Components In Industrial Machines Research Articles

Investigation of the influence of turbulence to tribological performance on smooth and multistep journal bearing with hydrodynamics simulation

A bearing is an element that maintains the relative motion between two components in industrial machines, while a journal bearing is often used to maintain the shaft constantly rotating on its axis. Generally, an increase in journal bearings’ performance is essential for industrial development. An increase in engine speed affects the distribution of pressure, temperature, and acoustics. Scientists have proposed various methods to analyze pressure distribution, load-bearing capacity, acoustic power, friction force, cavitation, and elastic phenomena to improve journal bearings’ tribological performance. Therefore, this study aimed to discuss the differences in the flow type modeling between smooth and multistep journal bearings. The results indicated that tribological performance increased significantly in turbulent flow in smooth and multistep journal bearings. The simulation results showed a difference between k-epsilon turbulent flow modeling and k-omega flow modeling.

Micro- to Nano- and from Surface to Bulk: Influence of Halogen-Free Ionic Liquid Architecture and Dissociation on Green Oil Lubricity

Four nonhalogenated ionic liquids (ILs) based on the same phosphonium cation are investigated in terms of the anion suitability for enhancing the lubricity of a biodegradable oil. For all test conditions, typical for industrial machine components, the lubrication is shown to be governed by nonsacrificial films formed by the physisorption of ionic species on the tribo-surfaces. The anionic structure appears to have an important role in the formation of friction modifying films. The orthoborate ILs exhibit the formation of robust ionic boundary films, resulting in reduced friction and better wear protection. On the contrary, the surface adsorption of phosphinate and phosphate ILs appears to antagonistically disrupt the intrinsic lubrication properties of the biodegradable oil, resulting in high friction and wear. Through additional investigations, it is postulated that the higher dissociation of orthoborate ILs in the biodegradable oil allows the formation of hierarchical and electrostatically overscreened layer structures with long-range order, whereas the ILs with phosphate and phosphinate anions exhibit low dissociation in biodegradable oil, possibly due to the ion pairs being surrounded by a hydrocarbon halo, which presumably results in weak adsorption to form a mixed interfacial layer with no long-range order.

Laju Korosi Atmosferik Baja Konstruksi di Area Pabrik PT. Karya Tanah Subur

Abstract Environmental pollution that can cause atmospheric corrosion is the exhaust gas (exhaust fumes) generated from operating the palm oil industry. This atmospheric corrosion will reduce the volume and material mass of a machine component, so that if it reaches certain conditions the strength of the material will result in failure of industrial machine components. The corrosion rate of the construction steel can be measured using the weight loss method according to the ASTM G-50 standard and the ASTM G-1 standard. The research was conducted for one year and data collection was taken every month, the research location was at PT Agro Sinergi Nusantara Batee Puteh. The results of the study, the highest corrosion rate occurred in strip steel reaching 0.57 mpy in October and the lowest corrosion rate occurred for angled steel in September and cylindrical steel in May with a corrosion rate of 0.06 mpy. Keywords— atmospheric corrosion, ASTM G-50, ASTM G-1, PT. KTS

Tingkat Laju Korosi Atmosferik Baja Konstruksi di Lingkungan Pabrik Kelapa Sawit

Environmental pollution that can cause atmospheric corrosion is the exhaust gas (exhaust fumes) generated from operating the palm oil industry. This atmospheric corrosion will reduce the volume and material mass of a machine component, so that if it reaches certain conditions the strength of the material will result in failure of industrial machine components. The corrosion rate of the construction steel can be measured using the weight loss method according to the ASTM G-50 standard and the ASTM G-1 standard. The research was conducted for one year and data collection was taken every month, the research location was at PT Agro Sinergi Nusantara Batee Puteh. The results of the study, the highest corrosion rate occurred in strip steel reaching 0.57 mpy in October and the lowest corrosion rate occurred for angled steel in September and cylindrical steel in May with a corrosion rate of 0.06 mpy. Keywords— atmospheric corrosion, palm oil industry, ASTM G-50, ASTM G-1, corrosion rate

Metallurgical investigation of failure analysis in industrial machine components

Bearings and chains are important industrial machine components used in wide range of heavy cyclical load applications. Main aim of this research work is to conduct a metallurgical investigation of failure analysis in failed bearing and chain parts. The failure analysis is carried out by chemical analysis, optical metallography, hardness analysis and scanning electron microscopy (SEM) analysis. In failed bearing inner race ways ridged marks were perceived due to electrical pitting and outer race ball groove worn-out on one side due to axial thrust load. Bearing balls have surface spalling due to contact fatigue. In chain failed bush multiple crack fronts are observed in longitudinal and circumferential. Crack geometry and fracture surface indicates failure through impact fatigue mode. The fracture surface indicated quasi cleavage fracture mode near the crack origins. The results in both investigations suggest that the major common causes of failures are due to over load, fatigue, wear and unsuitable material selection. MoS2coating, thin dense chromium coating and thin hard titanium nitride coating in all bearing components has been suggested to further improve the wear resistance and fatigue strength. Material SAE 8620 alloy steel for chain bush has been suggested to further improve life to withstand in the impact load conditions.

Vibration Signature of Roller Bearing’s Faults

Centrifugal pumps are widely used in many industrial applications. Defective pumps cause higher rate of energy loss with related overall performance degradation. Mechanical components fail due to decrease in pump’s strength or an increase in the force acting on it. Rolling bearings are one of the major components of the centrifugal pumps. Bearings are critical mechanical components in industrial machines. Bearing failure is often attributed to be one of the major causes of breakdown. Early detection of undesired conditions during the operation of the centrifugal pump has become very important in order to avoid consequential damages. Early detection is also vital for increasing availability and reliability. Automated health monitoring can reduce the outage time and repair costs. Using vibration analysis technique to monitor and evaluate the health of the machines has become an important technique in this field. In this research, we studied the vibration signatures of healthy and faulty conditions of the bearings of a pumping system. The pumping system consists of centrifugal pump and electrical motor. The vibration signatures analyze the bearing status with respect to the normal operation. The paper shows how to identify if there is a problem in the outer race of the Bearing Pass Frequency Outer Race (BPFO).

Applying machine learning to AHP multicriteria decision making method to assets prioritization in the context of industrial maintenance 4.0

The increasing competition among industries has led to the emergence of numerous tools and methods to support decision making focused on assets maintenance in a company, since ensuring good maintenance is directly linked with greater reliability and uptime for equipment, reducing losses in production processes and consequently increasing profitability. This work aims to use Machine Learning (ML) algorithms - Bayesian Networks (BN) and attribute relevance analysis (ARA), implemented in the Weka® platform, to process a dataset of event logs failure of industrial machine components. The approach aims to use the conditional probability relations generated by the BN and the ranking of criteria relevance for the design of an AHP decision-making model within the scope of industrial maintenance to prioritize which components of a specific machine are more susceptible to failures. The proposed integration mechanism aims to bring greater reliability to the weights assigned to the criteria of the AHP model, and consequently, a more accurate decision support. The results showed that the AHP model generated from a Bayesian Network is consistent with the conditional probabilities estimated by the BN, giving robustness to the decision sphere in the context of industrial maintenance. This AHP model can serve as a basis to be complemented by qualitative analysis criteria according to the need of the individual specialist, allowing the construction of strategic maintenance action plans.

Adaptive Multiclass Mahalanobis Taguchi System for Bearing Fault Diagnosis under Variable Conditions.

Bearings are vital components in industrial machines. Diagnosing the fault of rolling element bearings and ensuring normal operation is essential. However, the faults of rolling element bearings under variable conditions and the adaptive feature selection has rarely been discussed until now. Thus, it is essential to develop a practicable method to put forward the disposal of the fault under variable conditions. Considering these issues, this paper uses the method based on the Mahalanobis Taguchi System (MTS), and overcomes two shortcomings of MTS: (1) MTS is an effective tool to classify faults and has strong robustness to operating conditions, but it can only handle binary classification problems, and this paper constructs the multiclass measurement scale to deal with multi-classification problems. (2) MTS can determine important features, but uses the hard threshold to select the features, and this paper selects the proper feature sequence instead of the threshold to overcome the lesser adaptivity of the threshold configuration for signal-to-noise gain. Hence, this method proposes a novel method named adaptive Multiclass Mahalanobis Taguchi system (aMMTS), in conjunction with variational mode decomposition (VMD) and singular value decomposition (SVD), and is employed to diagnose the faults under the variable conditions. Finally, this method is verified by using the signal data collected from Case Western Reserve University Bearing Data Center. The result shows that it is accurate for bearings fault diagnosis under variable conditions.

Hardness and impact behaviour of aluminium metal matrix composite

This research paper deals with the fabrication procedure and the testing process of metal matrix composite material comprising of Aluminium and Silicon Carbide in addition with Carbon Nanotubes. Aluminium composites are preferred in manufacturing of various industrial machine components and automobile parts due to their favourable properties like low density which thereby results in their light weight, high strength and superior malleability comparatively to iron carbon alloys like steel and cast iron. When Silicon Carbide is added it helps in furthering the thermal conductivity and melting point properties of Aluminium in addition to improving its hardness.The addition of Carbon Nanotubes potentially helps in further improving the tensile strength of the metal matrix composite. In this study four samples of different compositions of Silicon Carbide are prepared and studied. The fabrication process is done by Stir casting carries out by adding the required additives into the molten mixture of aluminium followed by continuous stirring. After solidification for a period of time the samples are cut according to standard dimensions for carrying out the testing procedures. The various test procedures like hardness and impact tests were conducted to study the mechanical properties of obtained metal matrix composite. The various results inferred from the tests are analysed and subjected to comparison with each other in this study.

A SAW wireless sensor network platform for industrial predictive maintenance

Predictive maintenance predicts the system health, based on the current condition, and defines the needed maintenance activities accordingly. This way, the system is only taken out of service if direct evidence exists that deterioration has actually taken place. This increases maintenance efficiency and productivity on one hand, and decreases maintenance support costs and logistics footprints on the other. We propose a system based on wireless sensor network to monitor industrial systems in order to prevent faults and damages. The sensors use the surface acoustic wave technology with an architecture composed of an electronic interrogation device and a passive sensor (without energy at the transducer) which is powered by the radio frequency transmitted by the interrogation unit. The radio frequency link transfers energy to the sensor to perform its measurement and to transmit the result to the interrogation unit—or in a description closer to the implemented, characterize the cooperative target cross section characteristics to recover the physical quantity defining the transducer material properties. We use this sensing architecture to measure the temperature of industrial machine components and we evaluate the robustness of the method. This technology can be applied to other physical parameters to be monitored. Captured information is transmitted to the base station through multi-hop communications. We also treat interferences involved in both interrogator to interrogator and sensor to interrogator communications.

SVD and Hankel matrix based de-noising approach for ball bearing fault detection and its assessment using artificial faults

Ball bearings remain one of the most crucial components in industrial machines and due to their critical role, it is of great importance to monitor their conditions under operation. However, due to the background noise in acquired signals, it is not always possible to identify probable faults. This incapability in identifying the faults makes the de-noising process one of the most essential steps in the field of Condition Monitoring (CM) and fault detection. In the present study, Singular Value Decomposition (SVD) and Hankel matrix based de-noising process is successfully applied to the ball bearing time domain vibration signals as well as to their spectrums for the elimination of the background noise and the improvement the reliability of the fault detection process. The test cases conducted using experimental as well as the simulated vibration signals demonstrate the effectiveness of the proposed de-noising approach for the ball bearing fault detection.

Preventive maintenance by vibratory analysis: case study

majority of the industrial machine components have to satisfy to the requirements of the high quality and incessantly increasing; so the machines maintaining in good state during the production has became a fundamental point for a product or a company success. Through the measure of the predictive maintenance, it is possible to satisfy these complex requirements with success and to reduce the costs of maintenance. However, an optimization of the maintenance is realized by a continued monitoring of degradation, by way of a control system on line On Line, which can respond to industrial imperatives and which provides to define just the necessary at the level of interventions, The machine can't be serviced except if only its state requires.For an effective maintenance, it takes precise and reliable measures. Nevertheless, the experience has shown that the vibratory measure is the most reliable parameter that gives the precociously and with a best ways the deterioration state of a turnable machine. It provides to identify the efforts as soon as they appear, before of an irreversible damage, it also provides, after analysis, to deduce the origin and to estimate the breakdown risks.In this context, a study had done inside a workshop of a phosphoric office, which adopt a continued surveillance (On-Line), in the hope of doing an optimal process of a conditional maintenance which can be developed in time toward an idealized by a vibratory indicator accomplishing satisfactory results.