• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.11a
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• pp.84-88
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• 2008

The roller bearings play an important role not only sustain radial or axial load of system, but carry out a rotatory movement as a various operating conditions. They happen that incipient faults which were caused by excessive load, manufacturing or assembling process's errors and many other reasons are created. The bearing faults make noise and vibration by a continuous collision of rotatory components, which can lower the quality and stability of auto-transmission. Therefore, it is important to detect the early fault as soon as possible. This paper presents a detecting method for the improvement in quality by developing the program which can be used to analyze and predict the vibrational characteristics caused by roller bearing faults. We completed development of the inspection system of vibration by appling the most efficient detecting methods and verified the system's reliability through experiments.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.3
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• pp.268-273
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• 2009

The roller bearings play an important role not only sustain radial or axial load of system, but carry out a rotatory movement as a various operating conditions. They happen that incipient faults which were caused by excessive load, manufacturing or assembling process's errors and many other reasons are created. The bearing faults make noise and vibration by a continuous collision of rotatory components, which can lower the quality and stability of auto-transmission. Therefore, it is important to detect the early fault as soon as possible. This paper presents a detecting method for the improvement in quality by developing the program which can be used to analyze and predict the vibrational characteristics caused by roller bearing faults. We completed development of the inspection system of vibration by applying the most efficient detecting methods and verified the system's reliability through experiments.

• Journal of applied mathematics & informatics
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• v.41 no.6
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• pp.1257-1274
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• 2023

Rotating machines heavily rely on an intricate network of interconnected sub-components, with bearing failures accounting for a substantial proportion (40% to 90%) of all such failures. To address this issue, intelligent algorithms have been developed to evaluate vibrational signals and accurately detect faults, thereby reducing the reliance on expert knowledge and lowering maintenance costs. Within the field of machine learning, Artificial Immune Systems (AIS) have exhibited notable potential, with applications ranging from malware detection in computer systems to fault detection in bearings, which is the primary focus of this study. In pursuit of this objective, we propose a novel procedure for detecting novel instances of anomalies in varying operating conditions, utilizing only the signals derived from the healthy state of the analyzed machine. Our approach incorporates AIS augmented by Dynamic Time Warping (DTW). The experimental outcomes demonstrate that the AIS-DTW method yields a considerable improvement in anomaly detection rates (up to 53.83%) compared to the conventional AIS. In summary, our findings indicate that our method represents a significant advancement in enhancing the resilience of AIS-based novelty detection, thereby bolstering the reliability of rotating machines and reducing the need for expertise in bearing fault detection.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.5
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• pp.835-842
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• 2020

This paper considers a method of reproducing abnormal and fault operation for smart monitoring of thrust bearing used in wave power generation system. In order to develop smart monitoring technology, abnormal and failure data of actual equipment are required. However, it is impossible to artificially break down the actual equipment in operation due to safety and cost. To tackle this problem, a test bed that can secure data through reproduction of a faulty operating environment should be developed. Therefore, in this study, test bed that can reproduce each situation was developed and the operation result was analysis after identifying the situation to be reproduced through the failure factor analysis of the thrust bearing.

• 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.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.2
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• pp.113-121
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• 2023

High-precision lasers and anti-aircraft radars are the main equipment to protect the Korean Peninsula, and require preemptive maintenance before signs of failure. Of the key components in the drive sector, bearings do not have a fault alarm function. Therefore, the technology for diagnosing defects in bearings before the performance degradation of equipment occurs is becoming more important. In this paper, for the experimental analysis, we measured the acceleration of the four sets of the same lot using acceleration sensors. Through periodic measurements, the factors that changed until the bearing stopped rotating were analyzed. To determine the replacement time, the main factors and threshold values of the bearing signal were analyzed. The error of the theoretical and experimental analysis results of the defect frequency was within 2.8 %, and the validity of the theoretical analysis results could be confirmed. Based on the results, it is possible to remotely transmit trouble alerts to users through the system check function.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.32 no.11
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• pp.905-910
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• 2008

Vibration analysis is widely used in machinery diagnosis and the wavelet transform has also been implemented in many applications in the condition monitoring of machinery. In contrast to previous applications, this paper examines whether acoustic signal can be used effectively along vibration signal to detect the various local fault, in local fault of gearboxes using the wavelet transform. Moreover, envelop analysis is well known as useful tool for the detection of rolling element bearing fault. In this paper, a acoustic emission (AE) sensor is employed to detect gearbox damage by installing them around bearing housing at driven-end side. Signal processing is conducted by wavelet transform and enveloping to detect her fault all at once gearbox using AE signal.

• Journal of the Korean Society of Safety
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• v.21 no.1 s.73
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• pp.6-14
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• 2006

The development of automatic production systems have required intelligent diagnostic and monitoring functions to overcome system failure and reduce production loss by the failure. In order to perform accurate operations of the intelligent system, implication about total system failure and fault analysis due to each mechanical component failures are required. Also solutions for repair and maintenance can be suggested from these analysis results. As an essential component of a mechanical system, a bearing system is investigated to define the failure behavior. The bearing failure is caused by lubricant system failure, metallurgical deficiency, mechanical condition(vibration, overloading, misalignment) and environmental effects. This study described slab transfer equipment fault train due to stress variation and metallurgical deficiency from lubricant failure by using FTA.

• Transactions of the Korean Society of Automotive Engineers
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• v.25 no.3
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• pp.273-286
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• 2017

We develop an inspection method for bearing faults with a rapid change in the rotation speed and present indexes for the pass/fail inspection. At the end of line, impulse noises generated by the operation of machines and conveyors may distort the inspection results. In this paper, we present robust inspection indexes for bearing faults under impulse noises, by taking into account fault signals having pulse train. Using logistic regression, we optimize the pass/fail criterion for each index and evaluate the performance of the inspection indexes based on the total error rate.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.3
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• pp.83-92
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• 2008

This paper develops the theory for a fault-tolerant, permanent magnet biased, homopolar magnetic bearing. If some of the coils or power amplifiers suddenly fail, the remaining coil currents change via a novel distribution matrix such that the same magnetic forces are maintained before and after failure. Lagrange multiplier optimization with equality constraints is utilized to calculate the optimal distribution matrix that maximizes the load capacity of the failed bearing. Some numerical examples of distribution matrices are provided to illustrate the theory. Simulations show that very much the same dynamic responses (orbits or displacements) are maintained throughout failure events (up to any combination of 3 coils failed for the 6 pole magnetic bearing) while currents and fluxes change significantly. The overall load capacity of the bearing actuator is reduced as coils fail. The same magnetic forces are then preserved up to the load capacity of the failed.