Blade fault diagnosis using artificial intelligence technique

Blade fault diagnosis is conventionally based on interpretation of vibration spectrum and wavelet map. These methods are however found to be difficult and subjective as it requires visual interpretation of chart and wavelet color map. To overcome this problem, important features for blade fault diagnosis in a multi row of rotor blade system was selected to develop a novel blade fault diagnosis method based on artificial intelligence techniques to reduce subjective interpretation. Three artificial neural network models were developed to detect blade fault, classify the type of blade fault, and locate the blade fault location. An experimental study was conducted to simulate different types of blade faults involving blade rubbing, loss of blade part, and twisted blade. Vibration signals for all blade fault conditions were measured with a sampling rate of 5 kHz under steady-state conditions at a constant rotating speed. Continuous wavelet transform was used to analyse the vibration signals and its results were used subsequently for feature extraction. Statistical features were extracted from the continuous wavelet coefficients of the rotor operating frequency and its corresponding blade passing frequencies. The extracted statistical features were grouped into three different feature sets. In addition, two new feature sets were proposed: blade statistical curve area and blade statistical summation. The effectiveness of the five different feature sets for blade fault detection, classification, and localisation was investigated. Classification results showed that the statistical features extracted from the operating frequency to be more effective for blade fault detection, classification, and localisation than the statistical features from blade passing frequencies. Feature sets of blade statistical curve area was found to be more effective for blade fault classification, while feature sets of blade statistical summation were more effective for blade fault localisation. The application of feature selection using genetic algorithm showed good accuracy performance with fewer features achieved. The neural network developed for blade fault detection, classification, and localisation achieved accuracy of 100%, 98.15% and 83.47% respectively. With the developed blade fault diagnosis methods, manual interpretation solely dependent on knowledge and the experience of individuals can be reduced. The novel methods can therefore be used as an alternative method for blade fault diagnosis

Solar PV Microgrids Implementation model: A case study of Local Self Governments in the Indian State of Kerala

The State of Kerala in Southern part of India has significant potential for generation of power from renewable energy sources, especially solar energy. Most of the PV projects in the State are being implemented on roof-tops due to the unavailability of land area in the densely populated State for large utility scale PV power plants. The concept of implementing Solar PV projects by empowering Local Self Governments has been explained in this paper by illustrating the case study of the Indian State of Kerala. The respective Local Self Governments like Municipal Corporations are playing a key role in Kerala in this decision-making process to accomplish model carbon free solar communities by installing Solar Photovoltaic Projects. The requirement of local beneficiary will be analysed by local trained experts and feasibility study will be conducted for the beneficiary communities involving residential buildings, local industries and commercial institutions and educational institutions and the PV projects will be implemented utilising the local E.P.C players. Local Self Governments like Municipal Corporations were empowered to plan, formulate and implement their own Solar Photovoltaic Projects. This approach is being implemented in Kerala resulting in solar electrification of local communities/institutions through the decentralised approach. This created a new business model at the local level involving trained manpower and supply chain for meeting the Local Self Government targets for new PV projects in order to achieve the targets of carbon free communities

Design and simulation of solar roof-top projects for an energy self-reliant university campus

A University campus becoming self-reliant in terms of electricity generation is always important. The power requirement of a University campus is mainly for its academic blocks for different departments. The laboratories, libraries, and hostel facilities also require continuous power needs. Conventionally, the electricity needs are met utilizing the power received from the local utility and the University will be paying for it based on the tariff fixed by the local regulators and agreed by the utility. It is proposed to install Solar Roof-top PV power plants in shade-free rooftops of the buildings inside a University campus to offset the University’s electricity needs and to make the University self-reliant in terms of electricity generation and consumption. This integrated and comprehensive solar project is intended to reduce the carbon footprint of the University and aiming for a carbon-neutral campus. Detailed feasibility studies have been conducted for installing solar roof-top power plants in the selected building roof-tops of the University. Based on the feasibility study, design and simulation have been carried out for a total 1.6MWp capacity of solar roof-top projects to completely offset the total electricity needs of the University based on the current electricity consumption patterns. The design and simulation are accomplished utilizing PVsyst software and the economic and environmental analysis has been carried out utilizing RETscreen software for arriving at the result

Comparison of normal and weather corrected performance ratio of photovoltaic solar plants in hot and cold climates

Performance Ratio (PR) is one of the best performance metrics used to assess solar power plant performance. PR is also used for commercial acceptance of an installed PV power plants. If the PR is tested in different climatic conditions or seasons, there are bias errors, affecting the contractual acceptance testing. PR is often corrected to the Standard Test Conditions (STC), resulting in higher PR since modules usually operate at higher temperatures. This research work utilizes NREL's advanced methodology to determine weather corrected PR of PV power plants in six different geographical locations and climatic regions. The Solar PV plant performance is simulated to get the normal PR as per IEC 61724-1:2017. Percentage variation of Weather corrected PR with Normal PR is determined for all these six geographical regions and compared. The weather corrected PR of Montana (with the lowest annual average temperature of 1.58 °C.) PV power plant has a maximum variation of 7.64% from the normal PR during the summer and −8.61% variation in December during the winter. The weather corrected PR of Kuzhalmannam (with the highest annual average temperature of 27.28 °C) PV power plant has a maximum variation of only 1.16% from the normal PR during summer and a variation of −0.91% in July during the rainy season. It is concluded that the metric of weather-corrected PR gains paramount importance for colder areas, whereas it has minimal influence for tropical regions

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

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

Weather impact on solar farm Performance : A comparative analysis of machine learning techniques

Forecasting the performance and energy yield of photovoltaic (PV) farms is crucial for establishing the economic sustainability of a newly installed system. The present study aims to develop a prediction model to forecast an installed PV system’s annual power generation yield and performance ratio (PR) using three environmental input parameters: solar irradiance, wind speed, and ambient air temperature. Three data-based artificial intelligence (AI) techniques, namely, adaptive neuro-fuzzy inference system (ANFIS), response surface methodology (RSM), and artificial neural network (ANN), were employed. The models were developed using three years of data from an operational 2MWp Solar PV Project at Kuzhalmannam, Kerala state, India. Statistical indices such as Pearson’s R, coefficient of determination (R2), root-mean-squared error (RMSE), Nash-Sutcliffe efficiency (NSCE), mean absolute-percentage error (MAPE), Kling-Gupta efficiency (KGE), Taylor’s diagram, and correlation matrix were used to determine the most accurate prediction model. The results demonstrate that ANFIS was the most precise performance ratio prediction model, with an R2 value of 0.9830 and an RMSE of 0.6. It is envisaged that the forecast model would be a valuable tool for policymakers, solar energy researchers, and solar farm developers

Template Synthesis of Ni Nanowires: Characterization and Modelling

Template-assisted electrochemical deposition is a straight forward approach for the synthesis of 1D nanostructures (e.g., nanowire, nanorod, and nanobelt) with controllable morphology. This approach is suitable for mass production as it works at ambient pressure and temperature with the properties of synthesized 1D nanostructures being influenced by synthesis conditions during the electrochemical deposition process. This work aims to investigate the influence of stabilizing agent concentration and heating temperature towards the physical behavior of Nickel (Ni) nanowires synthesized via a template-assisted electrochemical deposition approach. In this research, the electrolyte bath was prepared in three different concentrations of the stabilizing agent (6 g/L, 40 g/L and 70 g/L), and the deposition bath temperature used was 30°C, 70°C, and 110°C respectively. The elemental composition was determined using Energy Dispersive X-ray (EDX) analysis to investigate the percentage of pure Ni element in the synthesized nanowires. The diameter, surface texture, and growth length of the synthesized Ni nanowires were characterized using Field Emission Scanning Electron Microscope (FESEM). X-ray diffractions (XRD) was used for crystal size and crystal orientation analysis. Additionally, the mechanical properties of Ni nanowires were extracted via molecular dynamic simulation. Growth length of Ni nanowires found to be significantly improved as the heating temperature increased, but it decreased when stabilizer agent concentration is high. The diffraction patterns for all synthesis conditions exhibited the synthesis Ni nanowires are polycrystalline as the crystalline planes with Miller indices of 111, 200, and 220. All the investigated nanowires showed ductile failure behavior, a typical behavior at larger length scales of Ni

An improved image processing approach for machinery fault diagnosis

Wavelet analysis has been proven to be effective in analysing non-stationary vibration signals. However, the interpretation of the wavelet analysis results, such as a wavelet scalogram, requires high levels of knowledge and experience, which remains a great challenge to practitioners in the field. Recently, the rapid development and advancement of image processing technologies have shed new light on this challenge. In this study, image features such as Harris Stephens(Harris);speeded-up robust features(SURFs);and binary, robust, invariant, scalable keypoints (BRISKs)were obtained from a red, green, and blue (RGB) colour-filtered wavelet scalogram. Each colour filter generates a set of image features from an RGB-filtered wavelet scalogram. Then, the features were utilised as inputs to the fault classifier, namely the support vector machine (SVM),for fault classification. However, there will be a situation where the classification results from the fault classifier, based on the image generated from the different colour filters, are contradictory to each other. No conclusion can thus be made in these situations. This paper employed the Dempster-Shafer (DS) theory to refine the contradicting results and provide an ultimate conclusion to the machine condition. Therefore, the proposed method has improved the fault classification accuracy from 69% to 78%

Diagnosis of twisted blade in rotor system

This paper studies the diagnosis of twisted blade in a multi-stages rotor system using vibra-tion analysis. Experimental study was undertaken to simulate twisted blade conditions in a three stages rotor system. The feasibility of vibration analysis as the technique to detect twisted blade was investigated in this study. Vibration signals were analyzed with both Fou-rier and Wavelet transforms for comparison purposes. Experimental results show that twisted blade can be detected by comparing the pattern in both the vibration spectrum and wavelet scalogram. The feasibility and effectiveness of wavelet analysis as compared to vibration spectrum to detect twisted blade was also discussed and presented in this paper