• International Journal of Fluid Machinery and Systems
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• v.2 no.1
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• pp.61-71
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• 2009

Machine fault diagnostic and prognostic techniques have been the considerable subjects of condition-based maintenance system in the recent time due to the potential advantages that could be gained from reducing downtime, decreasing maintenance costs, and increasing machine availability. For the past few years, research on machine fault diagnosis and prognosis has been developing rapidly. These publications covered in the wide range of statistical approaches to model-based approaches. With the aim of synthesizing and providing the information of these researches for researcher's community, this paper attempts to summarize and classify the recent published techniques in diagnosis and prognosis of rotating machinery. Furthermore, it also discusses the opportunities as well as the challenges for conducting advance research in the field of machine prognosis.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.6
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• pp.381-389
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• 2021

Most diagnosis approaches rely on historical failure data that might not be feasible in real operating conditions because the battery voltage and internal parameters are nonlinear according to various operating conditions, such as cell-to-cell configuration and initial condition. To overcome this issue, the estimator and the predictor require integrated approaches that consider comprehensive data, with the degradation process and measured data taken into account. In this paper, vector autoregressive models (VAR) with various parameters that affect overdischarge to the cell in the battery pack were constructed, and the cell-to-cell parameters were identified using an adaptive model to analyze the influence of failure prognosis. The theoretical analysis is validated using experimental results in terms of the feasibility and advantages of fault prognosis.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.2
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• pp.287-291
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• 2013

Fault Diagnosis is a process of detecting and isolating faults in a system. On demanding for safety and high reliability systems make it important for some reasons such as economical and environmental incentives. Especially embedded technology and IT technology combined with precise sensing techniques has been doing well developed and applied to fault diagnosis and prognosis in industrial systems like as automotive, ship, heavy industry and aerospace as well. This paper, as an empirical application of diesel engine, presents a method how to get raw data from physical systems, what to consider for successful implementation and which theoretic mathematical models should be applied. In a sense of system level Adaptive Filtering (we call Modified Kalman Filter) and a unit of part level Hidden Markov Process was developed and applied.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.886-890
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• 2007

Machine fault prognosis techniques have been considered profoundly in the recent time due to their profit for reducing unexpected faults or unscheduled maintenance. With those techniques, the working conditions of components, the trending of fault propagation, and the time-to-failure are forecasted precisely before they reach the failure thresholds. In this work, we propose an approach of Least Square Regression Tree (LSRT), which is an extension of the Classification and Regression Tree (CART), in association with one-step-ahead prediction of time-series forecasting technique to predict the future conditions of machines. In this technique, the number of available observations is firstly determined by using Cao's method and LSRT is employed as prognosis system in the next step. The proposed approach is evaluated by real data of low methane compressor. Furthermore, the comparison between the predicted results of CART and LSRT are carried out to prove the accuracy. The predicted results show that LSRT offers a potential for machine condition prognosis.

• International Journal of Safety
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• v.11 no.1
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• pp.26-32
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• 2012

This paper introduces a feasibility evaluation method for prognosis systems based on an empirical model in nuclear power plants. By exploiting the dynamical signature characterized by abnormal phenomena, the prognosis technique can be applied to detect the plant abnormal states prior to an unexpected plant trip. Early $operator^{\circ}{\emptyset}s$ awareness can extend available time for operation action; therefore, unexpected plant trip and time-consuming maintenance can be reduced. For the practical application in nuclear power plant, it is important not only to enhance the advantages of prognosis systems, but also to quantify the negative impact in prognosis, e.g., uncertainty. In order to apply these prognosis systems to real nuclear power plants, it is necessary to conduct a feasibility evaluation; the evaluation consists of 4 steps (: the development of an evaluation method, the development of selection criteria for the abnormal state, acquisition and signal processing, and an evaluation experiment). In this paper, we introduce the feasibility evaluation method and propose further study points for applying prognosis systems from KHNP's experiences in testing some prognosis technologies available in the market.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.4
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• pp.221-227
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• 2007

This paper introduces an on-line particle-filtering-based framework for failure prognosis in nonlinear, non-Gaussian systems. This framework uses a nonlinear state-space model of the plant(with unknown time-varying parameters) and a particle filtering(PF) algorithm to estimate the probability density function(pdf) of the state in real-time. The state pdf estimate is then used to predict the evolution in time of the fault indicator, obtaining as a result the pdf of the remaining useful life(RUL) for the faulty subsystem. This approach provides information about the precision and accuracy of long-term predictions, RUL expectations, and 95% confidence intervals for the condition under study. Data from a seeded fault test for a UH-60 planetary carrier plate are used to validate the proposed methodology.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.2041_2042
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• 2009

The diagnostic/prognostic problems for condition based maintenance or Prognostics and Health Management has been used. Primary objectives of diagnosis/prognosis are maximizing system availability and minimizing downtime from fault isolation through more effective troubleshooting efforts. Diagnosis aims to detect the onset of failures to improve system performance and reduce life cycle cost by reducing the failure time. The prognosis can reduce operational and support total ownership cost and improve safety of machinery and complex systems. In this Paper, a fault diagnosis methodology has been described using a monopropellant propulsion system model as a test bench.

• Korean Journal of Computational Design and Engineering
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• v.18 no.4
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• pp.250-257
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• 2013

This paper introduces a failure analysis procedure that underpins real-time fault prognosis. In the previous study, we developed a systematic eventization procedure which makes it possible to reduce the original data size into a manageable one in the form of event logs and eventually to extract failure patterns efficiently from the reduced data. Failure patterns are then extracted in the form of event sequences by sequence-mining algorithms, (e.g. FP-Tree algorithm). Extracted patterns are stored in a failure pattern library, and eventually, we use the stored failure pattern information to predict potential failures. The two practical case studies (marine diesel engine and SIRIUS-II car engine) provide empirical support for the performance of the proposed failure analysis procedure. This procedure can be easily extended for wide application fields of failure analysis such as vehicle and machine diagnostics. Furthermore, it can be applied to human health monitoring & prognosis, so that human body signals could be efficiently analyzed.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.2
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• pp.102-108
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• 2007

The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. This study introduces a technique to detect and identify faults in induction motors. Stator currents were measured and stored by time domain. The time domain is not suitable for representing current signals, so wavelet transform is used to convert the signal; onto frequency domain. The raw signals can not show the significant feature, therefore difference values are applied. The difference values were transformed by wavelet transform and the features are extracted from the transformed signals. The dynamic time warping method was used to identify the four fault types. This study describes the results of detecting fault using wavelet analysis.

• Journal of Advanced Marine Engineering and Technology
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• v.31 no.1
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• pp.95-102
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• 2007

The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis and prognosis are of increasing importance. This study introduces a technique to detect and identify faults in induction motors. Stator currents were measured and stored by time domain. The time domain is not suitable for representing current signals, so wavelet transform is used to convert the signals onto frequency domain. The raw signals can not show the significant feature, therefore difference values between the signal of the health conditions and that of the fault conditions are applied. The difference values were transformed by wavelet transform and the features are extracted from the transformed signals. The dynamic time warping method was used to identify the fault type. This study describes the results of detecting fault using wavelet analysis.