A review of intelligent methods for condition monitoring and fault diagnosis of stator and rotor faults of induction machines

Nowadays, induction motor (IM) is extensively used in industry, including mechanical and electrical applications. However, three main types of IM faults have been discussed in the literature, bearing, stator, and rotor. Importantly, stator and rotor faults represent approximately 50%. Traditional condition monitoring (CM) and fault diagnosis (FD) methods require a high processing cost and much experience knowledge. To tackle this challenge, artificial intelligent (AI) based CM and FD techniques are extensively developed. However, there have been many review research papers for intelligent CM and FD machine learning methods of rolling elements bearings of IM in the literature. Whereas there is a lack in the literature, and there are not many review papers for both stator and rotor intelligent CM and FD. Thus, the proposed study's main contribution is in reviewing the CM and FD of IM, especially for the stator and the rotor, based on AI methods. The paper also provides discussions on the main challenges and possible future works

A review of wearable sensors based monitoring with daily physical activity to manage type 2 diabetes

Globally, the aging and the lifestyle lead to rabidly increment of the number of type two diabetes (T2D) patients. Critically, T2D considers as one of the most challenging healthcare issue. Importantly, physical activity (PA) plays a vital role of improving glycemic control T2D. However, daily monitoring of T2D using wearable devices/ sensors have a crucial role to monitor glucose levels in the blood. Nowadays, daily physical activity (PA) and exercises have been used to manage T2D. The main contribution of the proposed study is to review the literature about managing and monitoring T2D with daily PA through wearable devices and sensors. Finally, challenges and future trends are also highlighted

A Review of Physical Human Activity Recognition Chain Using Sensors

In the era of Internet of Medical Things (IoMT), healthcare monitoring has gained a vital role nowadays. Moreover, improving lifestyle, encouraging healthy behaviours, and decreasing the chronic diseases are urgently required. However, tracking and monitoring critical cases/conditions of elderly and patients is a great challenge. Healthcare services for those people are crucial in order to achieve high safety consideration. Physical human activity recognition using wearable devices is used to monitor and recognize human activities for elderly and patient. The main aim of this review study is to highlight the human activity recognition chain, which includes, sensing technologies, preprocessing and segmentation, feature extractions methods, and classification techniques. Challenges and future trends are also highlighted.

A Novel Feature Extraction and Fault Detection Technique for the Intelligent Fault Identification of Water Pump Bearings

The reliable and cost-effective condition monitoring of the bearings installed in water pumps is a real challenge in the industry. This paper presents a novel strong feature selection and extraction algorithm (SFSEA) to extract fault-related features from the instantaneous power spectrum (IPS). The three features extracted from the IPS using the SFSEA are fed to an extreme gradient boosting (XBG) classifier to reliably detect and classify the minor bearing faults. The experiments performed on a lab-scale test setup demonstrated classification accuracy up to 100%, which is better than the previously reported fault classification accuracies and indicates the effectiveness of the proposed method

A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor

The fault detection and diagnosis (FDD) along with condition monitoring (CM) and of rotating machinery (RM) have critical importance for early diagnosis to prevent severe damage of infrastructure in industrial environments. Importantly, valuable industrial equipment needs continuous monitoring to enhance the safety, reliability, and availability and to decrease the cost of maintenance of modern industrial systems and applications. However, induction motor (IM) has been extensively used in several industrial processes because it is cheap, reliable, and robust. Rolling bearings are considered to be the main component of IM. Undoubtedly, any failure of this basic component can lead to a serious breakdown of IM and for whole industrial system. Thus, many current methods based on different techniques are employed as a fault prognosis and diagnosis of rolling elements bearing of IM. Moreover, these techniques include signal/image processing, intelligent diagnostics, data fusion, data mining, and expert systems for time and frequency as well as time-frequency domains. Artificial intelligence (AI) techniques have proven their significance in every field of digital technology. Industrial machines, automation, and processes are the net frontiers of AI adaptation. There are quite developed literatures that have been approaching the issues using signals and data processing techniques. However, the key contribution of this work is to present an extensive review of CM and FDD of the IM, especially for rolling elements bearings, based on artificial intelligent (AI) methods. This study highlights the advantages and performance limitations of each method. Finally, challenges and future trends are also highlighted

Investigating The Impact of AI- Driven Chatbots On the Acquisition of English as A Foreign Language Among Saudi Undergraduate Students

The primary aim of this research is to investigate the influence of AI-driven technology, such as chatbots, on the acquisition of English as a foreign language among undergraduate students currently enrolled at Najran University. The present study examines the reported benefits, academic accomplishments, challenges, and overall satisfaction of using chatbots for English language acquisition. However, the process of gathering data for language proficiency assessments is commonly conducted by administering surveys. The surveys gathered measurable data on students' viewpoints regarding the benefits of employing chatbots, as well as their overall satisfaction with these solutions. The research assessed the influence of chatbots on the process of language acquisition by quantifying students' language abilities before and following the implementation of these technological resources. This study also investigates the challenges and limitations associated with implementing chatbots. This study also aims to identify the obstacles associated with chatbot-based language learning systems to get insights into areas that can be enhanced. The findings inform the design and implementation of more effective language learning systems. The research findings significantly contribute to the existing academic literature on novel approaches to foreign language acquisition (FLA) in higher education contexts. The results obtained from this study offer significant insights to educators specializing in language instruction, developers responsible for designing curriculum, and technology experts interested in the effectiveness and limitations of artificial intelligence technologies and chatbots in enhancing the English language learning process. However, the research provides empirically supported recommendations for integrating chatbots into language learning instructional methods and optimizing the effectiveness and involvement of FLA encounters for undergraduate students at Najran University and comparable educational establishments

AI-driven Technology and Chatbots as Tools for Enhancing English Language Learning in the Context of Second Language Acquisition: A Review Study

The proposed review study aims to explore the role of AI-driven technology and chatbots in enhancing English language learning within the context of second language acquisition. With the increasing integration of technology into language education, AI-driven tools and chatbots have raised as innovative approaches to provide personalized and interactive learning experiences. This review study aims to examine the effectiveness of these tools in improving language proficiency, learner engagement, and learner autonomy. The review involves a comprehensive analysis of relevant literature, including empirical studies, theoretical frameworks, and best practices in the field. The findings reveal that AI-driven technology and chatbots offer numerous benefits, such as individualized feedback, adaptive learning pathways, and authentic language interactions. Moreover, they promote learner’s motivation, active participation, and self-directed learning. However, challenges regarding the design, implementation, and assessment of these tools also emerge. This review study provides insights into the potential and limitations of AI-driven technology and chatbots in English language learning and suggests future directions for research and pedagogical applications in second language acquisition

Synthesis and Characterization of Manganese-Modified Black TiO2 Nanoparticles and Their Performance Evaluation for the Photodegradation of Phenolic Compounds from Wastewater

The release of phenolic-contaminated treated palm oil mill effluent (TPOME) poses a severe threat to human and environmental health. In this work, manganese-modified black TiO2 (Mn-B-TiO2) was produced for the photodegradation of high concentrations of total phenolic compounds from TPOME. A modified glycerol-assisted technique was used to synthesize visible-light-sensitive black TiO2 nanoparticles (NPs), which were then calcined at 300 °C for 60 min for conversion to anatase crystalline phase. The black TiO2 was further modified with manganese by utilizing a wet impregnation technique. Visible light absorption, charge carrier separation, and electron–hole pair recombination suppression were all improved when the band structure of TiO2 was tuned by producing Ti3+ defect states. As a result of the enhanced optical and electrical characteristics of black TiO2 NPs, phenolic compounds were removed from TPOME at a rate of 48.17%, which is 2.6 times higher than P25 (18%). When Mn was added to black TiO2 NPs, the Ti ion in the TiO2 lattice was replaced by Mn, causing a large redshift of the optical absorption edges and enhanced photodegradation of phenolic compounds from TPOME. The photodegradation efficiency of phenolic compounds by Mn-B-TiO2 improved to 60.12% from 48.17% at 0.3 wt% Mn doping concentration. The removal efficiency of phenolic compounds from TPOME diminished when Mn doping exceeded the optimum threshold (0.3 wt%). According to the findings, Mn-modified black TiO2 NPs are the most effective, as they combine the advantages of both black TiO2 and Mn doping

Kinetics and Adsorption Isotherms of Amine-Functionalized Magnesium Ferrite Produced Using Sol-Gel Method for Treatment of Heavy Metals in Wastewater

This study is focused on the kinetics and adsorption isotherms of amine-functionalized magnesium ferrite (MgFe2O4) for treating the heavy metals in wastewater. A sol-gel route was adopted to produce MgFe2O4 nanoparticles. The surfaces of the MgFe2O4 nanoparticles were functionalized using primary amine (ethanolamine). The surface morphology, phase formation, and functionality of the MgFe2O4 nano-adsorbents were studied using the SEM, UV-visible, FTIR, and TGA techniques. The characterized nanoparticles were tested on their ability to adsorb the Pb2+, Cu2+, and Zn2+ ions from the wastewater. The kinetic parameters and adsorption isotherms for the adsorption of the metal ions by the amine-functionalized MgFe2O4 were obtained using the pseudo-first-order, pseudo-second-order, Langmuir, and Freundlich models. The pseudo-second order and Langmuir models best described the adsorption kinetics and isotherms, implying strong chemisorption via the formation of coordinative bonds between the amine groups and metal ions. The Langmuir equation revealed the highest adsorption capacity of 0.7 mmol/g for the amine-functionalized MgFe2O4 nano-adsorbents. The adsorption capacity of the nanoadsorbent also changed with the calcination temperature. The MgFe2O4 sample, calcined at 500 °C, removed the most of the Pb2+ (73%), Cu2+ (59%), and Zn2+ (62%) ions from the water