Elman's recurrent neural network applications to condition monitoring in nuclear power plant and rotating machinery

The purpose of this study is to show the capability of recurrent neural nets (RNN) for condition monitoring and diagnosis in nuclear power plant systems and rotating machinery. In the first application, the study addresses the use of RNN for detecting anomalies introduced from the simulated power operation of a high-temperature gas cooled nuclear reactor. In the second, it is used to detect the motor bearing damage using a coherence function approach, which is defined between the motor current and vibration signals, for induction motors. Hence, the high performance of Elman's RNN was shown by means of two different applications.     

CHARACTERIZATION OF SURFACE AUXIN RESIDUE IN GREENHOUSE TOMATOES (LYCOPERSICON ESCULENTUM)

Greenhouse tomato samples (n = 20) was analyzed before and after peel removal in order to determine surface auxin residue. Mean 4‐CPA residue levels of greenhouse tomatoes with and without peels were 0.383 ± 0.123 mg kg^?1 and 0.241 ±0.085 mg kg^?1, respectively. This difference (36 ±13%) was statistically significant. The frequency distribution curve of tomatoes with peel had a peak point at 4‐CPA reside interval of 0.4‐ < 0.5 mg kg^?1 tomato, and shifted back to 4‐CPA residue interval of 0.2‐ < 0.3 mg kg^?1 for tomatoes without peel. Percentage of samples having 4‐CPA level lower than the critical concentration of 0.5 mg kg^?1 was 80% before peel removal, but increased to 100% upon being peeled. The mean 4‐CPA residue of peels was roughly estimated to be 3.449 mg kg^?1 peel based on peeled versus nonpeeled fruit residue.     

Detection of interdependent primary systems using wideband cognitive radios

Cognitive radios (CRs) may be sharing multiple frequency bands with primary systems if the CR is a wideband or an ultra wideband (UWB) system. In that case, the CR should ensure all the coexisting primary systems in these bands are detected before it can start data transmission. In this work, we study the primary system detection performance of a wideband CR assuming that there are multiple coexisting primary systems and that these primary systems may be jointly active. Accordingly, we consider the implementation of energy detection scheme in multiple bands followed by two detection methods: (i) a maximum-a-posteriori (MAP) based detection (i.e., joint detection) that takes into account the statistics of simultaneously operating systems in independent bands and (ii) a Neyman–Pearson (NP) test based detection that optimizes the threshold values independently in each band (i.e., independent detection). For a simpler implementation of the independent detection, we show that the threshold values obtained from joint detection can be used in order to achieve the optimum NP test based independent detection results. In addition to quantifying the gain of joint detection over independent detection in terms of probabilities of false alarm and detection for practical scenarios, we also present the operation capability of CRs in terms of the fractions of time the CR can access the channel without interfering with the primary systems. The results are important for the practical implementation of multiband detection when the primary systems are known to be interdependent.     

Reliability analysis of the complex mode indicator function and Hilbert Transform techniques for operational modal analysis

This study presents application of the CMIF and the Hilbert Transform techniques onto simulated response data obtained using a numerical model of a typical school building from Turkey. White noise is added to the data in order to achieve a noise to signal ratio of 5%. 100 Monte Carlo analysis sequences are carried out and the modal parameters (the frequencies, the mode shapes and the damping ratios) are identified at each Monte Carlo run for both techniques. The results are compared with the identifications obtained from the simulated data using stochastic subspace based system identification technique. The overall results of the study show that the mode shapes are clearly identified the best by using the CMIF technique. The damping ratios are estimated better by using the stochastic subspace based system identification technique whereas the frequencies are best determined by the CMIF. The results also show that both the CMIF and the Hilbert Transform techniques are sensitive to the type of window used as well as the averaging and the decimation process. It is apparent that the CMIF technique is as robust as the frequently used stochastic subspace based system identification technique and can be confidently used for modal parameter estimation of stiff low to mid rise reinforced concrete structures.     

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.     

Inverse propagation of uncertainties in finite element model updating through use of fuzzy arithmetic

A fuzzy finite element model updating (FFEMU) method is presented in this study for the damage detection problem. The uncertainty caused by the measurement noise in modal parameters is described by fuzzy numbers. Inverse analysis is formulated as a constrained optimization problem at each α-cut level. Membership functions of each updating parameter which correspond to reduction in bending stiffness of the finite elements is determined by minimizing an objective function using a hybrid version of genetic algorithms (GA) and particle swarm optimization method (PSO) which is very efficient in terms of accuracy and robustness. Practical evaluation of the approximate bounds of the interval modal parameters in FFEMU iterations is addressed. A probabilistic analysis is performed using Monte Carlo simulation (MCS) and the results are compared with presented FFEMU method. It is apparent from numerical simulations that the proposed method is well capable in finding the membership functions of the updating parameters within reasonable accuracy. It is also shown that the results obtained by FFEMU are in good agreement with the MCS results while FFEMU is not as computationally expensive as the MCS method. Nevertheless, the proposed FFEMU do not required derivatives of the objective function like existing methods except in the deterministic case.