Information Theoretical Measurements From Induction Motors Under Several Load and Voltage Conditions for Bearing Faults Classification

Bazan, Gustavo Henrique; Scalassara, Paulo Rogério; Endo, Wagner; Goedtel, Alessandro

doi:10.1109/tii.2019.2939678

Cited by 28 publications

(18 citation statements)

References 41 publications

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“…The rolling bearings data collected from real mechanical equipment was used as the target domain data set. Figure 4 is target domain data set acquisition equipment [26]. As shown in Figure 4, two accelerometers were installed on the test bench to measure the horizontal and vertical vibration signals.…”

Section: Experimental Datamentioning

confidence: 99%

A Fault Diagnosis Method for Rolling Bearings Based on Parameter Transfer Learning under Imbalance Data Sets

et al. 2021

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Fault diagnosis under the condition of data sets or samples with only a few fault labels has become a hot spot in the field of machinery fault diagnosis. To solve this problem, a fault diagnosis method based on deep transfer learning is proposed. Firstly, the discriminator of the generative adversarial network (GAN) is improved by enhancing its sparsity, and then adopts the adversarial mechanism to continuously optimize the recognition ability of the discriminator; finally, the parameter transfer learning (PTL) method is applied to transfer the trained discriminator to target domain to solve the fault diagnosis problem with only a small number of label samples. Experimental results show that this method has good fault diagnosis performance.

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Section: Experimental Datamentioning

confidence: 99%

A Fault Diagnosis Method for Rolling Bearings Based on Parameter Transfer Learning under Imbalance Data Sets

et al. 2021

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“…Bazan et al [52] used an MLP model with the MI feature extraction method to classify induction motor bearing faults. MI shows the reduction in uncertainty associated with one random variable when combined with information from another variable in simultaneously acquired time-series data.…”

Section: Multilayer Perceptron (Mlp)mentioning

confidence: 99%

The Importance of Feature Processing in Deep-Learning-Based Condition Monitoring of Motors

Soother

Daudpoto

Harris

et al. 2021

Mathematical Problems in Engineering

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The advent of deep learning (DL) has transformed diagnosis and prognosis techniques in industry. It has allowed tremendous progress in industrial diagnostics, has been playing a pivotal role in maintaining and sustaining Industry 4.0, and is also paving the way for industry 5.0. It has become prevalent in the condition monitoring of industrial subsystems, a prime example being motors. Motors in various applications start deteriorating due to various reasons. Thus, the monitoring of their condition is of prime importance for sustaining the operation and maintaining efficiency. This paper presents a state-of-the-art review of DL-based condition monitoring for motors in terms of input data and feature processing techniques. Particularly, it reviews the application of various input features for the effectiveness of DL models in motor condition monitoring in the sense of what problems are targeted using these feature processing techniques and how they are addressed. Furthermore, it discusses and reviews advances in DL models, DL-based diagnostic methods for motors, hybrid fault diagnostic techniques, points out important open challenges to these models, and signposts the prospective future directions for DL models. This review will assist researchers in identifying research gaps related to feature processing, so that they may effectively contribute toward the implementation of DL models as applied to motor condition monitoring.

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“…On-line monitoring is mainly for the motors under working conditions [8][9][10][11]. The related methods for signal analysis and processing are shown in [12][13][14]. However, the off-line rotor inspection in the production line before rotor assembly is of more significance in guarantying motor quality and reducing failures in operation.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Defect Detection of Die Cast Rotor in Induction Motor Based on Circular Flux Sensing Coils

Zhu

Wang

et al. 2022

IEEE Trans. Ind. Inf.

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The die cast rotor bars in squirrel cage induction motors (SCIMs) are easily subjected to porosity or other defects in production, which considerably affects the motors' reliability and efficiency in operation. Planar flux sensing coils have been investigated for the defect detection of SCIM rotor. However, these types of sensors cannot accurately evaluate the severity of porosity or broken bar. This study develops a novel instrument to inspect and quantitatively analyze the rotor quality of SCIM. The sensor consists of the electromagnetic flux sensing coils directly from a SCIM stator. By injecting a DC voltage at phases A and B of the sensor, the induced voltage signal is generated from phase C. A quantitative fault indicator (QFI) is constructed on the basis of the instrument voltage output. The variation trend of the QFI with respect to fault severity is investigated by establishing a theoretical sensor model. Experimental results indicate that the proposed method can accurately detect the porosity and broken bar and evaluate their severities for the die cast rotor. The developed solution can be easily implemented with low cost and computational complexity, which can achieve real-time inspection of SCIM rotor in the production line.

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Information Theoretical Measurements From Induction Motors Under Several Load and Voltage Conditions for Bearing Faults Classification

Cited by 28 publications

References 41 publications

A Fault Diagnosis Method for Rolling Bearings Based on Parameter Transfer Learning under Imbalance Data Sets

A Fault Diagnosis Method for Rolling Bearings Based on Parameter Transfer Learning under Imbalance Data Sets

The Importance of Feature Processing in Deep-Learning-Based Condition Monitoring of Motors

Real-Time Defect Detection of Die Cast Rotor in Induction Motor Based on Circular Flux Sensing Coils

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