• 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.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2002.05a
• /
• pp.39-52
• /
• 2002

Research Direction The significant research direction in mechanical fault diagnosis area: Theorles and approaches for fault feature extracting and fault classification. Identification Complicated fault generating mechanism and its model Intelligent fault diagnosis system (including the expert system and network based remote diagnosis system) One of the Key Points： Fault feature extracting techniques based on (modern) signal processing(omitted)

• Annals of Clinical Neurophysiology
• /
• v.10 no.1
• /
• pp.29-32
• /
• 2008

Although various criteria on the diagnosis of diabetic neuropathy are applied from trial to trial, being tailored in concert with its purpose, the utmost evidences of the diagnosis are subjective symptoms and objective signs of neurologic deficit. The application and interpretation of auxiliary electrophysiological test including nerve conduction study (NCS) should be made on the context of clinical pictures. The evaluation of the functions of small, thinly myelinated or unmyelinated nerve fibers has been increasingly stressed recently with the advent of newer techniques, e.g., measurement of intraepidermal fiber density, quantitative sensory testing, and autonomic function test. And the studies with those techniques have shed light to the nature of the evolution of diabetic neuropathy. The practical application of these techniques to the diagnosis of diabetic neuropathy in the individual patients, however, should be made cautiously due to several shortcomings: limited accessibility, wide overlapping zone between norm and abnormality with resultant unsatisfactory sensitivity and specificity, difficulty in performing subsequent tests, unproven quantitative correlation with clinical deficit, and invasiveness of some technique. NCS, as an extension of clinical examination, is still the most reliable electrophysiological test in evaluating neuropathy and gives the invaluable information about the nature of neuropathy, whereas the newer techniques need more refinement of the procedure and interpretation, and the accumulation of large scaled data of application to be considered as established diagnostic tools of peripheral neuropathy.

• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.14
• /
• pp.5613-5618
• /
• 2015

Clinicians should looking for techniques that helps to early diagnosis of cancer, because early cancer detection is critical to increase survival and cost effectiveness of treatment, and as a result decrease mortality rate. Medical images are the most important tools to provide assistance. However, medical images have some limitations for optimal detection of some neoplasias, originating either from the imaging techniques themselves, or from human visual or intellectual capacity. Image processing techniques are allowing earlier detection of abnormalities and treatment monitoring. Because the time is a very important factor in cancer treatment, especially in cancers such as the lung and breast, imaging techniques are used to accelerate diagnosis more than with other cancers. In this paper, we outline experience in use of image processing techniques for lung and breast cancer diagnosis. Looking at the experience gained will help specialists to choose the appropriate technique for optimization of diagnosis through medical imaging.

• Transactions on Electrical and Electronic Materials
• /
• v.14 no.4
• /
• pp.211-215
• /
• 2013

Partial discharge diagnosis techniques using ultra high frequencies do not affect load movement, because there is no interruption of power. Consequently, these techniques are popular among the prevention diagnosis methods. For the first time, this measurement technique has been applied to the GIS, and has been tested by applying an extra high voltage switchboard. This particular technique makes it easy to measure in the live state, and is not affected by the noise generated by analyzing the causes of faults ? thereby making risk analysis possible. It is reported that the analysis data and the evaluation of the risk level are improved, especially for poor location, and that the measurement of Ultra high frequency (UHF) partial discharge of the real live wire in industrial switchgear is spectacular. Partial discharge diagnosis techniques by using the Ultra High Frequency sensor have been recently highlighted, and it is verified by applying them to the GIS. This has become one of the new and various power equipment techniques. Diagnosis using a UHF sensor is easy to measure, and waveform analysis is already standardized, due to numerous past case experiments. This technique is currently active in research and development, and commercialization is becoming a reality. Another aspect of this technique is that it can determine the occurrences and types of partial discharge, by the application diagnosis for live wire of ultra high voltage switchgear. Measured data by using the UHF partial discharge techniques for ultra high voltage switchgear was obtained from 200 places in Gumi, Yeosu, Taiwan and China's semiconductor plants, and also the partial discharge signals at 15 other places were found. It was confirmed that the partial discharge signal was destroyed by improving the work of junction bolt tightening check, and the cable head reinforcement insulation at 8 places with a possibility for preventing the interruption of service. Also, it was confirmed that the UHF partial discharge measurement techniques are also a prevention diagnosis method in actual industrial sites. The measured field data and the usage of the research for risk assessment techniques of the live wire status of power equipment make a valuable database for future improvements.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.4 no.1
• /
• pp.23-28
• /
• 2009

Testing of analog(and mixed-signal) circuits has been a difficult task for test engineers and effective test techniques to solve these problems are required. This paper develops a new technique which increases fault detection and diagnosis rates for analog circuits by using extended MTSS (Modified Time Slot Specification) technique based on MTSS proposed by the author. High performance current sensors with digital outputs are used as core components for these techniques. A fault diagnosis structure with minimal hardware overhead in ATE is also described.

• International Journal of Computer Science & Network Security
• /
• v.22 no.7
• /
• pp.1-12
• /
• 2022

The current progression in the Internet of Things (IoT) and Machine Learning (ML) based technologies converted the traditional healthcare system into a smart healthcare system. The incorporation of IoT and ML has changed the way of treating patients and offers lots of opportunities in the healthcare domain. In this view, this research article presents a new IoT and ML-based disease diagnosis model for the diagnosis of different diseases. In the proposed model, vital signs are collected via IoT-based smart medical devices, and the analysis is done by using different data mining techniques for detecting the possibility of risk in people's health status. Recommendations are made based on the results generated by different data mining techniques, for high-risk patients, an emergency alert will be generated to healthcare service providers and family members. Implementation of this model is done on Anaconda Jupyter notebook by using different Python libraries in it. The result states that among all data mining techniques, SVM achieved the highest accuracy of 0.897 on the same dataset for classification of Parkinson's disease.

• Journal of IKEEE
• /
• v.25 no.3
• /
• pp.548-555
• /
• 2021

This paper outlines the basic concepts, approaches and research trends of fault diagnosis and fault tolerant control applied to industrial processes, facilities, and motor drives. The main role of fault diagnosis for industrial processes is to create effective indicators to determine the defect status of the process and then take appropriate measures against failures or hazadous accidents. The technologies of fault detection and diagnosis have been developed to determine whether a process has a trend or pattern, or whether a particular process variable is functioning normally. Firstly, data-driven based and model-based techniques were described. Secondly, fault detection and diagnosis techniques for industrial processes are described. Thirdly, passive and active fault tolerant control techniques are considered. Finally, major faults occurring in AC motor drives were listed, described their characteristics and fault diagnosis and fault tolerant control techniques are outlined for this purpose.

• Proceedings of the KIEE Conference
• /
• 1998.07b
• /
• pp.420-422
• /
• 1998

All Fault Detection and Diagnosis(FDD) methods utilize classification techniques. The objective of this study was to demonstrate the application of classification techniques to the problem of diagnosing faults in data generated by a variable-air-volume(VAV) air-handling unit(AHU) simulation model and to describe the characteristics of the techniques considered. Artificial neural network classifier and fuzzy clustering classifier were considered for fault diagnostics.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.1
• /
• pp.538-543
• /
• 2016

For a good product quality and plant safety, it is necessary to implement the on-line monitoring and diagnosis schemes of industrial processes. Combined with monitoring systems, reliable diagnosis schemes seek to find assignable causes of the process variables responsible for faults or special events in processes. This study deals with the real-time diagnosis of complicated industrial processes from the intelligent use of multivariate statistical techniques. The presented diagnosis scheme consists of a classification-based diagnosis using nonlinear representation and filtering of process data. A case study based on the simulation data was conducted, and the diagnosis results were obtained using different diagnosis schemes. In addition, the choice of future estimation methods was evaluated. The results showed that the performance of the presented scheme outperformed the other schemes.