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.
A series of overbased magnesium fatty acids such as caprylate, caprate, laurate, myristate, palmitate, stearate and oleate) were synthesized by the reaction of the fatty acids with active – 60 magnesium oxide and carbon dioxide (CO2) gas at 60 oC in the presence of ammonia solution as catalyst, toluene / ethanol solvent mixture (9:1vol/vol) was added.
The prepared detergent additives were characterized by FTIR, 1HNMR and evaluated by blending each additive in various concentrations with medium lubricant oil fraction (60 stock) supplied by Iraqi Midland Refineries Company. The total base number (TBN, mg of KOH/g) was determined, and the results of TBN were treated by using two-way analysis of variance (ANOVA) test. It was found that
In this paper, two meshless methods have been introduced to solve some nonlinear problems arising in engineering and applied sciences. These two methods include the operational matrix Bernstein polynomials and the operational matrix with Chebyshev polynomials. They provide an approximate solution by converting the nonlinear differential equation into a system of nonlinear algebraic equations, which is solved by using
Wellbore instability is one of the major issues observed throughout the drilling operation. Various wellbore instability issues may occur during drilling operations, including tight holes, borehole collapse, stuck pipe, and shale caving. Rock failure criteria are important in geomechanical analysis since they predict shear and tensile failures. A suitable failure criterion must match the rock failure, which a caliper log can detect to estimate the optimal mud weight. Lack of data makes certain wells' caliper logs unavailable. This makes it difficult to validate the performance of each failure criterion. This paper proposes an approach for predicting the breakout zones in the Nasiriyah oil field using an artificial neural network. It
... Show MoreDue to the advantages over other metallic materials, such as superior corrosion resistance, excellent biocompatibility, and favorable mechanical properties, titanium, its alloys and related composites, are frequently utilized in biomedical applications, particularly in orthopedics and dentistry. This work focuses on developing novel titanium-titanium diboride (TiB2; ceramic material) composites for dental implants where TiB2 additions were estimated to be 9 wt.%. In a steel mold, Ti-TiB2 composites were fabricated using a powder metallurgy technique and sintered for five hours at 1200 °C. Microstructural and chemical properties were analyzed by energy dispersive X-ray spectroscopy (EDX), scanning electron microscopy (SEM), and X-ra
... Show MoreB Saleem, H Alwan, L Khalid, Journal of Engineering, 2011 - Cited by 2
This study was conducted to identify the health status of children's nurseries in the city of Baghdad and to identify improper dietary habits practiced by these children have shown the results of this study that the same proportion of childhood diarrhea disease research and infections
The present study combines UV-Vis spectrophotometry and dispersive liquid-liquid microextraction (DLLME) for the preconcentration and determination of trace level clidinium bromide (Clid) in pharmaceutical preparation and real samples. The method is based on ion-pair formation between Clid and bromocresol green in aqueous solution using citrate buffer (pH = 3). The colored product was first extracted using a mixture of 800 µL acetonitrile and 300 µL chloroform solvents. Then, a spectrophotometric measurement of sediment phase was performed at λ = 420 nm. The important parameters affecting the efficiency of DLLME were optimized. Under the optimum conditions, the calibration graphs of standard -1 (Std.), drug, urine and serum were ranged
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