Alkan Alkaya

Variance sensitive adaptive threshold-based PCA method for fault detection with experimental application

Principal Component Analysis (PCA) is a statistical process monitoring technique that has been widely used in industrial applications. PCA methods for Fault Detection (FD) use data collected from a steady-state process to monitor T(2) and Q statistics with a fixed threshold. For the systems where transient values of the processes must be taken into account, the usage of a fixed threshold in PCA method causes false alarms and missing data that significantly compromise the reliability of the monitoring systems. In the present article, a new PCA method based on variance sensitive adaptive threshold (T(vsa)) is proposed to overcome false alarms which occur in the transient states according to changing process conditions and the missing data problem. The proposed method is implemented and validated experimentally on an electromechanical system. The method is compared with the conventional monitoring methods. Experimental tests and tabulated results confirm the fact that the proposed method is applicable and effective for both the steady-state and transient operations and gives early warning to operators.

Non-linear minimum variance estimation for fault detection systems

A novel model-based algorithm for fault detection in stochastic linear and non-linear systems is proposed. The non-linear minimum variance estimation technique is used to generate a residual signal, which is then used to detect actuator and sensor faults in the system. The main advantage of the approach is the simplicity of the non-linear estimator theory and the straightforward structure of the resulting solution. Simulation examples are presented to illustrate the design procedure and the type of results obtained. The results demonstrate that both actuator and sensor faults can be detected successfully.

Experimental application of nonlinear minimum variance estimation for fault detection systems

The purpose of this paper is to present an experimental design and application of a novel model-based fault detection technique by using a nonlinear minimum variance (NMV) estimator. The NMV estimation technique is used to generate a residual signal which is then used to detect faults in the system. The main advantage of the approach is the simplicity of the nonlinear estimator theory and the straightforward structure of the resulting solution. The proposed method is implemented and validated experimentally on DC servo system. Experimental results demonstrate that the technique can produce acceptable performance in terms of fault detection and false alarm.

Wavelet - Based principal component analysis for process monitoring with experimental application

PCA methods for Fault Detection (FD) use data collected from a steady-state process to monitor T2 statistics with a fixed threshold. The fixed threshold method causes false alarms in the transient state of the system. To overcome the false alarms arising from the transient state the combination of the fixed and adaptive threshold (Tcomb) based PCA method is used. But the measurements noise results in very high Tcomb. This causes to produce the missing fault signal components. The problem can be solved by filtering the noisy measurement signals. The wavelet transform has been widely used in signal de-noising, due to its extraordinary time-frequency representation capability. In this paper, a new PCA method based on wavelet is proposed to overcome false alarms which occur in the transient states according to changing process conditions and the missing data problem. The proposed method is implemented and validated experimentally on an electromechanical system. Experimental results illustrate the much better fault detection performance of the proposed method in comparison with classical PCA monitoring and process controlling charts.

Photocurrent Measurements in Operating a‐Si:H p‐i‐n Solar Cells with Different P‐Layer Conditions

I‐V characteristics, light intensity‐ and modulation frequency‐ dependences, and spectral distributions of photocurrent of three‐types of a‐Si:H p‐i‐n devices with different p‐layer conditions (baseline, thinner and thicker) have been measured at room temperature. Dc and chopped light from a HeNe laser were used for excitation. From the results, the fill‐factor, quantum efficiency, carrier lifetime, and the exponent v in the power‐low relationship, Iph ∼ Gv between photocurrent and generation rate were obtained and compared under different bias conditions and modulation frequencies. The results were interpreted suitably due to radiation loss and recombination mechanisms.