It is important to maintain every machine affecting the process of making sugar to ensure excellent product quality with minimal losses and to accelerate productivity and profitability targets. The centrifuges are widely used in industry today with some being very difficult and critical for surgery, and the collapse of the engine has the ability to cause expensive damage. One of these is the centrifugal machines, and they are expected to be efficient to produce high-quality sugar. Meanwhile, an efficient diagnostic tool to predict the correct time for centrifugal repair is vibration signal analysis namely by attaching the accelerometer sensor to the location of the centrifugal bearing to produce vibration data that is ready to be analyzed. Still, the process requires sufficient insight and experience. The manual method usually used is complicated and requires a lot of time to obtain results of a centrifugal diagnosis. Therefore, this study was conducted to design an intelligent system to diagnose centrifugal vibrations using Artificial Neural Networks (ANN). The situation is involved in applying and training the concept of vibration analysis from spectrum data to ANN to produce diagnostic results according to the spectrum diagnosis reference. The results obtained were quite good with the largest cross-entropy value of 10.67 having 0% error value with the largest Mean Square Error value being 0.0023 while the smallest regression was 0.993. The test conducted on nine new spectrums produced eight true predictions and one false. The system can provide fairly accurate results in a short time. Classification quality improvement can be made by adding training data.

The use of condensers in air conditioning units is more common in large-capacity units than in ones with a smaller capacity. Air conditioning provides comfort and freshness to an air-conditioned room. It should be noted that each room has a different heat load, which affects the specifications of the condenser used. The accuracy with which appropriate condenser specifications are determined affects the performance of the air conditioner. Thus, considering how important condenser needs are, it is necessary to design condensers with optimal performance, which adhere to proven standards. To achieve this, the design of a condenser should be based on the results of the smallest condenser dimensions of three types of surfaces, as they are intended for a limited place. This condenser design uses the standard dimensions of the Kays and London charts. Data is collected by measuring the results of temperature and enthalpy of a refrigerant at desuperheating and condensation, inlet air temperature, outlet air temperature, refrigerant mass flow rate, and air mass flow rate. The results of the compact condenser design are based on existing data, which is obtained from the smallest design results. The result uses the type of Surface CF-8.72(c) with a heat transfer area of 0.259 m2, a total tube length of 9.5 m, crossing tube length 0.594 m and a pressure drop of 3778 Pascal (Pa) on the side of a tube. This design fulfills the stipulated requirements, as the pressure drop is less than the specified maximum limit in most units.

Moving energy from one machine to another and functioning to reduce speed while increasing torque is the ability of the gearbox. Due to many components and the structure between the components is fairly complex, thus to be able to detect the initial damage, sophisticated methods is needed. Vibration analysis is a method that has been effective in detecting the initial damage that occurs in machine. But it takes time and costs are not small to implement. The purpose of this study is to create an intelligent system capable of detecting gearbox damage based on data obtained from vibration measurements. Merging of two methods of vibration analysis and Bayesian networks is done to be able to design the system with the expected results. A series of multistate nodes are applied to the network and a system review is performed. Results are given and compared with results provided by the manual analysis. The results indicate that the system is feasible and reasonable which can assist inidentifying gearbox damages. This study definitively answers the problem of how to design an expert system capable of replacing the work of an expert on vibration analysis services.

A classifier plays a crucial role in the cement industry. It is in charge of separating coal that has been smoothened out and is ready to be burned although the coal is still rough after going through the grinding process. It takes a long time to burn coal that is not perfectly processed with a classifier. Therefore, it will reduce the amount of cement production, and the factories will release more energy. The closed arrangement and the number of components in the unit classifier requires a sophisticated method to detect damage that occurs early. Vibration analysis is a method that has been effectively employed in detecting the initial damage that occurs to the engine, especially the classifier. This study was aimed at detecting the location of the damage occurring in the classifier by using a vibration signal analysis and by measuring the magnitude of vibration and presenting it to the frequency domain (spectrum) form using Fast Fourier Transform. Engine condition assessment referred to ISO 10816-3 standard in velocity and displacement modes. Based on data spectrum analysis, the dominant damage laid in the unbalanced rotor. Spectrum characteristics of the damage appeared to be in the spectrum line worth 438.01 μm at a 3.5-Hz frequency (1X) radially. This analysis proved to be supported by the decrease in vibration value to 18.65 μm after balancing the Classifier rotor.

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