Emine Ayaz

Feature extraction related to bearing damage in electric motors by wavelet analysis

The purpose of this paper is to extract features from vibration signals measured from motors subjected to accelerated bearing fluting aging and to detect the effects of bearing fluting at each aging cycle of induction motors. This trending of bearing degradation is achieved by monitoring the changes in the vibration signals at various sub-band levels. This is accomplished by the application of wavelet transforms and multi-resolution analysis in order to extract information from selected frequency bands with minimum signal distortion.

Comparisons between the various types of neural networks with the data of wide range operational conditions of the Borssele NPP

This paper addresses a trend monitoring in operating nuclear power plant by use of two types of Recurrent Neural Networks (RNN). The interesting feature of the RNN is intrinsic dynamic memory that reflects the current output as well as the previous inputs and outputs are gradually quenched. The first one Elman type of RNN which has a feed-back from hidden layer to the input layer neurons while in the Jordan type, from the outputs of the neural net to the inputs of the neural net. In this paper the theoretical assessment of the both RNNs is given. Both topological structures including Back Propagation (BP) neural network were implemented to the Borssele NPP. Learning achieved from 30% to 100% nominal power at the starting period of the new core 30 September 2001. After learning period the reactor operation is followed by the neural network. Paper will present the reactor system, the real time data collection and the merits of the three types of the neural network applied while in the learning and continuous processing of the changing of the operational conditions.

Fault detection based on continuous wavelet transform and sensor fusion in electric motors

Purpose – The purpose of this paper is to extract features from vibration signals measured from induction motors subjected to accelerated aging of bearings by fluting tests.Design/methodology/approach – Aging tests were performed according to IEEE test procedures. The data acquisition involved the measurement of vibration signals using accelerometers that were installed on the bearings and on the motor casing. In this application, only two accelerometers, which were placed near the process end of the motor bearing, are used for data analysis and feature extraction studies. After the data collection, information from the two sensors was combined using simple sensor fusion method under the linearity conditions, and then spectral analysis and time‐scale analysis were performed. The fused vibration signal is decomposed into several scales using continuous wavelet transform (CWT) and its first scale is used to indicate the bearing degradation.Findings – Bearing damage characterization was determined between 2‐...

Adaptive neuro-fuzzy inference system for bearing fault detection in induction motors using temperature, current, vibration data

In this study the features for bearing fault diagnosis is investigated based on the analysis of temperature, vibration and current measurements of a 3 phase, 4 poles, 5 HP induction motors which are chemically, thermally and electrically aged by artificial aging methods. Then three adaptive neuro-fuzzy inference systems which takes the temperature, current and vibration measurements as inputs and the condition of the motors as output are established, and the performances of these networks are compared.

Real time reactor noise diagnostics for the Borssele (PWR) nuclear power plant

Abstract After the upgrade of Borssele NPP in 1997, core cycle 24, the power plant operated three years more with 91% availability. The authority of the power plant decided to enhance and upgrade the reactor trend monitoring and plant information recording system with higher frequencies than the plant data processing system (PPS) as well as installing a flexible and multiple-purpose reactor noise analysis system which may support the reactor maintenance group with on-line and off-line capabilities for several different signal processing applications. Two measuring and monitoring systems were built in 2001 and fully taken in implementation during the start-up of the new core 28. In this sense, the new system was used in power operation during the 29 th of September 2001. This paper will introduce the measuring system, the operational tasks, and the results obtained so far on the real-time core-barrel motions (CBM) and the two-primary coolant pump vibrations measured through the reactor noise analysis.

On-line neuro-expert monitoring system for borssele nuclear power plant

Abstract A new method for an on-line monitoring system for the nuclear power plants has been developed utilizing the neural networks and the expert system. The integration of them is expected to enhance a substantial potential of the functionality as operators support. The recurrent neural network and the feed-forward neural network with adaptive learning are selected for the plant modeling and anomaly detection because of the high capability of modeling for dynamic behavior. The expert system is used as a decision agent, which works on the information space of both the neural networks and the human operators. The information of other sensory signals is also fed to the expert system, together with the outputs that the neural networks generate from the measured plant signals. The expert system can treat almost all known correlation between plant status patterns and operation modes as a priori set of rules. From the off-line test at Borssele Nuclear Power Plant (PWR 480 MWe) in the Netherlands, it was shown that the neuro-expert system successfully monitored the plant status. The expert system worked satisfactorily in diagnosing the system status by using the outputs of the neural networks and a priori knowledge base from the PWR simulator. The electric power coefficient is simultaneously monitored from the measured reactive and active electric power signals.

Neuro-detector Based on Coherence Analysis for Stator Insulation in Electric Motors

Abstract This research describes the monitoring of the fundamental spectral features of stator insulation damage through accelerated aging studies for induction motors with a power rating of 5 HP. In order to accomplish this goal, even-harmonic values of the line frequency defined between the 4th and the 16th harmonics, which are computed by the coherence approach between the stator currents and vibration signals, are determined as indicators of stator insulation damage. After this determination, a neuro-detector based on the auto-associative neural structure is trained in the frequency domain. This uses coherence variations and even-harmonic values as indicators of the insulation damage of an induction motor by observing the changes in the errors (residuals) generated by the neural net.

Transfer Function approach based upon wavelet transform for bearing damage detection in electric motors

This study presents a transfer function (TF) approach based on the continuous wavelet transform (CWT) using the vibration measurements for feature extraction. This approach helps to extract the origin of the bearing damage that develops during the aging process and then, it can be used to find the potential defects, which exist in healthy motor bearings as manufacturing defects. In this manner, there are two fundamental steps of the study. They are the definition of the transfer function and feature extraction which is related to the bearing damage characterization in electric motors. The definition of the feature transfer function is done between the first scale of the CWT analysis and original vibration signal in the frequency domain. Hence, it is introduced as a new viewpoint for condition monitoring studies in induction motors.

Artificial neural networks for dynamic monitoring of simulated-operating parameters of high temperature gas cooled engineering test reactor (HTTR)

Abstract This paper addresses to the problem of utilisation of the artificial neural networks (ANNs) for detecting anomalies as well as physical parameters of a nuclear power plant during power operation in real time. Three different types of neural network algorithms were used namely, feed-forward neural network (back-propagation, BP) and two types of recurrent neural networks (RNN). The data used in this paper were gathered from the simulation of the power operation of the Japans High Temperature Engineering Testing Reactor (HTTR). For the wide range of power operation, 56 signals were generated by the reactor dynamic simulation code for several hours of normal power operation at different power ramps between 30 and 100% nominal power. Paper will compare the outcomes of different neural networks and presents the neural network system and the determination of physical parameters from the simulated operating data.

Signal based fault detection for stator insulation in electric motors

Stator winding insulation damage in induction motors is considered in this research and fault features developed during the artificial aging process of the stator insulation are extracted from the collected data by the spectral analysis methods. Even harmonic effects are determined as a common feature between the phase currents and vibration signals using the coherence analysis and hence this feature is interpreted as an indicator of the stator insulation fault.