• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.8
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• pp.2061-2068
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• 1993

Condition monitoring technology is of great importance for the maintenance of complex machinery in view of its early monitoring of the abnormal condition and the protection against failure. Several methods have been used for the detection of failure of journal bearings, one of the main elements of mechanical system. The methods most frequently used are vibration and temperature monitoring, but these are unable to monitor the wear conditions exactly. In this study, an ultrasonic measument method, one of the non-destructive testing methods, was introduced as the monitoring technology. Furtermore a pattem recognition method was applied to analyze the ultrasonic signal. The monitoring system using the pattern recognition method is composed of digital signal processing units and uses Hamming net algorithm for the recognition of ultrasonic waves. From the journal bearing wear test, the occurrence of adhesive wear of the white metal in rubbing contact with the shaft was exactly detected by this system, and the wear status of the journal bearing was monitored by measuring the wear thickness.

• Journal of Applied Reliability
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• v.11 no.4
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• pp.331-342
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• 2011

The transmission of tracked vehicles performs complex functions as steering, shifting, braking, etc. and the system level life time has been a key influenced by the number of sub-parts like as gear assembly, torque converter, clutches, bearings and so on. In particular, the mechanical type steering system in tracked vehicle has impact shock torques in steering shift and those kind of shock torques can effect on the durability of many sub-parts in power train system. The field failure modes of gear assembly, steering assembly and the bearings of output shaft appear as a very complex phenomenon. In this study, the actual failure, which may occur in field, of the transmission was investigated comprehensively and that the endurance test on the resulting output shaft bearing failure analysis and life assessment was performed. Life time test method used in this study, developed for the purpose of the internal usage, and under these testing techniques the impact of the each bearing damage, which used in tracked vehicle transmission left / right outputs of different structures, was analyzed.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.17 no.1 s.118
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• pp.24-32
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• 2007

This paper presents a finite element method to analyze the free vibration of a flexible HDD composed of the spinning disk-spindle system with fluid dynamic bearings(FDBs), the head-suspension-actuator with pivot bearings, and the base plate with complicated geometry. Experimental modal testing shows that the proposed method well predicts the vibration characteristics of a HDD. This research also shows that even the vibration motion of the spinning disk corresponding to half-speed whirl and the pure disk mode are transferred to a head-suspension-actuator and base plate through the air bearing and the pivot bearing consecutively. The proposed method can be effectively extended to investigate the forced vibration of a HDD and to design a robust HDD against shock.

• Journal of Power Electronics
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• v.8 no.2
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• pp.181-191
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• 2008

The task performed by induction motors grows increasingly complex in modern industry and hence improvements are sought in the field of fault diagnosis. It is essential to diagnose faults at their very inception, as unscheduled machine down time can upset critical dead lines and cause heavy financial losses. Artificial intelligence (AI) techniques have proved their ability in detection of incipient faults in electrical machines. This paper presents an application of AI techniques for the detection of inter-turn insulation and bearing wear faults in single-phase induction motors. The single-phase induction motor is considered a proto type model to create inter-turn insulation and bearing wear faults. The experimental data for motor intake current, rotor speed, stator winding temperature, bearing temperature and noise of the motor under running condition was generated in the laboratory. The different types of fault detectors were developed based upon three different AI techniques. The input parameters for these detectors were varied from two to five sequentially. The comparisons were made and the best fault detector was determined.

• Smart Structures and Systems
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• v.29 no.3
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• pp.513-522
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• 2022

Due to the importance of accurate analysis of bearing capacity in civil engineering projects, this paper studies the efficiency of two novel metaheuristic-based models for this objective. To this end, black hole algorithm (BHA) and multi-verse optimizer (MVO) are synthesized with an artificial neural network (ANN) to build the proposed hybrid models. Based on the settlement of a two-layered soil (and a shallow footing) system, the stability values (SV) of 0 and 1 (indicating the stability and failure, respectively) are set as the targets. Each model predicted the SV for 901 stages. The results indicated that the BHA and MVO can increase the accuracy (i.e., the area under the receiving operating characteristic curve) of the ANN from 94.0% to 96.3 and 97.2% in analyzing the SV pattern. Moreover, the prediction accuracy rose from 93.1% to 94.4 and 95.0%. Also, a comparison between the ANN's error decreased by the BHA and MVO (7.92% vs. 18.08% in the training phase and 6.28% vs. 13.62% in the testing phase) showed that the MVO is a more efficient optimizer. Hence, the suggested MVO-ANN can be used as a reliable approach for the practical estimation of bearing capacity.

• Journal of the Korean Society for Nondestructive Testing
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• v.14 no.2
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• pp.122-131
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• 1994

Journal bearings in the rotating machineries are vulnerable to the contamination or the insufficient supply of lubricating oil, which is likely to be the cause of unexpected shutdown or malfunction of these systems. Various destructive and nondestructive testing methods had been used for the reduction of maintenance cost and the operational safety problems due to the accidents related to bearing damages. In this experimental approach, acoustic emission monitoring is employed to the detection of incipient failure caused by intervention of foreign particles most probable in the journal bearing systems. Experimental schedules for the intervention of foreign particles was composed to be more quantitative and systematic than last study in consideration of minimum oil film thickness and particle size. The experiment was conducted under such designed conditions as inserting alumina particles to the lubrication layer in the simulated journal bearing system. Several parameters such as AE rms level, waveform, AE energy distribution and other AE event parameters are used for analysis and characterization of damage source. The results showed that the history of damage was well correlated with the changes of AE rms level and the type of damage source signal can be verified using other informations such as waveform, distributions of AE parameters etc.

• Structural Engineering and Mechanics
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• v.56 no.4
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• pp.695-705
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• 2015

Distressed structures require necessary remedial measures in order to restore their original structural properties like strength and stiffness. Validating the effectiveness of the proposed qualitative remedial measure experimentally is of utmost importance as there is no well-established analytical method to verify the effectiveness of the same quantitatively. Prototype testing which would have been the best option for this purpose would not only prove costly but also be associated with numerous practical difficulties; hence model testing is resorted as the only option for the purpose. This paper presents one such typical experimental study on the structural behavior of a distressed bridge, mainly observed in the form of prominent tilt in the bearing plate in transverse and longitudinal direction on downstream side. The main focus of the proposed experimental investigation is to assess the structural behavior particularly the load carrying capacity. The extent of deformation of some models with specific structural arrangements and some models with specific need based remedial measures were also studied. This study also assessed the contribution of each remedial measure towards restoration individually and collectively.

• Geomechanics and Engineering
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• v.38 no.4
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• pp.439-453
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• 2024

The research findings of two nonlinear machine learning and soft computing models- the Cuckoo optimization algorithm (COA) and the Teaching-learning-based optimization (TLBO) in combination with artificial neural network (ANN)-are presented in this article. Detailed finite element modeling (FEM) of a shallow footing on two layers of cohesionless soil provided the data sets. The models are trained and tested using the FEM outputs. Additionally, various statistical indices are used to compare and evaluate the predicted and calculated models, and the most precise model is then introduced. The most precise model is recommended to estimate the solution after the model assessment process. When the anticipated findings are compared to the FEM data, there is an excellent agreement, which indicates that the TLBO-MLP solutions in this research are reliable (R²=0.9816 for training and 0.99366 for testing). Additionally, the optimized COA-MLP network with a swarm size of 500 was observed to have R² and RMSE values of (0.9613 and 0.11459) and (0.98017 and 0.09717) for both the normalized training and testing datasets, respectively. Moreover, a straightforward formula for the soft computing model is provided, and an excellent consensus is attained, indicating a high level of dependability for the suggested model.

• Proceedings of the Korean Geotechical Society Conference
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• 2002.10a
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• pp.334-341
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• 2002

Nowadays, owing to the development of analysis techniques for PDA test, we can evaluate the serious damage of pile during driving in site. In this study, we checked the damage of pile by pulling it out after evaluation of the pile damage during driving by PDA testing. After that, almost damaged pile was checked and the outline depth of damage could be verified. To increase the quality of driven pile, we have to increase the number of PDA testing for drivability analysis with checking the damage especially for preliminary pile driving and dynamic load test for checking the bearing capacity and consider the application of driving by energy monitoring.

• Tribology and Lubricants
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• v.36 no.5
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• pp.279-289
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• 2020

In this paper, a method is proposed for establishing an approximate prediction model of rotor-dynamics through modal testing. In particular, the proposed method is applicable to systems that cannot be established according to conventional methods owing to the absence of information regarding the dimensions and material of the rotor-bearing system. The proposed method is demonstrated by employing a motor dynamometer driven by a 1 MW class induction motor without dimension and material information. The proposed method comprises a total of seven steps, wherein an initial model is established by incorporating approximate dimensions and material information, and the model is improved on the basis of the natural frequency characteristics of the system. During model improvement, the modification factor is introduced for adjusting the elastic modulus and shear modulus of the system. Analysis of critical speed and imbalance response indicates that the separation margin is 67% and the maximum vibration amplitude is less than the amplitude limit of 0.032 mm under the API 611 standard, which means that the motor dynamometer can stably operate at a rated speed of 1800 rpm. Hence, the obtained results validate the feasibility of the proposed method. Furthermore, for broad usage, it is necessary to accordingly apply and validate the proposed method for various rotor-bearing systems.