Early and extremely early multi-label fault diagnosis in induction motors

Juez-Gil, Mario; Saucedo-Dorantes, Juan José; Arnaiz‐González, Álvar; Nozal, Carlos López; García‐Osorio, César; Lowe, David

doi:10.1016/j.isatra.2020.07.002

Cited by 26 publications

(14 citation statements)

References 65 publications

(82 reference statements)

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“…And mechanical faults often occur due to unexpected working environments, which has made condition monitoring and fault diagnosis a hot spot in the field of rotating machinery [1][2]. Timely fault detection is very important to ensure reliable operations and avoid catastrophic accidents [3]. Vibration analysis has developed into a dominant detection technology as vibration signals can reliably reflect the health condition of rotating machinery [4][5].…”

Section: Introductionmentioning

confidence: 99%

Improved cyclostationary analysis method based on TKEO and its application on the faults diagnosis of induction motors

Wang

Yang

et al. 2022

ISA Transactions

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Section: Introductionmentioning

confidence: 99%

Improved cyclostationary analysis method based on TKEO and its application on the faults diagnosis of induction motors

Wang

Yang

et al. 2022

ISA Transactions

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“…Therefore, fault diagnosis has played an important role in the safety guarantee practice. [1,2] With the help of advanced sensors technology, data acquisition has become easier and a data-based fault diagnosis method has been developed rapidly. [3,4] The data-based fault diagnosis method does not need to establish a complex mechanism model; it only utilizes the relevant or causal latent features among data to match the difference between normal and fault.…”

Section: Introductionmentioning

confidence: 99%

A novel imbalanced fault diagnosis method integrated KLFDA with improved cost‐sensitive learning ANBSVM

Jiang

et al. 2022

Can J Chem Eng

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Fault diagnosis, as an important approach to ensure the safety and stability of industrial processes, has been widely studied in recent years. During the running process, it is noted that the normal data are always much more than the fault data, which demonstrates imbalanced characteristics and leads to a negative effect on the overall accuracy of fault diagnosis. Targeting the problem, a novel imbalanced fault diagnosis method integrated kernel local Fisher discriminant analysis (KLFDA) with improved adaptive near-Bayesian support vector machine (ANBSVM) is proposed in this paper. First, KLFDA is used to extract the non-linear features while maintaining the local spatial structure of the data by introducing flow pattern learning. Second, considering the imbalance characteristics of the data, the data set is divided into a majority class (normal data) and a minority class (fault data). The density distributions of the two classes in their overlapping region are characterized by the proportional function of variance. Third, by minimizing the Bayesian error under the proportion function, the weight factors are adaptively obtained and then introduced into the objective function of the support vector machine (SVM). Namely, a cost sensitivity-based ANBSVM classifier for fault diagnosis is constructed. Finally, by the simulation experiment on the Tennessee Eastman (TE) process, the comparison results show that the proposed ANBSVM-based fault diagnosis method makes progress in the performance of fault diagnosis with higher diagnostic accuracy and F1 score.

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“…Hence, BRB detection is still an open issue for researchers searching for highly accurate techniques, as BRBs are some of the most frequent and hard to detect faults in IM. BRB detection is even more difficult to perform during the steady state and under low mechanical load [27,28].…”

Section: Introductionmentioning

confidence: 99%

Broken Rotor Bar Detection in Induction Motors through Contrast Estimation

Ferrucho-Alvarez

Martinez-Herrera

Cabal-Yépez

et al. 2021

Sensors

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Induction motors (IM) are key components of any industrial process; hence, it is important to carry out continuous monitoring to detect incipient faults in them in order to avoid interruptions on production lines. Broken rotor bars (BRBs), which are among the most regular and most complex to detect faults, have attracted the attention of many researchers, who are searching for reliable methods to recognize this condition with high certainty. Most proposed techniques in the literature are applied during the IM startup transient, making it necessary to develop more efficient fault detection techniques able to carry out fault identification during the IM steady state. In this work, a novel methodology based on motor current signal analysis and contrast estimation is introduced for BRB detection. It is worth noting that contrast has mainly been used in image processing for analyzing texture, and, to the best of the authors’ knowledge, it has never been used for diagnosing the operative condition of an induction motor. Experimental results from applying the approach put forward validate Unser and Tamura contrast definitions as useful indicators for identifying and classifying an IM operational condition as healthy, one broken bar (1BB), or two broken bars (2BB), with high certainty during its steady state.

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Early and extremely early multi-label fault diagnosis in induction motors

Cited by 26 publications

References 65 publications

Improved cyclostationary analysis method based on TKEO and its application on the faults diagnosis of induction motors

Improved cyclostationary analysis method based on TKEO and its application on the faults diagnosis of induction motors

A novel imbalanced fault diagnosis method integrated KLFDA with improved cost‐sensitive learning ANBSVM

Broken Rotor Bar Detection in Induction Motors through Contrast Estimation

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