Jang-Hwan Park

C-ANFIS based fault diagnosis for voltage-fed PWM motor drive systems

Since most of the induction motors are operated by the inverter, an unexpected fault of the inverter can cause serious troubles such as downtime of equipment, heavy loss, and etc. Therefore, the studies on the robust drive system for induction motors to protect the system under the fault modes are gaining more interests. This paper investigates the fault diagnosis for open-switch damages in a voltage-fed PWM motor drive system. For diagnosing the conditions of a inverter, we transform the current signal to the d-q axis. And then, we obtain the features consisting of the trajectories of d-q phase currents for each fault mode. In the ideal cases, a set of fault modes can be classified by using the shape of these trajectories. There are, however, many variational elements such as load torque and the electrical/mechanical variable parameters. So, we propose a robust diagnosis method based on the neuro-fuzzy algorithm. For this, we adopted the Clustering Adaptive Neuro Fuzzy Inference System(C-ANFIS) to recognize the various and vague fault patterns. Finally, we implement the method for the diagnosis module of the inverter with MATLAB and show its usefulness.

Kernel PCA based faults diagnosis for wastewater treatment system

A Kernel PCA based fault diagnosis system for biological reaction in full-scale wastewater treatment plant was proposed using only common bio-chemical sensors such as ORP (Oxidation-Reduction Potential) and DO (Dissolved Oxygen). SBR (Sequencing Batch Reactor) is one of the most general sewage/wastewater treatment processes and, particularly, has an advantage in high concentration wastewater treatment like sewage wastewater. During the SBR operation, the operation status could be divided into normal status and abnormal status such as controller malfunction, influent disturbance and instrumental trouble. For the classification and diagnosis of these statuses, a series of preprocessing, dimension reduction using PCA, LDA, K-PCA and feature reduction was performed. Also, raw data obtained from SBR were transformed to synthetic data or fusion data and the performance were compared with each other. As the results, the fault recognition rate using fusion data showed the better result than that of raw data of [ORP] or [DO] and the combination method of K-PCA with LDA was superior to other methods such as PCA and LDA.

Fault diagnosis for induction machines using kernel principal component analysis

For the fault diagnosis of three-phase induction motors, we set up an experimental unit and then develop a diagnosis algorithm based on pattern recognition. The experimental unit consists of induction motor drive and data acquisition module to obtain the fault signals. As the first step for diagnosis procedure, preprocessing is performed to make the acquired current simplified and normalized. To simplify the input data, three-phase currents are transformed into the magnitude of Concordia vector. As the next step, feature extraction is performed by kernel PCA. Finally, we used the linear classifier based on two types of distance measures. To show the effectiveness, the proposed fault diagnostic system has been intensively tested with the various data acquired under the different electrical and mechanical faults with varying load.

Detection and Diagnosis of Induction Motor Using Conditional FCM and Radial Basis Function Network

In this paper, we propose a hierarchical hybrid neural network for detecting faults of induction motor. Implementing the classifier based on the input and output data, we apply appropriate transform and classification method at each step. In the proposed method, after obtaining the current of state of motor for each period, we transform it by Principle Component Analysis(PCA) to reduce its dimension. Before the training process, we use the conditional Fuzzy C-means(FCM) for obtaining the initial parameters of neural network for more effective learning procedure. From the various simulations, we find that the proposed method shows better performance to detect and diagnosis of induction motor and compare than other methods.

Face Recognition Using Wavelet Coefficients and Hidden Markov Model

In this paper, we proposes a method for face recognition using HMM(hidden Markov model) and wavelet coefficients First, input images are compressed by using the multi-resolution analysis based on the discrete wavelet transform. And then, the wavelet coefficients obtained from each subband are used as feature vectors to construct the HMMs. In the recognition stage, we obtained higher recognition rate by summing of each recognition rate of wavelet subband. The usefulness of the proposed method was shown by comparing with conventional VQ and DCT-HMM ones. The experimental results show that the proposed method is more satisfactory than previous ones.

Knowledge-based faults diagnosis system for wastewater treatment

This paper proposed a knowledge-based fault diagnosis system using ORP (Oxidation-Reduction Potential) and DO (Dissolved Oxygen) values which usually applied as control parameters in wastewater treatment plants. If the basic control parameters such as ORP and DO can be applied to operation diagnosis, the stability of process will be remarkably improved without additional expenses. This proposed diagnosis method uses only the ORP and DO values obtained from full-scale SBR (Sequencing Batch Reactor). For the classification and diagnosis of these statues, a sequenced process of preprocessing, dimension reducing using PCA and feature extraction with ORP, DO and a synthetic parameter of [ORP DO] were proposed and applied. As results, the synthetic parameter of [ORP DO] shows better fault recognition rate than that of independent application of each parameter. It was considered that this diagnostic system using control parameters could be used to support small-scale wastewater treatment management.

Fault Diagnosis of Voltage-Fed Inverters Using Pattern Recognition Techniques for Induction Motor Drive

Since an unexpected fault of induction motor drive systems can cause serious troubles in many industrial applications, which the technique is required to diagnose faults of a voltage-fed PWM inverter for induction motor drives. The considered fault types are rectifier diodes, switching devices and input terminals with open-circuit faults, and the signal for diagnosis is derived from motor currents. The magnitude of dq-current trajectory is used for the feature extraction of a fault and PCA LDA are applied to diagnose. Also, we show results with respect to the execution time because of the possibility to use that a diagnosis software is embedded in the controllers of medium and small size induction motors drive for real-time diagnosis. After we performed various simulations for the fault diagnosis of the inverter, the usefulness of proposed algerian was verified.

Emotion Recognition Method from Speech Signal Using the Wavelet Transform

In this paper, an emotion recognition method using speech signal is presented. Six basic human emotions including happiness, sadness, anger, surprise, fear and dislike are investigated. The proposed recognizer have each codebook constructed by using the wavelet transform for the emotional state. Here, we first verify the emotional state at each filterbank and then the final recognition is obtained from a multi-decision method scheme. The database consists of 360 emotional utterances from twenty person who talk a sentence three times for six emotional states. The proposed method showed more 5% improvement of the recognition rate than previous works.

Real-time Fault Diagnosis of Induction Motor Using Clustering and Radial Basis Function

For the fault diagnosis of three-phase induction motors, we construct a experimental unit and then develop a diagnosis algorithm based on pattern recognition. The experimental unit consists of machinery module for induction motor drive and data acquisition module to obtain the fault signal. As the first step for diagnosis procedure, preprocessing is performed to make the acquired current simplified and normalized. To simplify the data, three-phase current is transformed into the magnitude of Concordia vector. As the next step, feature extraction is performed by kernel principal component analysis(KPCA) and linear discriminant analysis(LDA). Finally, we used the classifier based on radial basis function(RBF) network. To show the effectiveness, the proposed diagnostic system has been intensively tested with the various data acquired under different electrical and mechanical faults with varying load.

Knowledge-Based fuzzy control of pilot-scale SBR for wastewater treatment

A fuzzy controller to optimize oxic phase of sub-cycle in pilot scale SBR (working volume, 20m3) located at public swine wastewater treatment plant was investigated. The operation mode of intermittent feeding of raw water and sub-cycle with repeating anoxic-aeration conditions were adapted to avoid the high-strength nitrogen inhibition. In sub-cycle, aeration time for ammonium oxidation was tried to control by fuzzy system using DO value and its differential values for input membership function. In previous study, a lag time of DO profile was proved to be useful for inference of nitrogen loading rates and abrupt raise of DO showed potential ability to detect ending point of ammonia oxidation, however, it also needs proper safety factor for high level DO in lag time or noisy system. Here, the fuzzy system could reduce the complexity of application. As results, the fuzzy system with the simultaneous application of DO value and its differential values was appropriate for the stable control of SBR.