Artificial Intelligence Application in Machine Condition Monitoring and Fault Diagnosis

The subject of machine condition monitoring and fault diagnosis as a part of system maintenance has gained a lot of interest due to the potential benefits to be learned from reduced maintenance budgets, enhanced productivity and improved machine availability. Artificial intelligence (AI) is a successful method of machine condition monitoring and fault diagnosis since these techniques are used as tools for routine maintenance. This chapter attempts to summarize and review the recent research and developments in the field of signal analysis through artificial intelligence in machine condition monitoring and fault diagnosis. Intelligent systems such as artificial neural network (ANN), fuzzy logic system (FLS), genetic algorithms (GA) and support vector machine (SVM) have previously developed many different methods. However, the use of acoustic emission (AE) signal analysis and AI techniques for machine condition monitoring and fault diagnosis is still rare. In the future, the applications of AI in machine condition monitoring and fault diagnosis still need more encouragement and attention due to the gap in the literature

Hyperbolic Functions of Al-Tememe Acceleration Methods for Improving the Values of Integrations Numerically of First Kind

Abstract: The main aim of this work is to introduce acceleration methods called a hyperbolic acceleration methods which are of series of numerated methods. In general, these methods named as AL-Tememe’s acceleration methods of first kind discovered by (Ali Hassan Mohammed). These are very beneficial to acceleration the numerical results for definite integrations with continuous integrands which are of 2nd order main error, with respect to the accuracy and the number of the used subintervals and the fasting obtaining results. Especially, for acceleration the results which are obviously obtained by trapezoidal and midpoint methods. Moreover, these methods could be enhancing the results of the ordinary differential equations numerically which are of 2nd order main error

Triangular functions of Al-Tememe Acceleration Methods of First Kind for Improving the Values of Integrals Numerically

Abstract: The main aim of this work is to introduce acceleration methods called a Trigonometric acceleration methods which are of series of numerated methods. In general, these methods named as AL-Tememe’s acceleration methods of first kind to his discoverer ''Ali Hassan Mohammed''. These are very beneficial to acceleration the numerical results for definite integrations with continuous integrands which are of 2nd order main error, with respect to the accuracy and the number of the used subintervals and the fasting obtaining results. Especially, for acceleration the results which are obviously obtained by trapezoidal and midpoint methods. Moreover, these methods could be enhancing the results of the ordinary differential equations numerically which are of 2nd order main error

Prediction, classification and diagnosis of spur gear conditions using artificial neural network and acoustic emission

The gear system is a critical component in the machinery and predicting the performance of a gear system is an important function. Unpredictable failures of a gear system can cause serious threats to human life, and have large scale economic effects. It is necessary to inspect gear teeth periodically to identify crack propagation and, other damages at the earliest. This study has two main objectives. Firstly, the research predicted and classified specific film thickness (λ) of spur gear by Artificial Neural Network (ANN) and Regression models. Parameters such as acoustic emission (AE), temperature and specific film thickness (λ) data were extracted from works of other researchers. The acoustic emission signals and temperature were used as input to ANN and Regression models, while (λ) was the output of the models. Second objective is to use the third generation ANN (Spiking Neural Network) for fault diagnosis and classification of spur gear based on AE signal. For this purpose, a test rig was built with several gear faults. The AE signal was processed through preprocessing, features extraction and selection methods before the developed ANN diagnosis and classification model were built. These processes were meant to improve the accuracy of diagnosis system based on information or features fed into the model. This research investigated the possibility of improving accuracy of spur gear condition monitoring and fault diagnoses by using Feed-Forward Back- Propagation Neural Networks (FFBP), Elman Network (EN), Regression Model and Spiking Neural Network (SNN). The findings showed that use of specific film thickness has resulted in the FFBP network being able to provide 99.9% classification accuracy, while regression and multiple regression models attained 73.3 % and 81.2% classification accuracy respectively. For gear fault diagnosis, the SNN achieved nearly 97% accuracy in its diagnosis. Finally, the methods use in the study have proven to have high accuracy and can be used as tools for prediction, classification and fault diagnosis in spur gear

Counterterrorism in Public Opinion: A Cross Sectional Research in Punjab, Pakistan

Main objective of terrorism is to influence wide audience and creates state of fear among them Demand for scaled down of terrorism is foremost phenomenon in Pakistan Public pursued governments for not only sustainable terrorism policy but also react to affairs related to terrorism Present study aimed to collect general information regarding terrorism and government responses to terrorism in the light of public perspicacity A cross sectional survey was conducted with a sample size of 372 inhabitants from Punjab Pakistan The study demonstrated public feelings and thinking regarding responses to terrorism by government of Pakistan and role of military offensive actions Majority of the respondents shown confidence on military response to terrorism Political leadership s policies regarding counterterrorism were not highly appreciated by the public Political affiliation of the respondents affirmed the offensive action against all forms of terrorism Political affiliation significantly favors p 000 0 05 the demand that Pakistan army should be given full authority to control terrorism International assistance to counterterrorism was disproved by the people of Pakistan Demand of negotiation with militant was much significant p 001 0 05 among those who belong to religious organization

Using K-fold cross validation proposed models for SpikeProp learning enhancements

Spiking Neural Network (SNN) uses individual spikes in time field to perform as well as to communicate computation in such a way as the actual neurons act. SNN was not studied earlier as it was considered too complicated and too hard to examine. Several limitations concerning the characteristics of SNN which were not researched earlier are now resolved since the introduction of SpikeProp in 2000 by Sander Bothe as a supervised SNN learning model. This paper defines the research developments of the enhancement Spikeprop learning using K-fold cross validation for datasets classification. Hence, this paper introduces acceleration factors of SpikeProp using Radius Initial Weight and Differential Evolution (DE) Initialization weights as proposed methods. In addition, training and testing using K-fold cross validation properties of the new proposed method were investigated using datasets obtained from Machine Learning Benchmark Repository as an improved Bohte's algorithm. A comparison of the performance was made between the proposed method and Backpropagation (BP) together with the Standard SpikeProp. The findings also reveal that the proposed method has better performance when compared to Standard SpikeProp as well as the BP for all datasets performed by K-fold cross validation for classification datasets

Investigating The Effect of Brand’s Social Media Pages On Developing Economy Consumers Purchase Intention

With the passage of time people come to know that, investigating the brand social media, the brand familiarity and information quality on the future purchase intention is very impactful. In spite of appreciation of brand familiarity and information, quality prominence in brand communities, dynamics and consequences remains limited. To explore this subject of consumer purchase intention we found that information quality and brand familiarity effect the future purchase. To explore our hypotheses, we directed a survey with about 200 university students with the target population of Karachi Pakistan using Facebook with considerable experience with a particular brand. In this research, the statistical technique we use is the structural equation Modeling SEM. This technique helps to determine the structural relation found between latent constructs and measured variables. Furthermore, the findings show that there is a big impact of Brand familiarity and Information quality on Attitude towards brand social media pages and Future purchase intention

Green synthesis of titanium and zinc oxide nanoparticles for simultaneous photocatalytic removal of estrogens in wastewater

This study reports on the green synthesis of titanium and zinc oxide nanoparticles (ZnO-NPs) using lemon juice and peel extract, zinc acetate, ethylene glycol, and titanium IV isopropoxide as precursors. The prepared TiO2 and ZnO-NPs were characterized via X-ray Diffraction analysis, UV-vis spectroscopy, dynamic light scattering, scanning electron microscopy, and EDAX. The as-prepared samples, TiO2 and ZnO-NPs, were further subjected for the photocatalytic degradation of estrogenic hormones (Estrone, Estradiol, Ethinylestradiol, and Estriol) under UV light irradiation at 365 nm, which resulted in promising photocatalytic activity. All four hormones were significantly degraded owing to the photocatalytic activity combined with a slight contribution (4-11 %) from the hormonal adsorption onto the surface of the photocatalysts. Overall hormonal degradation rates in the range of 84-93 % and approximately 99 % were achieved in 60 minutes under UV light irradiation by ZnO and TiO2, respectively. © 2021 NANOCON Conference Proceedings - International Conference on Nanomaterials. All rights reserved.Ministerstvo Školství, Mládeže a Tělovýchovy, MŠMT: IGACPS2021002, RP/CPS/2020/002; Univerzita Tomáše Bati ve Zlín

Platelet derived growth factor inhibitors: A potential therapeutic approach for ocular neovascularization.

Retinochoroidal vascular diseases are the leading causes of blindness in the developed world. They include diabetic retinopathy (DR), retinal vein occlusion, retinopathy of prematurity, age-related macular degeneration (AMD), and pathological myopia, among many others. Several different therapies are currently under consideration for the aforementioned disorders. In the following section, agents targeting platelet-derived growth factor (PDGF) are discussed as a potential therapeutic option for retinochoroidal vascular diseases. PDGF plays an important role in the angiogenesis cascade that is activated in retinochoroidal vascular diseases. The mechanism of action, side effects, efficacy, and the potential synergistic role of these agents in combination with other treatment options is discussed. The future of treatment of retinochoroidal vascular diseases, particularly AMD, has become more exciting due to agents such as PDGF antagonists