An Integrated Multitasking Intelligent Bearing Fault Diagnosis Scheme Based on Representation Learning Under Imbalanced Sample Condition

Zhang, Jiusi; Zhang, Ke; An, Yiyao; Luo, Hao; Yin, Shen

doi:10.1109/tnnls.2022.3232147

Cited by 76 publications

(27 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The VLSTM-LWSAN performs better than several DL models and techniques, according to the experimental outcomes. In an article, Jiusi Zhang et al [29] proposed a new integrated multitasking intelligent bearing fault detection method that can diagnose faults, classify them, and find faults that are not known to be there. This method uses representational learning under the conditions of imbalanced samples.…”

Section: Related Workmentioning

confidence: 99%

The Role of Socioeconomic Factors in Improving the Performance of Students Based on Intelligent Computational Approaches

et al. 2023

View full text Add to dashboard Cite

Nowadays, we are living in the modern era of technological revolution and globalization, where people are giving more priority to proper education to compete among the top countries and to achieve something in their lives. Education improves a person’s abilities and creativity, which in turn have a positive effect on the development of a nation’s or an individual’s economy and also play a productive role in it. The traditional approaches are based only on statistical measures and are not capable of figuring out the most significant socioeconomic factors affecting the performance of a student. Keeping in mind the significance of socioeconomic status (SES) in improving the performance of a student, this study analyzes the important socioeconomic factors that affect the performance of a student in Khyber Pakhtunkhwa, Pakistan. We developed our own dataset by collecting data from 100 different schools (both government and private) in Khyber Pakhtunkhwa, Pakistan, consisting of more than 5550 students who were given a proper questionnaire survey. The created dataset consists of a total of 18 features and a target class. In this research, we used different statistical and machine learning (ML) methodologies to identify the most crucial elements that significantly affect the academic achievements of a student and have a strong correlation with the target class. To select the most prominent features from the dataset, we used two different feature selectors (FCBF and relief) and measured their performances along with ML models. To measure the significance rate of each ML algorithm using the full and selected feature space, we used different performance measures such as accuracy, precision, recall, sensitivity, specificity, etc. The experimental outcomes show that the feature selection algorithms significantly improve the performance of the classification models by providing more relevant features that have a strong association with the target class. This study also offered some advice for decision-makers, particularly in the respective education sector and other authorities, to develop specific solution strategies, plans, and initiatives to address the issue. It is envisioned that the suggested scheme will help the residents of Khyber Pakhtunkhwa province, in particular, obtain a high-quality education that can help pave the way for an educated and developed Pakistan.

show abstract

Section: Related Workmentioning

confidence: 99%

The Role of Socioeconomic Factors in Improving the Performance of Students Based on Intelligent Computational Approaches

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Being identified by the experimental method, these types of model provide acceptable precision. To avoid rotational speed sensors of mechanical systems, this Zhang et al (2023b) present a novel integrated multitasking intelligent bearing fault diagnosis scheme with the aid of representation learning under imbalanced sample condition, which realizes bearing fault detection, classification, and unknown fault identification. This study requires a significant cost of implementation and it is not very compatible with a good integration on electronic cards in real time.…”

Section: Introductionmentioning

confidence: 99%

Optimal design and control of hybrid synchronous generator regulating turbine angular speed for turbine protection

Tounsi

2023

Wind Engineering

View full text Add to dashboard Cite

In this paper is presented a methodology of systemic design and control of a synchronous generator with hybrid excitation for the generation of wind energy. The design of the generator is made by the analytical method taking into account the interactions of the global system. Indeed, the regulation of the speed of the turbine is ensured by adjustment of the excitation current to regulate the electromagnetic torque and in this case the excitation current is limited to a fixed value since the increase of excitation current leads to an increase generator’s phase currents magnitude. The increase of the phase’s currents of the generator is linked to the increase of the amplitude of the electromotive forces depending on the excitation current. Therefore, to limit the amplitude of the phase’s currents to the value of the design current fixed by the note book specifications for a given power of the wind turbine, it is necessary to limit the excitation current by limiting the supply voltage of the DC-DC converter used to vary the excitation current for the regulation of the electromagnetic torque. This technique of speed adjustment is simpler and less expensive than techniques using mechanical and hydraulic braking systems.

show abstract

“…New methods have been introduced for fault diagnosis and prognosis, including the use of bidirectional gated recurrent units with temporal self-attention mechanisms for remaining useful life prediction (Zhang et al, 2023a). Another innovative approach involves an integrated multitasking intelligent bearing fault diagnosis scheme based on representation learning in imbalanced sample conditions (Zhang et al, 2023b).…”

Section: Introductionmentioning

confidence: 99%

Comparison of acoustic signal–based fault detection of mechanical faults in induction motors using image classification models

Boztas

2023

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

This study presents a novel deep neural network–based method for fault detection in induction motors. The focus was on identifying five types of mechanical cases: normal operation, shaft/load breakage, misalignment, mounting bolt looseness, and cooling fan problems. To increase the realism of the results, a laboratory-collected dataset of stereo microphone recordings was augmented with real factory noise. The audio data was transformed into image data using Mel-frequency cepstral coefficients as the feature extraction method and then processed with image-based classifiers. A comparison was made among 12 different networks in terms of accuracy and number of parameters, revealing that Mobilenet_v2, EfficientNetV2B0, and NASNetMobile had the best performance in terms of both network size and accuracy.

show abstract

An Integrated Multitasking Intelligent Bearing Fault Diagnosis Scheme Based on Representation Learning Under Imbalanced Sample Condition

Cited by 76 publications

References 55 publications

The Role of Socioeconomic Factors in Improving the Performance of Students Based on Intelligent Computational Approaches

The Role of Socioeconomic Factors in Improving the Performance of Students Based on Intelligent Computational Approaches

Optimal design and control of hybrid synchronous generator regulating turbine angular speed for turbine protection

Comparison of acoustic signal–based fault detection of mechanical faults in induction motors using image classification models

Contact Info

Product

Resources

About