• 한국멀티미디어학회논문지
• /
• 제25권11호
• /
• pp.1547-1556
• /
• 2022

As mechanical facilities are interacting with each other, the failure of some equipment can affect the entire system, so it is necessary to quickly detect and diagnose the abnormality of mechanical equipment. This study proposes a deep learning model that can effectively diagnose abnormalities in rotating machinery and equipment. CNN is widely used for feature extraction and LSTMs are known to be effective in learning sequential information. In LSTM, the number of parameters and learning time increase as the length of input data increases. In this study, we propose a method of segmenting an input segment signal into shorter-length sub-segment signals, sequentially inputting them to CNN through a time-distributed method for extracting features, and inputting them into LSTM. A failure diagnosis test was performed using the vibration data collected from the motor for ventilation equipment installed at the urban railway station. The experiment showed an accuracy of 99.784% in fault diagnosis. It shows that the proposed method is effective in the fault diagnosis of rotating machinery and equipment.

• 항공우주시스템공학회지
• /
• 제16권6호
• /
• pp.129-137
• /
• 2022

최근 항공기용 구동기는 전기식 구동기의 높은 정밀도 및 유지관리의 용이성 등으로 인해 기존 유압 중심의 기계시스템에서 전기구동 중심(More/All Electric)으로 기술이 변화하고 있다. 따라서 항공기의 전기 전동기 고장은 치명적인 결함을 넘어 인명피해로도 이어질 수 있다. 전기 전동기의 고장진단은 항공기의 안전성을 보장하는데 필수적인 요소이다. 이에 따라 효율적이고 적절한 고장진단이 요구되며 이러한 고장진단 기술 연구가 활발히 이루어지고 있다. 본 논문에서는 전기 전동기 중 영구자석 동기전동기의 고장 유형과 고장진단 기술 동향에 대해 소개하고 분석한다.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2007년도 제38회 하계학술대회
• /
• pp.2032-2033
• /
• 2007

There has been increasing interests of condition monitoring and diagnosis for electric equipment, which lead to the development of this system domestic and abroad. In the past, operators' interest was how to quickly repair and restore the electric equipments after failure. But, due to the North American Blackout in 2003 and the aging of equipments, users have paid attention to the condition based monitoring of electric equipment to prevent a fault like outrage. GIS-PDS("GIS Preventive and Diagnosis System") requiresa large amount of measurement data with timeliness for monitoring and analysis of real-time state of GIS(Gas Insulated Switchgear). We developed the timeliness-guaranteed communication device for GIS-PDS.

• 한국정밀공학회지
• /
• 제11권5호
• /
• pp.110-123
• /
• 1994

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

• Journal of Mechanical Science and Technology
• /
• 제20권5호
• /
• pp.583-590
• /
• 2006

It is hard to inspect the state of lubrication of an automobile transmission visually. Thus, it is necessary to develop a new inspection method. Wear debris can be collected from the lubricants of an operating transmission of an automobile, and its morphology will be directly related to the friction condition of the interacting materials from which the wear debris originated in the lubricated transmission. In this study, wear debris in lubricating oil are extracted by membrane filter $(0.45{\mu}m)$, and the quantitative values of shape parameters of wear debris are calculated by digital image processing. These shape parameters are studied and identified by an artificial neural network algorithm. The results of the study may be applicable to the prediction and diagnosis of the operating condition of transmission gear.

• Journal of Chest Surgery
• /
• 제27권12호
• /
• pp.1031-1035
• /
• 1994

Mechanical valve thrombosis is a serious and potential lethal complication unless early diagnosis & prompt therapy are made. We have been experienced 3 cases of valve thrombosis. From Aug. 1988 to July 1994, reoperations of mitral valve replacement [MVR] with mechanical prostheses [all mitral] were performed in three patients[2 men, 1 woman] due to valve thromboses. All three patients were diagnosed by means of cineradiography. Preoperative status of was shock status and he was applied intra-aortic balloon pump [IABP]. All three cases of prosthetic valve failure [PVF] were treated by Redo-MVR. Time intervals of reoperations were 5months, 40months, and 35months, respectively. In all cases, valve thromboses were excised successfully. Cineradiography provided an accurate diagnosis in all cases, which was utilized as safe, reliable & noninvasive imaging modalities. There were no operative death & complication. All three patients were fully recovered and returned to their employements, and active lives.

• 비파괴검사학회지
• /
• 제32권3호
• /
• pp.291-295
• /
• 2012

In the industrial field, real-time monitoring system like a fault early detection is very important. For this, the infrared thermography technique as a new diagnosis method is proposed. This study is focused on the damage detection and temperature characteristic analysis of ball bearing using the non-destructive infrared thermography method. In this paper, thermal image and temperature data were measured by a Cedip Silver 450 M infrared camera. Based on the results, the temperature characteristics under the conditions of normal, loss lubrication, damage, dynamic loading, and damage under loading were analyzed. It was confirmed that the infrared technique is very useful for the detection of the bearing damage.

• Structural Engineering and Mechanics
• /
• 제88권6호
• /
• pp.569-581
• /
• 2023

This paper introduces a system to detect and diagnose anomalies in pumped storage hydropower plants. We collect data from various types of sensors, including those monitoring temperature, vibration, and power. The data are classified according to the operation modes (pump and turbine operation modes) and normalized to remove the influence of the external environment. To detect anomalies and diagnose their types, we adopt a multivariate normal distribution analysis by learning the distribution of the normal data. The feasibility of the proposed system is evaluated using actual monitoring data of a pumped storage hydropower plant. The proposed system can be used to implement condition monitoring systems for other plants through modifications.

• 산업경영시스템학회지
• /
• 제40권1호
• /
• pp.57-64
• /
• 2017

In order to reduce damages to major railroad components, which have the potential to cause interruptions to railroad services and safety accidents and to generate unnecessary maintenance costs, the development of rolling stock maintenance technology is switching from preventive maintenance based on the inspection period to predictive maintenance technology, led by advanced countries. Furthermore, to enhance trust in accordance with the speedup of system and reduce maintenances cost simultaneously, the demand for fault diagnosis and prognostic health management technology is increasing. The objective of this paper is to propose a highly reliable learning model using various machine learning algorithms that can be applied to critical rolling stock components. This paper presents a model for railway rolling stock component fault diagnosis and conducts a mechanical failure diagnosis of motor components by applying the machine learning technique in order to ensure efficient maintenance support along with a data preprocessing plan for component fault diagnosis. This paper first defines a failure diagnosis model for rolling stock components. Function-based algorithms ANFIS and SMO were used as machine learning techniques for generating the failure diagnosis model. Two tree-based algorithms, RadomForest and CART, were also employed. In order to evaluate the performance of the algorithms to be used for diagnosing failures in motors as a critical railroad component, an experiment was carried out on 2 data sets with different classes (includes 6 classes and 3 class levels). According to the results of the experiment, the random forest algorithm, a tree-based machine learning technique, showed the best performance.

• 한국안전학회지
• /
• 제19권1호
• /
• pp.23-30
• /
• 2004

Modern production systems are very complex by request of automation, and failure modes that occur in thisautomatic system are very various and complex. The efficient fault diagnosis for these complex systems is essential for productivity loss prevention and cost saving. Traditional fault diagnostic system which perforns sequential fault diagnosis can cause catastrophic failure during diagnosis when fault propagation is very fast. This paper describes the Real-time Intelligent Multiple Fault Diagnosis System (RIMFDS). RIMFDS assesses current machine condition by using sensor signals. This system deals with multiple fault diagnosis, comprising of two main parts. One is a personal computer for remote signal generation and transmission and the other is a host system for multiple fault diagnosis. The signal generator generates various faulty signals and image information and sends them to the host. The host has various modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault diagnosis and graphic representation of the results. RIMFDS diagnoses multiple faults with fast fault propagation and complex physical phenomenon. The new system based on multiprocessing diagnoses by using Hierarchical Artificial Neural Network (HANN).