Duygu Bayram

Wavelet based Neuro-Detector for low frequencies of vibration signals in electric motors

This study presents a Wavelet based Neuro-Detector approach employed to detect the aging indications of an electric motor. Analysis of the aging indications, which can be seen in the low frequency region, is performed using vibration signals. More specifically, two vibration signals are observed for healthy and faulty (aged) cases which are measured from the same electric motor. Multi Resolution Wavelet Analysis (MRWA) is applied in order to obtain low and high frequency bands of the vibration signals. Thus for detecting the aging properties in the spectra, the Power Spectral Density (PSD) of the subband for the healthy case is used to train an Auto Associative Neural Network (AANN). The PSD amplitudes, which are computed for the faulty case, are applied to input nodes of the trained network for the re-calling process of AANN. Consequently, the simulation results show that some spectral properties defined in low frequency region are determined through the error response of AANN. Hence, some specific frequencies of the bearing damage related to the aging process are detected and identified.

Anfis model for vibration signals based on aging process in electric motors

In this study, the aging process of an electric motor is accomplished by adaptive neuro-fuzzy inference system (ANFIS) using vibration signals. Different ANFIS models are compared for representing the aging process in the best possible way. An artificial aging experiment is performed and vibration data taken from the initial (healthy) and final (faulty) cases are used to identify the aging process. Four different ANFIS models are presented. Moving average (MA) filters are applied to the input and output pairs for different lagging factors to change the smoothness degree of the data and thus the performance of system identification. The success of the models is evaluated on three conditions; the performance of the ANFIS and the linear correlation between expected output (faulty case data) and aging model output, in time and frequency domains.The study also evaluates the influence of preprocessing using MA filtering on the ANFIS performance for vibration data which have stochastic characteristics.

Redundancy-Based Predictive Fault Detection on Electric Motors by Stationary Wavelet Transform

In this study, a signal-based predictive fault-detection approach is developed to identify potential faults within an electric motor. In order to evaluate the performance of the proposed approach, first artificial motor vibration data are produced and used as a base line for analysis and assessment of the methodology. After successfully confirming proof of concept by detecting all fault frequencies hidden within the artificial data, the approach is then applied to the experimental data to see whether it can be accepted as a suitable potential fault detection tool for healthy electric motors. As the keystone of the study, algebraic summation operation (ASO) is introduced for predictive fault detection. ASO is built and based upon the redundant nature of stationary wavelet transform (SWT). Although similar to the SWT, the down sampling operation defined for the perfect reconstruction of SWT is omitted in ASO to amplify the redundancy within the transform. In other words, the redundancy obtained during decomposition is conserved on purpose with the goal of amplifying potential fault frequencies in electric motor vibration spectra and allowing for more robust and predictive fault detection.

Monitoring the Aging of Industrial Motors by Geometric Trending of Frequency Domain Signatures

The characterization of the aging of electric motors using vibration measurements and a geometric interpretation of spectral domain signatures is presented. For comparison, two vibration signal records from a three-phase induction motor were acquired during degradation due to bearing fluting, following thermal and chemical aging sequences. Power spectral densities (PSD) of the vibration signals were calculated and linear approximations to the spectral signatures were used to observe the overall trend. The intersection point and slopes of these linear representations were used to extract certain critical frequencies in order to define convex regions (CR) and convex hulls (CH) as representatives of the aging (degradation) process. Furthermore, using these CRs and CHs, the functional readiness of motors could be determined by comparison of the signatures for initial and aged conditions.

Lyapunov exponent for aging process in induction motor

Nonlinear systems like electrical circuits and systems, mechanics, optics and even incidents in nature may pass through various bifurcations and steady states like equilibrium point, periodic, quasi-periodic, chaotic states. Although chaotic phenomena are widely observed in physical systems, it can not be predicted because of the nature of the system. On the other hand, it is known that, chaos is strictly dependent on initial conditions of the system [1-3]. There are several methods in order to define the chaos. Phase portraits, Poincare maps, Lyapunov Exponents are the most common techniques. Lyapunov Exponents are the theoretical indicator of the chaos, named after the Russian mathematician Aleksandr Lyapunov (1857-1918). Lyapunov Exponents stand for the average exponential divergence or convergence of nearby system states, meaning estimating the quantitive measure of the chaotic attractor. Negative numbers of the exponents stand for a stable system whereas zero stands for quasi-periodic systems. On the...

Wavelet based trend analysis for monitoring and fault detection in induction motors

The aging mechanism of an induction motor is observed using vibration signatures in this study. A wavelet based trending application is used through Multi Resolution Wavelet Analysis. The progress of aging is shown and interpreted based on the trends of different cases of the same induction motor. Aging region concept is introduced to designate a new condition monitoring strategy. So, the vibration monitoring is refined into a smaller scale. By this way, a new simple and feasible monitoring algorithm is proposed. Then a rating parameter is introduced in order to estimate the situation of the motor. Thus, by using this method the aging of any random state can be detected as a percentage.

Integration of Time Series Modeling and Wavelet Transform for Monitoring Nuclear Plant Sensors

Dynamic fluctuations of nuclear plant sensors contain information about their response characteristics and bandwidth features. The random fluctuations can be characterized by using auto-regression (AR) time-series models. These discrete-time models are then utilized to estimate time-domain and frequency-domain signatures. Prior to developing these models, the sensor measurements are enhanced by filtering both low-frequency and high-frequency components using wavelet transforms. The use of wavelet transform for signal conditioning results in minimum distortion of the signal bandwidth, and thus provides an effective approach for data pre-processing. This integrated approach is applied to plant data from a pressurized water reactor (PWR). Univariate AR models were established for several pressure transmitter data, and used to estimate response time parameters of sensors and their frequency spectra. The results of this integrated approach demonstrate the improvement in the sensor signature estimation compared to the direct use of plant measurements.

Comparison of different line start interior permanent magnet synchronous motor types with respect to IE4 efficiency class

Recently, the increasing requirements and tendencies of higher efficiency systems have caused a substantial impact on electric motor design. Designers tend to change their views on motor topologies to provide the predetermined values of higher efficiency classes (such as IE3, IE4, IE5). Line-start interior permanent magnet synchronous motors (LSIPMSM) are strong candidates for higher efficiency class general purpose motors due to their higher MTPA (maximum torque per ampere) values. In this study, three different rotor types of LSIPMSM, i.e. V-magnet type, spoke-magnet type and rectangular-magnet type, are investigated with regard to efficiency requirements of super premium efficiency class (IE4). The impact of designs on rotor cage which is essential for starting and synchronization is also studied. The electromagnetic design study is conducted and then the optimized designs are manufactured and tested. The most applicable motor type is determined by means of its ease-of-manufacture and low cost features.

An approach to optimal design of double-sided coreless linear motor

Double-sided coreless linear motors have a crucial role in direct-driven precise linear motion applications. In this study, an optimum design effort focused on thrust force production is presented. For an applicable brushless linear motor structure, winding and permanent magnet (PM) dimensions which are providing the highest possible force are defined by using finite element analysis (FEA). It is shown that the most important problem of an optimum design is arising from placing proper amount of ampere-turns, i.e. windings, without losing a substantial value of permanent magnet flux density due to increased air gap. For obtaining an optimal design, structural demagnetization, loading demagnetization and fringing flux must be taken into account. So a trade-off is provided between the winding structure and the PM assembly.

In-situ Condition Monitoring of Components in Small Modular Reactors Using Process and Electrical Signature Analysis. Final report, volume 1. Development of experimental flow control loop, data analysis and plant monitoring

For reliable and economic long-term operation of Small Modular Reactors (SMR), it is imperative that continuous in-situ monitoring of critical equipment must be developed and incorporated in the reactor design phase. This capability is attractive for remote deployment of SMRs with longer fuel cycle duration and for minimizing forced outages, thus enhancing the utilization of these power generating systems in small electric grid environments. These issues were highlighted in a report on Instrumentation, Controls, and Human-Machine Interface Technology Development Roadmap for Grid Appropriate Reactors (ORNL). The DOE Workshops on On-Line Monitoring and Small Modular Reactors (held in June 2010) further emphasized the need for the development of continuous non-invasive approach for reactor surveillance. These technologies contribute to smart condition-based maintenance, reduced human resources, and remote monitoring of reactor components. In integral primary system reactors (IPSR) and other designs of SMRs, the pressure vessel incorporates most of the critical equipment used for power generation. Examples of such plant components include: motors, coolant circulation pumps, motor-operated valves, solenoid-operated valves, compressors, control rod drives, incore instrumentation, and reactor internal structures.