Improved Identification of Various Conditions of Induction Motor Bearing Faults

Nayana, B. R.; Geethanjali, P.

doi:10.1109/tim.2019.2917981

Cited by 20 publications

(15 citation statements)

References 43 publications

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“…The battery life is calculated based on a commonly used IIoT Li-battery with type ER34615 (EVE Energy Inc. China, price: 10 USD). The parameters of the battery are shown as follows: (15) in which V i , I i , and T i are the averaged voltage, averaged current, and duration of different stages, respectively. i = 1, 2, 3, 4 represents the signal acquisition, signal processing, signal transmission, and idle stages, respectively.…”

Section: Performance Evaluation Of the Iiot Nodes With Different Hmentioning

confidence: 99%

Efficient Data Reduction at the Edge of Industrial Internet of Things for PMSM Bearing Fault Diagnosis

Wang

Huang

et al. 2021

IEEE Trans. Instrum. Meas.

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An efficient data reduction algorithm is designed and implemented on an industrial Internet of Things (IIoT) node for permanent magnet synchronous motor (PMSM) bearing fault diagnosis in variable speed conditions. Leakage flux and vibration signals are, respectively, acquired by a magnetic sensor and an accelerometer on the IIoT node in a noninvasive manner. These two signals are processed and mixed on the IIoT and transmitted to a server. The received signal is separated, the cumulative rotation angle is calculated, and the vibration signal is resampled for bearing fault identification. The proposed method can reduce about 95% of the transmission data while maintaining sufficient precision in bearing fault diagnosis in comparison to a traditional method. The proposed method based on edge computing reduces the power consumption, and hence it is suitable to use on a battery-supplied IIoT node for remote PMSM condition monitoring and fault diagnosis.

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Section: Performance Evaluation Of the Iiot Nodes With Different Hmentioning

confidence: 99%

Efficient Data Reduction at the Edge of Industrial Internet of Things for PMSM Bearing Fault Diagnosis

Wang

Huang

et al. 2021

IEEE Trans. Instrum. Meas.

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“…Recently, machine learning technologies have been extensively used in diagnosis, sensing, monitoring, and measurement applications [6][7][8][9][10][11]. In addition, in such applications, image processing and computer vision technologies have been widely employed [12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Fragment Detection Approach for the Arena Fragmentation Test

Lee

Jung

et al. 2020

Applied Sciences

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The arena fragmentation test (AFT) is one of the tests used to design an effective warhead. Conventionally, complex and expensive measuring equipment is used for testing a warhead and measuring important factors such as the size, velocity, and the spatial distribution of fragments where the fragments penetrate steel target plates. In this paper, instead of using specific sensors and equipment, we proposed the use of a deep learning-based object detection algorithm to detect fragments in the AFT. To this end, we acquired many high-speed videos and built an AFT image dataset with bounding boxes of warhead fragments. Our method fine-tuned an existing object detection network named the Faster R-convolutional neural network (CNN) on this dataset with modification of the network’s anchor boxes. We also employed a novel temporal filtering method, which was demonstrated as an effective non-fragment filtering scheme in our recent previous image processing-based fragment detection approach, to capture only the first penetrating fragments from all detected fragments. We showed that the performance of the proposed method was comparable to that of a sensor-based system under the same experimental conditions. We also demonstrated that the use of deep learning technologies in the task of AFT significantly enhanced the performance via a quantitative comparison between our proposed method and our recent previous image processing-based method. In other words, our proposed method outperformed the previous image processing-based method. The proposed method produced outstanding results in terms of finding the exact fragment positions.

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“…In opposition to the methodologies that use the frequency domain, some studies propose alternative techniques, which use low-cost sensors and perform the analysis of the signal in the time domain, as in Nayana and Geethanjali [13], Nayana and Geethanjali [14], that employed time-domain features of the vibration signals, for diagnosing bearing failure in TIMs. The characteristic selection was then carried out using a filter approach, which was implemented using a Laplacian score (LS), and a wrapper approach using a brute-force method.…”

Section: Introductionmentioning

confidence: 99%

“…Some works from different areas have recently employed swarm intelligence tools and meta-heuristics techniques to achieve ideal solutions for complex optimization problems. Among them, it can be mentioned particle swarm optimization (PSO), whale optimization (WO), wheel-based differential evolution (WDE), grasshopper optimization (GO), biogeography-based optimization (BBO), beetle antennae search (BAS), and artificial bee colony (ABC) algorithms [8,14,16,17,23,[25][26][27][28]. In the work of Haidong et al [23], the researchers used the PSO algorithm to optimize the multiple parameters of the model proposed to diagnose bearing defects in several rotating machines.…”

Section: Introductionmentioning

confidence: 99%

“…In the work of Nayana and Geethanjali [14] previously described, the PSO and WDE algorithms allowed the identification of the most relevant features extracted through the feature selection tools used in the methodology. This approach presented classification rates of approximately 96% even in situations where the TIMs were subject to variations in the load, demonstrating the system's promising diagnostic capacity.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mutual Information and Meta-Heuristic Classifiers Applied to Bearing Fault Diagnosis in Three-Phase Induction Motors

et al. 2020

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Three-phase induction motors are extensively used in industrial processes due to their robustness, adaptability to different operating conditions, and low operation and maintenance costs. Induction motor fault diagnosis has received special attention from industry since it can reduce process losses and ensure the reliable operation of industrial systems. Therefore, this paper presents a study on the use of meta-heuristic tools in the diagnosis of bearing failures in induction motors. The extraction of the fault characteristics is performed based on mutual information measurements between the stator current signals in the time domain. Then, the Artificial Bee Colony algorithm is used to select the relevant mutual information values and optimize the pattern classifier input data. To evaluate the classification accuracy under various levels of failure severity, the performance of two different pattern classifiers was compared: The C4.5 decision tree and the multi-layer artificial perceptron neural networks. The experimental results confirm the effectiveness of the proposed approach.

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Improved Identification of Various Conditions of Induction Motor Bearing Faults

Cited by 20 publications

References 43 publications

Efficient Data Reduction at the Edge of Industrial Internet of Things for PMSM Bearing Fault Diagnosis

Efficient Data Reduction at the Edge of Industrial Internet of Things for PMSM Bearing Fault Diagnosis

A Deep Learning-Based Fragment Detection Approach for the Arena Fragmentation Test

Mutual Information and Meta-Heuristic Classifiers Applied to Bearing Fault Diagnosis in Three-Phase Induction Motors

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