• Journal of the Korean Society of Safety
• /
• v.25 no.3
• /
• pp.1-6
• /
• 2010

Ball bearings are one of main components that support the rotational shaft in high speed rotary machinery. So, it is very important to detect the incipient faults and fault growth of bearing since the damage and failure of bearing can cause a critical failures or accidents of machinery system. In the past, many researchers mainly performed to detect the bearing fault using traditional method such as wavelet, statistics, envelope etc in vibration signals. But study on the detection technique for bearing fault growth has a little been performed. In this paper, we verified the possibility for monitoring of fault growth and detection of fault size in bearing outer-race by using the envelope powerspectrum and probabilistic density function from measured vibration signals.

• Proceedings of the KIEE Conference
• /
• 1999.07c
• /
• pp.1440-1443
• /
• 1999

This paper proposes fault detection method using a neural network & fuzzy logic on distribution lines. Fault on distribution lines is simulated using EMTP. The pattern of high impedance fault on pebbles, ground and short-circuit fault were take as the learning model. In this paper proposed fault detection method is evaluated on various conditions. The average values after analyzing fault current by FFT of even odd harmonics and fundamental rms were used for the neural network input. Test results were verified the validity of the proposed method

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.10
• /
• pp.4774-4796
• /
• 2018

Wireless sensor networks are composed of a large number of inexpensive and tiny sensors used in different areas including military, industry, agriculture, space, and environment. Fault tolerance, which is considered a challenging task in these networks, is defined as the ability of the system to offer an appropriate level of functionality in the event of failures. The present study proposed an intelligent throughput descent and distributed energy-efficient mechanism in order to improve fault tolerance of the system against soft and permanent faults. This mechanism includes determining the intelligent neighborhood radius threshold, the intelligent neighborhood nodes number threshold, customizing the base paper algorithm for distributed systems, redefining the base paper scenarios for failure detection procedure to predict network behavior when running into soft and permanent faults, and some cases have been described for handling failure exception procedures. The experimental results from simulation indicate that the proposed mechanism was able to improve network throughput, fault detection accuracy, reliability, and network lifetime with respect to the base paper.

• Proceedings of the KIEE Conference
• /
• 1998.07c
• /
• pp.1114-1117
• /
• 1998

Recent applications of neural networks to power system fault diagnosis have provided positive results and have shown advantages in process speed over conventional approaches. This paper describes the application of neural network to fault detection and classification in distribution lines using the fundamental component, 2-5th harmonics index, even and odd harmonics index, and zero phase current. The Electromagnetic Transients Program (EMTP) is used to obtain fault patterns for the training and testing of neural networks. The proposed fault detection and classification method in distribution lines is obtained by analysing the difference among normal, HIF, ground fault, short circuit fault condition.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.14 no.5
• /
• pp.1-8
• /
• 2006

The Steer-By-Wire(SBW) system replaces complex mechanical linkages of the current steering system with electric motors, sensors, and electronic control units. However, the SBW system should guarantee its safety and reliability before commercialization, and therefore, a reliable and robust fault-tolerant technology has to be implemented. This paper proposes a fault-tolerant control algorithm for the SBW system. Based on careful analysis on propagation effects of sensor faults, a reliable fault-tolerant control strategy has been developed. The fault-tolerant controller consists of a fault detection part that monitors and detects faults in the steering wheel and road wheel sensors, and a reconfiguration part that switches to normal sensor signal based on fault detection information. It has been demonstrated by simulation that the proposed algorithm detects sensor faults accurately and enables reliable steering control under various dynamic fault situations.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.41 no.11
• /
• pp.735-742
• /
• 2017

In this study, an effective model-based sensor fault detection methodology that can detect and isolate PEM temperature sensors fault is introduced. In fuel cell vehicle operation process, the stack temperature affects durability of a fuel cell. Thus, it is important for fault algorithm to detect the fault signals. The major objective of sensor fault detection is to guarantee the healthy operations of the fuel cell system and to prevent the stack from high temperature and low temperature. For the residual implementation, parity equation based on the state space is used to detect the sensors fault as stack temperature and coolant inlet temperature, and residual is compared with the healthy temperature signals. Then the residuals are evaluated by various fault scenarios that detect the presence of the sensor fault. In the result, the designed in this study fault algorithm can detect the fault signal.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.49 no.10
• /
• pp.516-523
• /
• 2000

In this paper, intelligent methods using digital protective relay in power supervisory control system is developed in order to protect power systems by means of timely fault detection and diagnosis during operation for induction motor which has various load environments and capacities in power systems. The spectrum pattern of input currents was used to monitor to state of induction motors, and by clustering the spectrum pattern of input currents, the newly occurrence of spectrums pattern caused by faults were detected. For diagnosis of the fault detected, the fuzzy fault tree was derived, and the fuzzy relation equation representing the relation between an induction motor fault and each fault type, was solved. The solution of the fuzzy relation equation shows the possibility of each fault's occurring. The results obtained are summarized as follows: 1) The test result on the basis of KEMC1120 and IEC60255, show that the operation time error of the digital motor protective relay is improved within ${\pm}5%$. 2) Using clustering algorithm by unsupervisory learning, an on-line fault detection method, not affected by the characteristics of loads and rates, was implemented, and the degree of dependency by experts during fault detection was reduced. 3) With the fuzzy fault tree, fault diagnosis process became systematic and expandable to the whole system, and the diagnosis for sub-systems can be made as an object-oriented module.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.2576-2581
• /
• 2003

We consider fault detection and isolation (FDI) problem for inertial navigation systems (INS) which use redundant inertial sensors and propose an FDI method using average of multiple parity vectors which reduce false alarm and wrong isolation, and improve correct isolation. We suggest optimal isolation threshold based on navigation performance, and suggest optimal sample number to obtain short detection time and to enhance detectability of faults little larger than threshold.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.2
• /
• pp.185-189
• /
• 2007

This paper deals with a fault detection system design for nonlinear systems with uncertain time varying parameters modelled as a T-S fuzzy system. A coprime factorization for T-S fuzzy systems is defined and a residual generator is designed using a left coprime factor. A fault detection criteria derived from the residual generator is also suggested. In order to demonstrate the efficacy of the suggested method, the fault defection method is applied to an inverted pendulum system and computer simulations are performed.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10a
• /
• pp.287-290
• /
• 1996

Fault diagnosis problem is currently the subject of extensive research and numerous survey paper can be found. Although several works are studied on the fault detection and isolation observers and the residual generators, those are concerned with only the detection of actuator failures or sensor failures. So, the perfect detection and isolation is strongly required for practical applications. In this paper, a, strategy of fault diagnosis using multiple proportional integral (PI) observers including the magnitude of actuator failures is provided. It is shown that actuator failures are detected and isolated perfectly by monitoring the integrated error between actual output and estimated output by a PI observer. Also in presence of complex actuator and sensor failures, these failures are detected and isolated by multiple PI observers.