Blades rubs and looseness detection in gas turbines – operational field experience and laboratory study

Operational experience showed that blade related failures to be common faults in gas turbines. A review of detection and assessment techniques for blade related failures in gas turbines are presented. This paper examines the use of vibration analysis and monitoring of blade pass frequencies and its side bands for blade rubs and cracks, and wavelet analysis for blades looseness detection. Case studies of operational gas turbines used in power generation are presented. A case of severe rub from a cracked shaft (that exhibited increased vibration trend over time) showed severe harmonics of running speed during a full rub. A longitudinal crack was subsequently found on the rotor shaft. Another case study demonstrated significant increase in amplitudes associated with the blades passing frequencies with increased side band activities from stator blades and labyrinth rubs prior to shaft seizure. Actual field data and historical comparisons of vibration spectra and wavelet maps of the gas turbine are presented. Looseness in the packing pieces of blade roots are also potential problems in some gas turbine designs which are difficult to detect under field conditions. The use of wavelet analysis for simulated blades looseness in a laboratory study showed changes in wavelet maps for rotor coast down measurements suggesting potential in detection for blades looseness. Changes could not be detected from FFT spectra, coast down spectra nor were it detectable from steady state FFT and wavelet analysis

Variational mode decomposition: mode determination method for rotating machinery diagnosis

Variational mode decomposition (VMD) is a modern decomposition method used for many engineering monitoring and diagnosis recently, which replaced traditional empirical mode decomposition (EMD) method. However, the performance of VMD method specifically depends on the parameter that need to pre-determine for VMD method especially the mode number. This paper proposed a mode determination method using signal difference average (SDA) to determine the mode number for the VMD method by taking the advantages of similarities concept between sum of variational mode functions (VMFs) and the input signals. Online high-speed gear and bearing fault data were used to validate the performance of the proposed method. The diagnosis result using frequency spectrum has been compared with traditional EMD method and the proposed method has been proved to be able to provide an accurate number of mode for the VMD method effectively for rotating machinery applications

Self-tune linear adaptive-genetic algorithm for feature selection

Genetic algorithm (GA) is an established machine learning technique used for heuristic optimisation purposes. However, this natural selection-based technique is prone to premature convergence, especially of the local optimum event. The presence of stagnant performance is due to low population diversity and fixed genetic operator setting. Therefore, an adaptive algorithm, the Self-Tune Linear Adaptive-GA (STLA-GA), is presented in order to avoid suboptimal solutions in feature selection case studies. STLA-GA performs parameter tuning for mutation probability rate, population size, maximum generation number and novel convergence threshold while simultaneously updating the stopping criteria by adopting an exploration-exploitation cycle. The exploration-exploitation cycle embedded in STLA-GA is a function of the latest classifier performance. Compared to standard feature selection practice, the proposed STLA-GA delivers multi-fold benefits, including overcoming local optimum solutions, yielding higher feature subset reduction rates, removing manual parameter tuning, eliminating premature convergence and preventing excessive computational cost, which is due to unstable parameter tuning feedback

Noise Eliminated Ensemble Empirical Mode Decomposition Scalogram Analysis for Rotating Machinery Fault Diagnosis

Rotating machinery is one of the major components of industries that suffer from various faults due to the constant workload. Therefore, a fast and reliable fault diagnosis method is essential for machine condition monitoring. In this study, noise eliminated ensemble empirical mode decomposition (NEEEMD) was used for fault feature extraction. A convolution neural network (CNN) classifier was applied for classification because of its feature learning ability. A generalized CNN architecture was proposed to reduce the model training time. A sample size of 64×64×3 pixels RGB scalograms are used as the classifier input. However, CNN requires a large number of training data to achieve high accuracy and robustness. Deep convolution generative adversarial network (DCGAN) was applied for data augmentation during the training phase. To evaluate the effectiveness of the proposed feature extraction method, scalograms from related feature extraction methods such as ensemble empirical mode decomposition (EEMD), complementary EEMD (CEEMD), and continuous wavelet transform (CWT) are classified. The effectiveness of scalograms is also validated by comparing the classifier performance using grayscale samples from the raw vibration signals. All the outputs from bearing and blade fault classifiers showed that scalogram samples from the proposed NEEEMD method obtained the highest accuracy, sensitivity, and robustness using CNN. DCGAN was applied with the proposed NEEEMD scalograms to further increase the CNN classifier’s performance and identify the optimal number of training data. After training the classifier using augmented samples, the results showed that the classifier obtained even higher validation and test accuracy with greater robustness. The proposed method can be used as a more generalized and robust method for rotating machinery fault diagnosis

Denoising diffusion implicit model for bearing fault diagnosis under different working loads

Rotating machineries always operating under different loads and suffer from various types of bearing fault. Thus, bearing fault diagnosis is essential to prevent further loss or damage. Deep learning has been favoured over machine learning recently due to data explosion and its higher performance. In deep learning-based bearing fault diagnosis, vibration signals are usually transformed into images using time frequency analysis methods such as short-time Fourier transform, wavelet transform, and Hilbert-Huang transform. Convolutional neural network (CNN) is widely used for fault classification method. However, the training dataset and testing dataset usually have different load domains due to different working conditions. Obtaining training data of wide range of loadings are impractical and exhausting. Thus, this study is proposed to solve load domain adaptation using denoising diffusion implicit model (DDIM). In this study, synthetic images are generated using DDIM model while only convolutional neural network (CNN) is used as fault classification model. The classification accuracy of testing dataset is obtained using CNN models trained with original training dataset and augmented training dataset. The results showed that the synthetic scalograms could improve the performance of CNN model by 3.3% under different load domains

Blade fault localization with the use of vibration signals through artificial neural network: a data-driven approach

Turbines are significant for extracting energy for petrochemical plants, power generation, and aerospace industries. However, it has been reported that turbine-blade failures are the most common causes of machinery breakdown. Therefore, numerous analyses have been performed to formulate techniques for detecting and classifying the fault of the turbine blade. Nevertheless, the blade fault localization method, performed to locate the faulty parts, is equally important for plant operation and maintenance. Therefore, this study will propose a blade fault localization method centered on time-frequency feature extraction and a machine learning approach. The purpose is to locate the faulty parts of the turbine blade. In addition, experimental research is carried out to simulate various blade faults. It includes blade rubbing, blade parts loss, and twisted blade. An artificial neural network model was developed to localize blade fault through the extracted features with newly proposed and selected features. The classification results indicated that the proposed feature set and feature selection method could be used for blade fault localization. It can be seen from the classification rate for blade faultiness localization

Low-speed bearing fault diagnosis based on ArSSAE model using acoustic emission and vibration signals

The development of rolling element bearing fault diagnosis systems has attracted a great deal of attention due to bearing components having a high tendency toward unexpected failures. However, under low-speed operating conditions, the diagnosis of bearing components remains a problem. In this paper, the adaptive resilient stacked sparse autoencoder (ArSSAE) is proposed to compensate for the shortcomings of conventional fault diagnosis systems at low speed. The efficiency of the proposed ArSSAE model is initially assessed using the CWRU database. Then, the proposed model is evaluated on actual vibration analysis (VA) and acoustic emission (AE) signals measured on a bearing test rig at low operating speeds (48-480 rpm). Overall, the analysis demonstrates that the ArSSAE model is able to perform an accurate diagnosis of bearing components under low-speed conditions

Challenges and opportunities of deep learning models for machinery fault detection and diagnosis: a review

In the age of industry 4.0, deep learning has attracted increasing interest for various research applications. In recent years, deep learning models have been extensively implemented in machinery fault detection and diagnosis (FDD) systems. The deep architecture's automated feature learning process offers great potential to solve problems with traditional fault detection and diagnosis (TFDD) systems. TFDD relies on manual feature selection, which requires prior knowledge of the data and is time intensive. However, the high performance of deep learning comes with challenges and costs. This paper presents a review of deep learning challenges related to machinery fault detection and diagnosis systems. The potential for future work on deep learning implementation in FDD systems is briefly discussed

Automated harmonic signal removal technique using stochastic subspace-based image feature extraction

This paper presents automated harmonic removal as a desirable solution to effectively identify and discard the harmonic influence over the output signal by neglecting any user-defined parameter at start-up and automatically reconstruct back to become a useful output signal prior to system identification. Stochastic subspace-based algorithms (SSI) methods are the most practical tool due to the consistency in modal parameters estimation. However, it will be problematic when applied to structures with rotating machines and the presence of harmonic excitations. Difficulties arise when automating this procedure without any human interaction and the problem is still unresolved because stochastic subspace-based algorithms (SSI) methods still require parameters (the maximum within-cluster distance) that are compulsory to be defined at start-up for each analysis of the dataset. Thus, the use of image-based feature extraction for clustering and classification of harmonic components and structural poles directly from a stabilization diagram and for modal system identification is the focus of the present paper. As a fundamental necessary condition, the algorithm has been assessed first from computed numerical responses and then applied to the experimental dataset with the presence of harmonic excitation. Results of the proposed approach for estimating modal parameters demonstrated very high accuracy and exhibited consistent results before and after removing harmonic components from the response signal

Detection of swine hepatitis E virus in the porcine hepatic lesion in Jeju Island

Swine hepatitis E virus (HEV) is an emerging zoonotic pathogen due to its close genomic similarity to human HEV. The prevalence of swine HEV in the hepatic lesion of pigs from the Jeju Island was investigated by reverse transcriptase polymerase chain reaction (RT-PCR). In total, 40 pigs with hepatitis lesions were selected from 19 different farms, based on examination by microscopy. RT-PCR findings revealed swine HEV in 22 cases (55%), including 18 suckling pigs and 4 growing pigs. Several histopathological lesions, including multifocal lymphoplasmacytic hepatitis, portal inflammation, and focal hepatocellular necrosis, were observed in liver sections of swine HEV PCR-positive pigs. The present study suggests that the prevalence of swine HEV is very high in the pig population in Jeju Island, and that pigs are infected at early stages of growth (under 2 months of age). The high prevalence of swine HEV in pigs in Jeju Island and the ability of this virus to infect across species puts people with swine-associated occupations at possible risk of zoonotic infection