• Advances in Computational Design
• /
• v.8 no.3
• /
• pp.255-271
• /
• 2023

The gear drive of a combat aircraft engine is responsible for power transmission to the different accessories necessary for the engine's operation. Incorrect power transmission can occur due to the presence of failure modes in the gears like bending fatigue, pitting, adhesive wear, scuffing, abrasive wear and polished wear etc. Fault diagnosis of the gear drive is necessary to get an early indication of failure of the gears. The present research is to develop an algorithm using different vibration signal processing techniques on industrial vibration acquisition systems to establish gear fault diagnosis architecture. The signal processing techniques have been used to extract various feature vectors in the development of the fault diagnosis architecture. An open-source dataset of other gear fault conditions is used to validate the developed architecture. The results is a basis for development of artificial intelligence based expert systems for gear fault diagnosis of a combat aircraft engine.

• Journal of Drive and Control
• /
• v.18 no.1
• /
• pp.24-30
• /
• 2021

There is an increasing interest in condition-based maintenance for the prevention of economic loss due to failure. Moreover, immense research is being carried out in related technologies in the field of construction machinery. In particular, data-based failure diagnosis methods that employ AI (machine & deep learning) algorithms are in the spotlight. In this study, we have focused on the failure diagnosis and mode classification of reduction gear of excavator's travel device by using the AI algorithm. In addition, a remote monitoring system has been developed that can monitor the status of the reduction gear by using the developed diagnosis algorithm. The failure diagnosis algorithm was performed in the process of data acquisition of normal and abnormal under various operating conditions, data processing and analysis by the wavelet transformation, and learning. The developed algorithm was verified based on three-evaluation conditions. Finally, we have built a system that can check the status of the reduction gear of travel devices on the web using the Edge platform, which is embedded with the failure diagnosis algorithm and cloud.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2000.06a
• /
• pp.1114-1118
• /
• 2000

Impulsive sound and vibration signals in gear are often associated with their faults. Thus these impulsive sound and vibration signals can be used as indicators in the diagnosis of gear fault. The early detection of impulsive signal due to gear fault prevents from complete failure in gear. However it is often difficult to make objective measurement of impulsive signals because of background noise signals. In order to ease the detection of impulsive signals embedded in background noise, we enhance the impulsive signals using adaptive signal processing.

• Journal of the Korean Society of Industry Convergence
• /
• v.23 no.5
• /
• pp.825-833
• /
• 2020

Currently, the K-Water uses various valves that can be remotely controlled for optimal water management. Valve system fault can be classified into rotor defects, stator defects, bearing defects, and gear defects of induction motors. If the valve cannot be operated due to a gear fault, the water management operation can be greatly affected. For effective water management, there is an urgent need for preemptive repairs to determine whether gear is damaged through failure prediction diagnosis.. Recently, deep learning algorithms are being applied for valve failure diagnosis. However, the method currently applied has a disadvantage of attaching a vibration sensor to the valve. In this paper, propose a new algorithm to determine whether a fault exists using a convolutional neural network (CNN) based on the voltage and current information of the valve without additional sensor mounting. In particular, a normalized Lisasjous diagram was used to maximize the fault classification performance in the CNN-based diagnostic system.

• Journal of Drive and Control
• /
• v.16 no.3
• /
• pp.33-41
• /
• 2019

This study presents the development of a lifetime test bench for the strain wave reducer which is a precision gear reducer of the robot to realize fault diagnosis and failure prognostics. To this end, the lifetime test bench was designed to detect the vertical forward/reverse direction rotation load. Through the lifetime test bench, it is possible to apply the same load spectrum from robot working scenarios. We developed a data integration gateway for fault data collection. Through the development of dedicated software for fault diagnosis and failure prognostics, these data from vibration, noise and temperature sensors were collected and analyzed along with the operation of the lifetime evaluation.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.15 no.6
• /
• pp.2026-2036
• /
• 1991

An expert system is developed which can diagnose the causes and remedies of the failures of power transmission gears. The basic components of the expert system are knowledge base, inference engine, and working memory. The knowledges in knowledge base are classified into the knowledges for determining the failure types and for diagnosis of causes and remedies of the failures. The former is represented hierarchically into the main category of eleven groups by rules and the sub category of twenty four groups by facts, while the later is represented by facts according to the each group of knowledges. In the inference engine some considerations are implemented, i.e., the backward chaining method and depth first search to determine the category of the failures, the meta-knowledges to shorten the search space, the certainty factor to evaluate the reliability of result, and the unification strategy to diagnose the causes and remedies of the failures. The working memory is established to hold the results during inference temporarily. In addition, knowledge acquisition facility, explanation facility, and user interface are included for the usefulness of user. This expert system is written with the PROLOG programming language on IBM-PC compatible computer operated by MS-DOS and be executed alone.

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

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
• /
• 1998.04a
• /
• pp.321-327
• /
• 1998

This paper presented a case study on the application of on-line wear monitoring technique to a high duty air-turbo-compressor system. Main objects monitored were a gear unit and metal bearings, both shown frequent troubles due to the severe operation conditions at heavy dynamic load. The air-turbo-compressor system needs secure condition monitoring because it is one of the main utilities in steel making industry. Temperature and vibration characteristics have been mainly on-line monitored in this system for a predictive maintenance; however, it has been shown that they are not fairly good enough to give an early warning prior to the machine failure. In this work, an on-line Opto Magnetic Detector(OMD) was implemented for an on-line wear monitoring, which quantitatively measured the contamination level of both ferrous and non-ferrous wear particles by detecting the change in optical density of used oil. Results showed that the application of on-line OMD system was satisfactory in diagnosis of the machine system.

• Journal of the Semiconductor & Display Technology
• /
• v.21 no.4
• /
• pp.65-70
• /
• 2022

In manufacturing and semiconductor industries, transfer robots increase productivity through accurate and continuous work. Due to the nature of the semiconductor process, there are environments where humans cannot intervene to maintain internal temperature and humidity in a clean room. So, transport robots take responsibility over humans. In such an environment where the manpower of the process is cutting down, the lack of maintenance and management technology of the machine may adversely affect the production, and that's why it is necessary to develop a technology for the machine failure diagnosis system. Therefore, this paper tries to identify various causes of failure of transport robots that are widely used in semiconductor automation, and the Prognostics and Health Management (PHM) method is considered for determining and predicting the process of failures. The robot mainly fails in the driving unit due to long-term repetitive motion, and the core components of the driving unit are motors and gear reducer. A simulation drive unit was manufactured and tested around this component and then applied to 6-axis vertical multi-joint robots used in actual industrial sites. Vibration data was collected for each cause of failure of the robot, and then the collected data was processed through signal processing and frequency analysis. The processed data can determine the fault of the robot by utilizing machine learning algorithms such as SVM (Support Vector Machine) and KNN (K-Nearest Neighbor). As a result, the PHM environment was built based on machine learning algorithms using SVM and KNN, confirming that failure prediction was partially possible.