Martensitic grade stainless steel is generally used to manufacture steam turbine blades in power plants. The material degradation of those turbine blades, due to fatigue, will induce unexpected equipment damage. Fatigue cracks, too small to be detected, can grow severely in the next operating cycle and may cause failure before the next inspection opportunity. Therefore, a nondestructive electromagnetic technique, which is sensitive to microstructure changes in the material, is needed to provide a means to estimate the specimen’s fatigue life. To tackle these challenges, this paper presents a novel Magnetic Barkhausen noise (MBN) technique for garnering information relating to the material microstructure changes under test. The MBN signals are analyzed in time as well as frequency domain to infer material information that are influenced by the samples’ mate- rial state. Principal Component Analysis (PCA) is applied to reduce the dimensionality of feature data and extract higher order features. Afterwards, Probabilistic Neural Network (PNN) classifies the sample based on the percentage fatigue life to discover the most correlated MBN features to indicate the remaining fatigue life. Furthermore, one criticism of MBN is its poor repeatability and stability, therefore, Analysis of Variance (ANOVA) is carried out to analyze the uncertainty associated with MBN measurements. The feasibility of MBN technique is investigated in detecting early stage fatigue, which is associated with plastic deformation in ferromagnetic metallic structures. Experimental results demonstrate that the Magnetic Barkhausen Noise technique is a promising candidate for characterizing.

In the present study, the dynamic behavior of the last stage low-pressure steam turbine blade with fir-tree root at different conditions of blade root flank faces and their interfaces with rotor groove have been analyzed. Modal analysis has been done using a finite element approach to evaluate natural frequencies and evaluation of Campbell diagram generated under these conditions. For this, both healthy and defective blade have been taken. Since the variable crack size of fir-tree root flank has been taken, the excitation pattern has been evaluated due to stiffness variation of the cracked blade. This analysis provides the basis of excitation pattern of cracked blades due to inherent character and critical stressed zone. The outcome of this study forms the guidelines and checks during the fitting of blades in rotor assembly and its checks during health audit, overhaul, overspeed balancing test, and frequency turning.

Wind turbine (WT) blade structural health monitoring (SHM) is important as it allows damage or misalignment to be detected before it causes catastrophic damage such as that caused by the blade striking the tower. Both of these can be very costly and justify the expense of monitoring. This paper aims to deduce whether a SICK DT-50 laser displacement sensor (LDS) installed inside the tower and a half-bridge type II strain gauge bridge installed at the blade root are capable of detecting ice loading, misalignment, and bolt loosening while the WT is running. Blade faults were detected by the virtual instrument, which conducted a z-test at 99% and 98% significance levels for the LDS and at 99.5% and 99% significance levels for the strain gauge. The significance levels chosen correspond to typical Z-values for statistical tests. A higher significance was used for the strain gauge as it used a one-tail test as opposed to a two-tail test for the LDS. The two different tests were used to test for different sensitivities of the tests. The results show that the strain gauge was capable of detecting all the mass loading cases to 99.5% significance, and the LDS was capable of detecting misalignment, bolt loosening, and 3 out of 4 mass loading cases to 99% significance. It was able to detect the least severe mass loading case of 11 g at the root to only a 98% significance.

This paper deals with an application of the Yule series model for contactless (blade tip timing, BTT) measurements. The approach creates a straightforward processing procedure from the experimental data to obtain estimates of blade logarithmic decrement and natural frequency. The existing analytical expressions for confidence intervals enable a priori evaluation for the accuracy of blade oscillation characteristics estimates. Proposed sensors allocation scheme ensures oscillation sampling rates providing efficient estimates of oscillation characteristics. Real-time measurements of blade logarithmic decrement and natural frequency give a chance of this method implementation for research and health monitoring purposes.