The rise of Industry 4.0 and smart manufacturing has highlighted the importance of utilizing intelligent manufacturing techniques, tools, and methods, including predictive maintenance. This feature allows for the early identification of potential issues with machinery, preventing them from reaching critical stages. This paper proposes an intelligent predictive maintenance system for industrial equipment monitoring. The system integrates Industrial IoT, MQTT messaging and machine learning algorithms. Vibration, current and temperature sensors collect real-time data from electrical motors which is analyzed using five ML models to detect anomalies and predict failures, enabling proactive maintenance. The MQTT protocol is used for efficient communication between the sensors, gateway devices, and the cloud server. The system was tested on an operational motors dataset, five machine learning algorithms, namely k-nearest neighbor (KNN), supported vector machine (SVM), random forest (RF), linear regression (LR), and naive bayes (NB), are used to analyze and process the collected data to predict motor failures and offer maintenance recommendations. Results demonstrate the random forest model achieves the highest accuracy in failure prediction. The solution minimizes downtime and costs through optimized maintenance schedules and decisions. It represents an Industry 4.0 approach to sustainable smart manufacturing.
by in situ polymerization of aniline monomer, conducting polyaniline (PANI) nanocomposites containing various concentrations of carboxylic acid functionalized multi-walled carbon nanotubes (f-MWCNT) were synthesized. The morphological and electrical properties of pure PANI and PANI /MWCNT nanocomposites were examined by using Fourier transform- infrared spectroscopy (FTIR), X-ray diffraction (XRD) and Atomic Force Microscopy (AFM) respectively. FTIR spectra shows that the carboxylic acid groups formed at the both ends of the sidewalls of the MWCNTs. The aniline monomers were polymerized on the surface of MWCNTs, depending on the -* electron interaction between aniline monomers and MWCNTs and hydrogen bonding into interaction between t
... Show MoreThe paper reports the influence of annealing temperature under vacuum for one hour on the some structural and electrical properties of p-type CdTe thin films were grown at room temperature under high vacuum by using thermal evaporation technique with a mean thickness about 600nm. X-ray diffraction analysis confirms the formation of CdTe cubic phase at all annealing temperature. From investigated the electrical properties of CdTe thin films, the electrical conductivity, the majority carrier concentration, and the Hall mobility were found increase with increasing annealing temperatures.
In this paper, CdO nanoparticles prepared by pulsed laser deposition techniqueonto a porous silicon (PS) surface prepared by electrochemical etching of p-type silicon wafer with resistivity (1.5-4Ω.cm) in hydrofluoric (HF) acid of 20% concentration. Current density (15 mA/cm2) and etching times (20min). The films were characterized by the measurement of AFM, FTIR spectroscopy and electrical properties.
Atomic Force microscopy confirms the nanometric size.Chemical components during the electrochemical etching show on surface of PSchanges take place in the spectrum of CdO deposited PS when compared to as-anodized PS.
The electrical properties of prepared PS; namely current density-voltage charact
... Show MoreABSTRACT: Thin film of CdS has been deposited onto clean glass substrate by using Spray pyrolysis technique. Results of Morphological (AFM) studied; electrical properties and optical conductivity studied are analysis. AFM results show a crystalline nature of the films. From the conductivity measurement at different temperatures, the activation energy of the films was calculated and found to be between 0.188 - 0.124 eV for low temperature regions, and between 1.67-1.19eV for high temperature regions. Hall measurements of electrical properties at room temperature show that the resistivity and mobility of CdS polycrystalline films deposited at 400 C0, were 3.878x103 . cm and 1.302x104cm2/ (V.s), respectively. The electrical conductivity of th
... Show MoreThe aim of this paper, study the effect of carbon nanotubes on the electrical properties of polyvinylchloride. Samples of polyvinylchloride carbon nanotubes composite prepared by using hot press technique. The weight percentages of carbon nanotubes are 0,5,10 and 20wt.%. Results showed that the D.C electrical conductivity increases with increasing of the weight percentages of carbon nanotubes. Also, the D.C electrical conductivity changed with increase temperature for different concentrations of carbon nanotubes. The activation energy of D.C electrical conductivity is decreased with increasing of carbon nanotubes concentration.
The paper reports the influence of annealing temperature under vacuum for one hour on the some structural and electrical properties of p-type CdTe thin films were grown at room temperature under high vacuum by using thermal evaporation technique with a mean thickness about 600nm. X-ray diffraction analysis confirms the formation of CdTe cubic phase at all annealing temperature. From investigated the electrical properties of CdTe thin films, the electrical conductivity, the majority carrier concentration, and the Hall mobility were found increase with increasing annealing temperatures.
In this work, As60Cu40-xSex thin films were synthesized, and the pulsed laser deposition method was used to study the effected partial replacement of copper with selenium. The electrical characteristics and optical characteristics, as indicated by the absorbance and transmittance as a function of wavelength were calculated. Additionally, the energy gap was computed. The electrical conductivity of the DC in the various conduction zones was calculated by measuring the current and voltage as a function of temperature. Additionally, the mathematical equations are used to compute the energy constants, electron hopping distance, tail width, pre-exponential factor, and density of the energy states in variation zones (densities of the energ
... Show MoreThe literature shows conflicting outcomes, making it difficult to determine how e-learning affects the performance of students in higher education. The effect of e-learning was studied and data has been gathered with the utilization of a variety of qualitative and quantitative methods, especially in relation to students' academic achievements and perceptions in higher education, according to literature review that has been drawn from articles published in the past two decades (2000-2020). The development of a sense of community in the on-line environment has been identified to be one of the main difficulties in e-learning education across this whole review. In order to create an efficient online learning community, it could be claim
... Show MoreSocial media is known as detectors platform that are used to measure the activities of the users in the real world. However, the huge and unfiltered feed of messages posted on social media trigger social warnings, particularly when these messages contain hate speech towards specific individual or community. The negative effect of these messages on individuals or the society at large is of great concern to governments and non-governmental organizations. Word clouds provide a simple and efficient means of visually transferring the most common words from text documents. This research aims to develop a word cloud model based on hateful words on online social media environment such as Google News. Several steps are involved including data acq
... Show MoreIn this paper, the memorization capability of a multilayer interpolative neural network is exploited to estimate a mobile position based on three angles of arrival. The neural network is trained with ideal angles-position patterns distributed uniformly throughout the region. This approach is compared with two other analytical methods, the average-position method which relies on finding the average position of the vertices of the uncertainty triangular region and the optimal position method which relies on finding the nearest ideal angles-position pattern to the measured angles. Simulation results based on estimations of the mobile position of particles moving along a nonlinear path show that the interpolative neural network approach outperf
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