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.
ABSTRACT: Polypyrrole and polypyrrole / silver nanocomposites were fabricated by in-situ polymerization employing Ammonium Persulphate as an oxidizing agent. Nanocomposites were synthesized by combining polypyrrole and silver nanoparticles in various weight percentages (0.1%, 0.5%, 3%, 5% and 7% wt.). Crystallographic data were collected using X-ray diffraction. PPy particles were found to have an orthorhombic symmetry. In contrast, PPy/Ag nanocomposites were reported to have monoclinic structure. The crystallite size was determined by XRD using Scherrer equation and considered to be within 49 nm range. DC conductivity of pelletized samples was evaluated in the temperature range of 323.15k to 453.15k. The conductiv
... Show MoreABSTRACT: Polypyrrole and polypyrrole / silver nanocomposites were fabricated by in-situ polymerization employing Ammonium Persulphate as an oxidizing agent. Nanocomposites were synthesized by combining polypyrrole and silver nanoparticles in various weight percentages (0.1%, 0.5%, 3%, 5% and 7% wt.). Crystallographic data were collected using X-ray diffraction. PPy particles were found to have an orthorhombic symmetry. In contrast, PPy/Ag nanocomposites were reported to have monoclinic structure. The crystallite size was determined by XRD using Scherrer equation and considered to be within 49 nm range. DC conductivity of pelletized samples was evaluated in the temperature range of 323.15k to 453.15k. The conductivity displayed an
... Show MoreThe silicon carbide/carbon fiber (SiC/CF) hybrid fillers were introduced to improve the electrical and thermal conductivities of the epoxy resin composites. Results of Fourier transform infrared spectroscopy revealed that the peaks at 3532 and 2850 cm−1 relate to carboxylic acid O–H stretching and aldehyde C–H stretching appearing deeper with an increased volume fraction of SiC. Scanning electron microscopic image shows a better interface bonding between the fiber and the matrix when the volume fraction of SiC particles are increased. As frequency increases from 102 Hz to 106 Hz, dielectric constants decrease slightly. Dissipation factor (tan δ) values keep low a
... Show MoreTo enhance interfacial bonding between carbon fibers and epoxy matrix, the carbon fibers have been modified with multiwall carbon nanotubes (MWCNTs) using the dip- coating technique. FT-IR spectrum of the MWCNTs shows a peak at 1640 cm−1 corresponding to the stretching mode of the C=C double bond which forms the framework of the carbon nanotube sidewall. The broad peak at 3430 cm−1 is due to O–H stretching vibration of hydroxyl groups and the peak at 1712 cm−1 corresponds to the carboxylic (C=O) group attached to the carbon fiber. The peaks at 2927 cm−1 and 2862 cm−1 ar
(Sb2S3)1-xSnx thin films with different concentrations (0, 0.05 and
0.15) and thicknesses (300,500 and 700nm) have been deposited by
single source vacuum thermal evaporation onto glass substrates at
ambient temperature to study the effect of tin content, thickness and
on its structural morphology, and electrical properties. AFM study
revealed that microstructure parameters such as crystallite size, and
roughness found to depend upon deposition conditions. The DC
conductivity of the vacuum evaporated (Sb2S3)1-x Snx thin films was
measured in the temperature range (293-473)K and was found to
increase on order of magnitude with
This paper reports a.c., d.c. conductivity and dielectric behavior of Ep-hybrid composite with12 Vol.% Kevlar-Carbon hybrid . D.C. conductivity measurements are conducted on the graded composites by using an electrometer over the temperature range from (293-413) K. It was shown then that conductivity increases by increasing number of Kevlar –Carbon fiber layers (Ep1, Ep2, Ep3), due to the high electrical conductivity of Carbon fiber. To identify the mechanism governing the conduction, the activation energies at low temperature region (LTR) and at high temperature region (HTR) have been calculated. The activation energy values for hybrid composite decrease with increasing number of fiber layers. The a.c. conductivity was measured over fr
... Show MorePorous silicon was prepared by using electrochemical etching process. The structure, electrical, and photoelectrical properties had been performed. Scanning Electron Microscope (SEM) observations of porous silicon layers were obtained before and after rapid thermal oxidation process. The rapid thermal oxidation process did not modify the morphology of porous layers. The unique observation was the pore size decreased after oxidation; pore number and shape were conserved. The wall size which separated between pore was increased after oxidation and that effected on charge transport mechanism of PS
The financial markets are one of the sectors whose data is characterized by continuous movement in most of the times and it is constantly changing, so it is difficult to predict its trends , and this leads to the need of methods , means and techniques for making decisions, and that pushes investors and analysts in the financial markets to use various and different methods in order to reach at predicting the movement of the direction of the financial markets. In order to reach the goal of making decisions in different investments, where the algorithm of the support vector machine and the CART regression tree algorithm are used to classify the stock data in order to determine
... Show MoreAdvances in digital technology and the World Wide Web has led to the increase of digital documents that are used for various purposes such as publishing and digital library. This phenomenon raises awareness for the requirement of effective techniques that can help during the search and retrieval of text. One of the most needed tasks is clustering, which categorizes documents automatically into meaningful groups. Clustering is an important task in data mining and machine learning. The accuracy of clustering depends tightly on the selection of the text representation method. Traditional methods of text representation model documents as bags of words using term-frequency index document frequency (TFIDF). This method ignores the relationship an
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