Performance Analysis of First-Order Plus Dead-Time Processes Using Generalized Predictive Control

Generalized Predictive Control (GPC) is a part of a family of Model Predictive Controllers, which predicts the future output using the concept of Receding Horizon. A cost function is then optimized based on the predicted error and optimized control sequence is calculated. The standard GPC algorithm, proposed in 1987, finds many applications. It has several modifications and extensions to incorporate adaptiveness and constraints. Though GPC is inherently discrete, current research trend is development of continuous GPC algorithms. GPC controller has four tuning parameters, control weight, minimum and maximum prediction horizon and control horizon. This thesis tries to address the effect of these tuning parameters on the performance of a First-Order Plus Dead-Time (FOPDT) process. The performance was measured in terms of three important dynamic characteristics- settling time, rise time and peak overshoot. Lower values of control weight and higher values of prediction horizon gave better performance with regards to these criteria. The observations from these simulations were used as guidelines for tuning the GPC controller for a tank level control system. Single tank system is an example of an FOPDT process. Due to the non-linearity in the process model, it was found to perform better under adaptive GPC (AGPC) algorithm. Simulations were also performed to take the input constraints into consideration. Then, disturbance rejection and robustness behaviour of the adaptive constrained GPC (ACGPC) controller was studied. It was found that, GPC gives many configurable parameters to deal with different control problems. The performance was found to be better than conventional PI controller

Study of noise effect on bearing vibration signal based on statistical parameters

The signals emanating from the bearings are complex and contribute to various distributions. The effect of the distribution and mathematical operations are responsible for the change in the statistical moments. This paper investigates the effect of noise on statistical moments of the bearing vibration signals. Initially, the distribution function for Healthy, inner race defect (IRD), outer race defect (ORD), and ball defect (BD) are tested using Kolmogorov Smirnov test (K-S test). The resulting distributions obtained from the K-S test are normal and Laplacian distributed patterns and convey the faulty state of the bearings. The change in noise levels and their influence on the statistical moments are verified. It is observed, the kurtosis for IRD and ORD decreases with increase in noise, whereas, the trend increases for healthy and BD faults

Bearing fault analysis using kurtosis and wavelet based multi-scale PCA

The vibration signal monitoring that is being generated by a rotor supported by a rolling element bearing is becoming important to define reliability of rotary machine. It is most prudent and useful method for bearing fault detection. Recently, there has been a lot of research on rolling element bearings fault. The kurtosis is most vital parameter to find inner race fault and outer race fault. It is enhanced by a proper selection of variable frame sizes and decompositions levels using wavelet based multi-scale principal component analysis (WMSPCA). It is observed that the kurtosis changes significantly as compared to the actual kurtosis of the un-decomposed faulty signals by proper selection of frame size and decompositions level