Broken Rotor Bar Fault Detection in Asynchronous Machines Using Vibration Analysis

Treml, Aline; Flauzino, Rogério Andrade; Ramos, Rodrigo A.; Brito, Geraldo Carvalho

doi:10.1109/pesgm40551.2019.8973827

Cited by 8 publications

(6 citation statements)

References 11 publications

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“…The motor used to capture Dataset B [39] is a squirrel cage AC motor, running at a constant F o = 60HZ and has similar specifications to the motor used to capture Dataset A. The breakdown of the dataset is shown in Table 4.…”

Section: ) Dataset Bmentioning

confidence: 99%

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A Dataset Fusion Algorithm for Generalised Anomaly Detection in Homogeneous Periodic Time Series Datasets

Elhalwagy,

Kalganova

2023

IEEE Access

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The generalisation of Neural Networks (NN) to multiple datasets is often overlooked in literature due to NNs typically being optimised for specific data sources. This becomes especially challenging in time-series tasks due to difficulties in fusing temporal data from multiple sources. However, in a commercial environment, generalisation can effectively utilise available data and computational power which is essential to Green AI, the sustainable development of AI models. This paper introduces "Dataset Fusion," a novel dataset composition algorithm for fusing periodic signals from multiple homogeneous datasets whilst retaining unique features for generalised anomaly detection. The proposed approach, tested on a case study of three-phase current data from two different homogeneous Induction Motor (IM) fault datasets on anomaly detection, outperforms conventional training approaches with an Average F1 score of 0.879 and effectively generalises across all datasets. Furthermore, when tested with varying percentages of the training data, results show that using only 6.25% of the training data, translating to a 93.7% reduction in computational power, results in only a 4.04% decrease in performance, demonstrating the advantages of the proposed approach in terms of both performance and computational efficiency. Moreover, the algorithm's effectiveness under imperfect conditions highlights its potential for use in real-world applications.

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Section: ) Dataset Bmentioning

confidence: 99%

“…For the present case study, two homogeneous open-source datasets [39] [40] will be used to confirm the feasibility of the Dataset Fusion methodology. Specifications of the datasets used are detailed in Table 2.…”

Section: A Dataset Introductionmentioning

confidence: 99%

A Dataset Fusion Algorithm for Generalised Anomaly Detection in Homogeneous Periodic Time Series Datasets

Elhalwagy,

Kalganova

2023

IEEE Access

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“…For the present case study, two homogeneous open-source datasets [36] [37] will be used to confirm the feasibility of the Dataset Fusion methodology. Specifications of the datasets used are detailed in Table II.…”

Section: A Dataset Introductionmentioning

confidence: 99%

“…2) Dataset B: The motor used to capture Dataset B [36] is a squirrel cage AC motor, running at a constant F o = 60HZ and has similar specifications to the motor used to capture Dataset A. The breakdown of the dataset is shown in Table IV.…”

Section: A Dataset Introductionmentioning

confidence: 99%

Multi-Channel LSTM-Capsule Autoencoder Network for Anomaly Detection on Multivariate Data

Elhalwagy

Kalganova

2022

Applied Sciences

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Deep learning techniques have recently shown promise in the field of anomaly detection, providing a flexible and effective method of modelling systems in comparison to traditional statistical modelling and signal processing-based methods. However, there are a few issues that Neural Networks (NN)s face, such as generalisation ability, requiring large volumes of labelled data to train effectively, and understanding spatial context in data. This paper introduces a novel NN architecture to tackle these problems, which utilises a Long-Short-Term-Memory (LSTM) encoder and Capsule decoder in a multi-channel input Autoencoder architecture for use on multivariate time series data. Experimental results show that using Capsule decoders increases the resilience of the model to overfitting and improves training efficiency, which is shown by the improvement of Mean Squared Error (MSE) on unseen data from an average of 10.61 to 2.08 for single channel architectures, and 10.08 to 2.05 for multi-channel architectures. Additionally, results also show that the proposed model can learn multivariate data more consistently, and was not affected by outliers in the training data. The proposed architecture was also tested on an open-source benchmark, where it achieved state-of-the-art performance in outlier detection, and performs best overall with a total accuracy of 0.494 over the metrics tested.

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“…vibração, como magnitude, média, desvio padrão e cruzamentos de zero, foi possível detectar vários níveis de danos nas barras do rotor(CRUZ-VEGA et al, 2017;TREML et al, 2019). A desvantagem da análise de vibração é a necessidade de um sistema de emSIMANI et al (2003).…”

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Detecção de barras interrompidas em rotores de motores de indução por meio de um método de subespaços para identificação de sistemas e algoritmos classificadores

Raimundo da Silva

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Broken Rotor Bar Fault Detection in Asynchronous Machines Using Vibration Analysis

Cited by 8 publications

References 11 publications

A Dataset Fusion Algorithm for Generalised Anomaly Detection in Homogeneous Periodic Time Series Datasets

A Dataset Fusion Algorithm for Generalised Anomaly Detection in Homogeneous Periodic Time Series Datasets

Multi-Channel LSTM-Capsule Autoencoder Network for Anomaly Detection on Multivariate Data

Detecção de barras interrompidas em rotores de motores de indução por meio de um método de subespaços para identificação de sistemas e algoritmos classificadores

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