Health Monitoring of three phase induction motor using infrared thermography

Badoni, Shalinee; Jarial, R. K.

doi:10.1109/icces51350.2021.9489095

Cited by 4 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results obtained in different works for the automatic identification of failures in induction motors using infrared thermography are shown in Table 4. In particular, the methods used, and the accuracy of each methodology are also described [48][49][50][51][52]. It is observed that accuracy values higher than 90% were obtained for the classification of failures in induction motors by all the works.…”

Section: Discussionmentioning

confidence: 98%

“…In this sense, the proposal analyzed different failures in various parts of the induction motor, such as a healthy state, broken rotor bar and bearing failures, and misalignment failures, obtaining an accuracy of 99.4% in the classification. The accurate identification of these failures usually involves the fusion of different variables, such as current and vibration signals, or vibration and stray flux [51][52][53]. • Image processing methods to segment the region of interest.…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, another interesting point that can be discussed is that the presented methodologies required the hand-crafted selection of a wide variety of parameters at each stage their proposal required, such as image processing, feature extraction, dimensionality reduction, and classification algorithms to determine the state of the induction motor [48][49][50][51][52]; moreover, it can be seen that the required computing time for both the training and execution of the proposals was set to be higher compared to methodologies that employed less steps. On the contrary, the proposal did not require this hand-crafted selection to obtain the best possible results, as it processed thermographic images directly.…”

mentioning

confidence: 99%

See 2 more Smart Citations

A CNN-Based Methodology for Identifying Mechanical Faults in Induction Motors Using Thermography

Trejo-Chavez,

Cruz-Albarran,

Resendiz-Ochoa

et al. 2023

Machines

View full text Add to dashboard Cite

Infrared thermography (IRT) has become an interesting alternative for performing condition assessments of different types of induction motor (IM)-based equipment when it operates under harsh conditions. The reported results from state-of-the-art articles that have analyzed thermal images do not consider (1): the presence of more than one fault, and (2) the inevitable noise-corruption the images suffer. Bearing in mind these reasons, this paper presents a convolutional neural network (CNN)-based methodology that is specifically designed to deal with noise-corrupted images for detecting the failures that have the highest incidence rate: bearing and broken bar failures; moreover, rotor misalignment failure is also considered, as it can cause a further increase in electricity consumption. The presented results show that the proposal is effective in detecting healthy and failure states, as well as identifying the failure nature, as a 95% accuracy is achieved. These results allow considering the proposal as an interesting alternative for using IRT images obtained in hostile environments.

show abstract

Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

A CNN-Based Methodology for Identifying Mechanical Faults in Induction Motors Using Thermography

Trejo-Chavez,

Cruz-Albarran,

Resendiz-Ochoa

et al. 2023

Machines

View full text Add to dashboard Cite

show abstract

“…In [30], a data-driven approach coupled with unscented Kalman filter is proposed to enable predictive health management for bearing. In addition to these papers, various CM methods have been reported for health management of IMs [31], [32], [33], [34], [35], [36], [37].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Kalman Filter-Based Condition-Monitoring Technique for Induction Motors

et al. 2023

View full text Add to dashboard Cite

Induction motors are typical rotating machines that are widely used in various industrial processes. The condition of induction motors has to be monitored to avoid serious losses, which can be caused by various reasons. Over the last decades, although many studies have been performed on the condition monitoring (CM), there is still an increasing need for cost-effective and reliable CM techniques for induction motor. This paper presents an adaptive Kalman filter (AKF)-based CM technique for an induction motor driving a scrubber fan. In this work, AKFs are used to extract useful information about the induction motor's condition based on measured vibration signals. The main novelty of the proposed method is the use of multiple AKFs for the detection of outliers and anomalies. The output of the AKFs plays as the basis of severity assessment on the vibration signals. A set of AKFs are employed to deal with various anomaly conditions caused by different severity levels of vibration as the IM is deteriorated. Moreover, the effectiveness of the proposed method is demonstrated through experiments involving a real scrubber fan driven by an induction motor.

show abstract

Machine Learning Based Fault Detection in Induction Motor using Thermal Imaging

Ananthan

2022

2022 3rd International Conference on Electronics and Sustainable Communication Systems (ICESC)

View full text Add to dashboard Cite

Health Monitoring of three phase induction motor using infrared thermography

Cited by 4 publications

References 15 publications

A CNN-Based Methodology for Identifying Mechanical Faults in Induction Motors Using Thermography

A CNN-Based Methodology for Identifying Mechanical Faults in Induction Motors Using Thermography

An Adaptive Kalman Filter-Based Condition-Monitoring Technique for Induction Motors

Machine Learning Based Fault Detection in Induction Motor using Thermal Imaging

Contact Info

Product

Resources

About