• International Journal of Safety
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• v.11 no.2
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• pp.22-25
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• 2012

This paper deals with the problem of fault diagnosis for identifying a single fault when the number of assumed faults is larger than that of predictive variables. Principal component analysis (PCA) is employed to isolate and identify a single fault. PCA is a method to extract important information as reducing the number of large dimension in a process. The patterns of all assumed faults can be recognized by PCA and these can be employed whether a new fault is one of predefined faults or not. Through PCA, empirical models for analyzing patterns can be trained. When a single fault occurs, the pattern generated by PCA can be obtained and this is used to identify a fault. The performance of the proposed approach is illustrated in the actuator benchmark problem.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.706-709
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• 1996

In this paper, an algorithm for PLC(Programmable Logic Controller) fault diagnosis system is proposed and experimentation is conducted with a PLC and a virtual plant. Wrong output backward tracking algorithm is proposed in order to find the external faults of PLC. And query with keywords of the fault systems and specially designed test sequence programs are used. We lay emphasis on the backward tracking algorithm to diagnose the faults of PLC. It is shown experimentally that the proposed algorithm can find the faults which a typical self diagnostics in the-commercially available PLC cannot.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.314-316
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• 1993

In this paper, a method to detect and diagnose faults in Automated Manufacturing Systems(AMS) is proposed. In AMS, it is necessary to monitor the process-status. The detection and diagnosis of faults are often difficult in monitoring level with given passive data. We propose the model-based monitoring system for faults detection and diagnosis using Petri Nets to model AMS efficiently and easily. Simulation results show the validity of proposed method with example of Reverse Mill Process in Automated Mill Lines.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.7 s.178
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• pp.1731-1739
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• 2000

The Vibration signals of a gear driving system is often associated with gear tooth faults. Many studies have been done on the detection of impulsive vibration signals, which characterize the breaka ge of a gear tooth. Also, most of the studies on gear fault diagnosis are only about the fault existence at one gear-pair. This study concerns on the several possible faults of a geared motor that has three gear pairs. The measurement and analysis on the vibration signals of a running geared motor shows the relationship between the gear faults and the vibration signals. This study also shows that adaptive interference canceling technique can be appropriately applicable to detect which gear-pair has the fault, and that wavelet is better than spectrogram to figure out the gear fault.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.14 no.7
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• pp.552-559
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• 2002

The fault detection and diagnosis (FDD) technology may be applied in order to decrease the energy consumption and the maintenance cost of the air conditioning system. In this study, rule bases and curve fitting models were used to detect faults in an air handling unit. Gradually progressed faults, such as the fan speed degradation, the coil water leakage, the humidifier nozzle clogging, the sensor degradation and the damper stoppage, were applied to the developed FBD system. Simulation results show good detections and diagnoses of these faults. Therefore, this method may be effectively used for the fault detection and diagnosis of the air handling unit.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.3
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• pp.568-574
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• 2017

This paper proposes a demagnetization fault diagnosis method for interior permanent magnet synchronous motors(IPMSMs). In particular, a demagnetization fault is one of the most frequent electrical faults in IPMSMs. This paper proposes an estimation method for permanent magnet flux. The method is based on linear interpolation. The effectiveness of the proposed method for diagnose demagnetization faults is verified through various operating conditions by finite element simulation.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.5
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• pp.23-30
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• 2014

This paper presents an on-line diagnosis system for identifying health and faulted conditions in squirrel-cage induction motors using stator current, temperature, and partial discharge signals. The proposed diagnosis system can diagnose induction motor faults such as broken rotor bars, air-gap eccentricities, stator winding insulations, and bearing faults. Experimental results obtained from induction motors show that the proposed system is capable of detecting induction motor faults.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.7
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• pp.726-735
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• 2009

In this paper, we propose an approach to signal model-based fault detection and diagnosis system for induction motors. The current fault detection techniques used in the industry are limit checking techniques, which are simple but cannot predict the types of faults and the initiation of the faults. The system consists of two consecutive processes: fault detection process and fault diagnosis process. In the fault detection process, the system extracts the significant features from sound signals using combination of variance, cross-correlation and wavelet. Consequently, the pattern classification technique is applied to the fault diagnosis process to recognize the system faults based on faulty symptoms. The sounds generated from different kinds of typical motor's faults such as motor unbalance, bearing misalignment and bearing loose are examined. We propose two approaches for fault detection and diagnosis system that are waveletand-variance-based and wavelet-and-crosscorrelation-based approaches. The results of our experiment show more than 95 and 78 percent accuracy for fault classification, respectively.

• Nuclear Engineering and Technology
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• v.55 no.3
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• pp.827-838
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• 2023

Centrifugal pump is a key part of nuclear power plant systems, and its health status is critical to the safety and reliability of nuclear power plants. Therefore, fault diagnosis is required for centrifugal pump. Traditional fault diagnosis methods have difficulty extracting fault features from nonlinear and non-stationary signals, resulting in low diagnostic accuracy. In this paper, a new fault diagnosis method is proposed based on the improved particle swarm optimization (IPSO) algorithm-based variational modal decomposition (VMD) and relevance vector machine (RVM). Firstly, a simulation test bench for rotor faults is built, in which vibration displacement signals of the rotor are also collected by eddy current sensors. Then, the improved particle swarm algorithm is used to optimize the VMD to achieve adaptive decomposition of vibration displacement signals. Meanwhile, a screening criterion based on the minimum Kullback-Leibler (K-L) divergence value is established to extract the primary intrinsic modal function (IMF) component. Eventually, the factors are obtained from the primary IMF component to form a fault feature vector, and fault patterns are recognized using the RVM model. The results show that the extraction of the fault information and fault diagnosis classification have been improved, and the average accuracy could reach 97.87%.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.2
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• pp.99-104
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• 2009

The turn-to-turn short is one major fault of the motor faults of BLDC motors and can appear frequently. When the fault happens, the motor can be operated without breakdown, but it is necessary to maintain the motor for continuous working. In past research, several methods have been applied to detect winding faults. The representative approaches have been focusing on current signals, which can give important information to extract features and to detect faults. In this study, current sensors were installed to measure signals for fault detection of BLDC motors. In this study, the Park's vector method was used to extract the features and to isolate the faults from the current measured by sensors. Because this method can consider the three-phase current values, it is useful to detect features from one-phase and three-phase faults. After extracting two-dimensional features, the final feature was generated by using the two-dimensional values using the distance equation. The values were used in fuzzy similarity to isolate the faults. Fuzzy similarity is an available tool to diagnose the fault without model generation and the fault was converted to the percentage value that can be considered as possibility of the fault.