off in the neural network. The third tier involves validation through comparisons against a set of conventional machine-learning and deep-learning models capitalizing on performance prediction and statistical significance tests. A computerized platform was programmed in C#.net to facilitate implementation by the users. It was found that the method developed outperformed other prediction models achieving overall accuracy, F-measure, Kappa coefficient, balanced accuracy, Matthews’s correlation coefficient, and area under curve of 0.955, 0.955, 0.914, 0.965, 0.937, and 0.904, respectively as per cross validation. It is expected that the method developed can improve the decision-making process in bridge management systems.

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Decision fusion scheme for bearing defects diagnosis in induction motors

Cited by 12 publications

References 33 publications

A Review of Multisensor Data Fusion Solutions in Smart Manufacturing: Systems and Trends

A Review of Multisensor Data Fusion Solutions in Smart Manufacturing: Systems and Trends

Entropy Indicators: An Approach for Low-Speed Bearing Diagnosis

Hybrid Elman Neural Network and an Invasive Weed Optimization Method for Bridge Defect Recognition

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