Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

In this paper, a fault detection and diagnosis approach adopted for an input-output feedback linearization (IOFL) control of induction motor (IM) drive is proposed. This approach has been employed to detect and identify the simple and mixed broken rotor bars and static air-gap eccentricity faults right from the start its operation by utilizing advanced techniques. Therefore, two techniques are applied: the model-based strategy, which is an online method used to generate residual stator current signal in order to indicate the presence of possible failures by means of the sliding mode observer (SMO) in the closed-loop drive. However, this strategy is not able to recognise the fault types and it can be affected by the other disturbances. Therefore, the offline method using the multi-adaptive neuro-fuzzy inference system (MANAFIS) technique is proposed to identify the faults and distinguish them. However, the MANAFIS required a relevant database to achieve satisfactory results. Hence, the stator current analysis based on the HFFT combination of the Hilbert transform (HT) and Fast Fourier transform (FFT) is applied to extract the amplitude of harmonics due to defects occur and used them as an input data set for the MANFIS under different loads and fault severities. The simulation results show the efficiency of the proposed techniques and its ability to detect and diagnose any minor faults in a closed-loop drive of IM

Pembangunan model penentuan keperluan perumahan kajian kes: Johor Bahru, Malaysia

Perumahan merupakan satu komponen penting dalam pembangunan ekonomi di mana ia telah menjadi dasar kerajaan untuk menyediakan rumah bagi setiap rakyat. Rancangan Malaysia terdahulu telah cuba merancang bagi merealisasikan dasar ini. Walaupun anggaran keperluan perumahan dibuat di bawah Rancangan Malaysia, namun anggaran tersebut tidak membayangkan keperluan sebenar pembeli dan penyewa rumah di Malaysia. Negara-negara maju telah menggunakan pelbagai model dalam menentukan keperluan perumahan. Namun begitu, model-model tersebut tidak sesuai digunakan di Malaysia kerana data yang terhad. Kajian ini memfokuskan kepada dua objektif iaitu, mengenal pasti model dan faktor yang signifikan bagi menentukan keperluan perumahan, dan kedua menghasilkan model penentuan keperluan perumahan di Malaysia. Skop kajian ini tertumpu kepada pembeli dan penyewa rumah di Daerah Johor Bahru yang dipilih melalui kaedah pesampelan kelompok pelbagai peringkat. Data diperolehi melalui borang kaji selidik dan dianalisis menggunakan pendekatan kuantitatif. Analisis statistik deskriptif digunakan bagi menghuraikan taburan kekerapan, peratus, min, dan sisihan piawai manakala statistik inferensi iaitu ujian Korelasi Pearson dan Regresi Pelbagai digunakan untuk pembentukan model. Dengan menggunakan kaedah Enter, satu model yang signifikan dapat dihasilkan (F4,178 = 353.699 p < 0.05. Adjusted R square = .886) yang signifikan terhadap dua faktor utama iaitu demografi dan kemampuan. Model yang dihasilkan bagi kajian ini adalah General Linear Model. Model ini dapat digunakan bagi menentukan keperluan perumahan di Johor Bahru. Ia juga berfungsi sebagai alat penting dalam perancangan sektor perumahan pada masa hadapan di Malaysia

Profitability, reliability and condition based monitoring of LNG floating platforms: a review

The efficiency and profitability of Floating, Production, Storage and Offloading platform (FPSO) terminals depends on various factors such as LNG liquefaction process type, system reliability and maintenance approach. This review is organized along the following research questions: (i) what are the economic benefit of FPSO and how does the liquefaction process type affect its profitability profile?, (ii) how to improve the reliability of the liquefaction system as key section? and finally (iii) what are the major CBM techniques applied on FPSO. The paper concluded the literature and identified the research shortcomings in order to improve profitability, efficiency and availability of FPSOs

Analytical study of computer vision-based pavement crack quantification using machine learning techniques

Image-based techniques are a promising non-destructive approach for road pavement condition evaluation. The main objective of this study is to extract, quantify and evaluate important surface defects, such as cracks, using an automated computer vision-based system to provide a better understanding of the pavement deterioration process. To achieve this objective, an automated crack-recognition software was developed, employing a series of image processing algorithms of crack extraction, crack grouping, and crack detection. Bottom-hat morphological technique was used to remove the random background of pavement images and extract cracks, selectively based on their shapes, sizes, and intensities using a relatively small number of user-defined parameters. A technical challenge with crack extraction algorithms, including the Bottom-hat transform, is that extracted crack pixels are usually fragmented along crack paths. For de-fragmenting those crack pixels, a novel crack-grouping algorithm is proposed as an image segmentation method, so called MorphLink-C. Statistical validation of this method using flexible pavement images indicated that MorphLink-C not only improves crack-detection accuracy but also reduces crack detection time. Crack characterization was performed by analysing imagerial features of the extracted crack image components. A comprehensive statistical analysis was conducted using filter feature subset selection (FSS) methods, including Fischer score, Gini index, information gain, ReliefF, mRmR, and FCBF to understand the statistical characteristics of cracks in different deterioration stages. Statistical significance of crack features was ranked based on their relevancy and redundancy. The statistical method used in this study can be employed to avoid subjective crack rating based on human visual inspection. Moreover, the statistical information can be used as fundamental data to justify rehabilitation policies in pavement maintenance. Finally, the application of four classification algorithms, including Artificial Neural Network (ANN), Decision Tree (DT), k-Nearest Neighbours (kNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) is investigated for the crack detection framework. The classifiers were evaluated in the following five criteria: 1) prediction performance, 2) computation time, 3) stability of results for highly imbalanced datasets in which, the number of crack objects are significantly smaller than the number of non-crack objects, 4) stability of the classifiers performance for pavements in different deterioration stages, and 5) interpretability of results and clarity of the procedure. Comparison results indicate the advantages of white-box classification methods for computer vision based pavement evaluation. Although black-box methods, such as ANN provide superior classification performance, white-box methods, such as ANFIS, provide useful information about the logic of classification and the effect of feature values on detection results. Such information can provide further insight for the image-based pavement crack detection application

Deep Featured Adaptive Dense Net Convolutional Neural Network Based Cardiac Risk Prediction in Big Data Healthcare Environment

In recent days, cardiac vascular disease has been one of the deadliest health-affecting factors causing sudden death. So, the importance of early risk prediction through feature analysis has become a big problem in data analysis because more nonlinear time series data increase the feature dimension. Irrelevant feature dimension scaling affects the prediction accuracy and leads to classification inaccuracy. To resolve this problem, we propose an Enhanced Healthcare data analysis model for cardiac data prediction using an adaptive Deep Featured Adaptive Convolution Neural Network for early risk identification. Initially, the preprocessing was augmented to formalize the time series data collected from the CVD-DS dataset. Then the feature evaluation was carried out with the Relative Subset Clustering (RSC) approach. The Cardiac Deficiency Prediction rate (CDPr) was estimated to identify the relational feature to subset margins. Based on the CDPr weight the feature is extracted using Cross-Over Mutual Scaling Feature Selection Model (CMSFS). The selected features get with a deep neural classifier based on logical neurons. They are then constructed into a Dense Net Convolution Neural Network (DN-CNN) classifier to feed forward the feature values and predict the Disease Affection Rate (DAR) by class category. The proposed system produces high prediction accuracy in classification, precision, and recall rate to support premature treatment for early cardiac disease risk prediction.

Radial Power Distribution System Fault Classification Model Based on ANFIS

The classification of problems in power systems plays an extremely important part and has evolved into a necessity that is of the utmost importance to the operation of energy grids. For the purpose of fault classification in IEEE 13 node radial distribution systems, this paper makes use of both an Artificial Neural Network (ANN) and a Neural Fuzzy adaptive Inference System (ANFIS). Simulations of the suggested models are carried out in MATLAB/SIMULINK, and fault currents from all three phases are analyzed in order to extract statistical characteristics. Input data vectors include the standard deviation and correlation factors between the currents of any two phases, while output data vectors include the different sorts of faults. The findings demonstrate that the devised method is appropriate for the classification of all symmetrical and unsymmetrical faults

A sensitivity comparison of Neuro-fuzzy feature extraction methods from bearing failure signals

This thesis presents an account of investigations made into building bearing fault classifiers for outer race faults (ORF), inner race faults (IRF), ball faults (BF) and no fault (NF) cases using wavelet transforms, statistical parameter features and Artificial Neuro-Fuzzy Inference Systems (ANFIS). The test results showed that the ball fault (BF) classifier successfully achieved 100% accuracy without mis-classification, while the outer race fault (ORF), inner race fault (IRF) and no fault (NF) classifiers achieved mixed results

An Adaptive Neuro-Fuzzy Inference System-Based Approach for Oil and Gas Pipeline Defect Depth Estimation

Abstract-To determine the severity of metal-loss defects in oil and gas pipelines, the depth of potential defects, along with their length, needs first to be estimated. For this purpose, pipeline engineers use intelligent Magnetic Flux Leakage (MFL) sensors that scan the metal pipelines and collect defect-related data. However, due to the huge amount of the collected MFL data, the defect depth estimation task is cumbersome, timeconsuming, and error-prone. In this paper, we propose an adaptive neuro-fuzzy inference system (ANFIS)-based approach to estimate defect depths from MFL signals. Depth-related features are first extracted from the MFL signals and then are used to train the neural network to tune the parameters of the membership functions of the fuzzy inference system. A hybrid learning algorithm that combines least-squares and back propagation gradient descent method is adopted. Moreover, to achieve an optimal performance by the proposed approach, highly-discriminant features are selected from the obtained features by using the weight-based support vector machine (SVM). Experimental work has shown that encouraging results are obtained. Within error-tolerance ranges of ±15%, ±20%, ±25%, and ±30%, the depth estimation accuracies obtained by the proposed technique are 80.39%, 87.75%, 91.18%, and 95.59%, respectively. Moreover, further improvement can be easily achieved by incorporating new and more discriminant features

Application of artificial intelligence in fault detection and classification of solar power plants and prediction of power generation of combined cycled power plants

Solar energy is one of the most dependable renewable energy technologies, as it is feasible almost everywhere globally and is environmentally friendly. Photovoltaic-based renewable energy systems are highly susceptible to power grid transients. Their operation may suffer drastically during faults in the solar arrays, power electronics, and the inverter. Thus, it is vital to develop an intelligent mechanism to detect any type of fault or abnormalities within the shortest possible time that will increase reliability and decrease the maintenance cost of the solar system. To accomplish that, in this research, different artificial intelligence (AI) techniques are utilized to develop classification, fault detection, and optimization algorithms for solar photovoltaic (PV) panels. Initially, a convolutional neural network (CNN) model was designed to detect and classify PV modules based on the images taken from the solar panels. It is found that the proposed CNN model can identify the fault with an accuracy of 91.1% for binary (i.e., healthy and faulty) and 88.6% for multi-classification (i.e. cracked, shadowy, dusty and normal). However, sometimes the fault in the solar panel may not be detectable from the images of the solar panels. That is why an adaptive neuro-fuzzy inference system (ANFIS) model is developed to detect and classify the defects of PV systems based on the signals collected from the solar panels. The performance of the developed defect detection and classification algorithms was tested using real-life solar farm datasets. The performance of the proposed ANFIS-based fault detection scheme has been compared with different machine learning algorithms. It is found from the comparative results that the proposed ANFIS-based fault detection technique is robust and straightforward. Thus, the developed ANFISbased intelligent technique will enhance the reliability of the PV system by minimizing the maintenance cost and saving energy. Finally, another ANFIS model is developed to predict the power generation in a combined cycle power plant. The codes were written in MATLAB, and their validity is confirmed with the available ANFIS toolboxes in MATLAB. The proposed ANFIS is found capable of successfully predicting power generation with extremely high accuracy and being much faster than the built-in ANFIS of MATLAB Toolbox. Thus, the developed ANFIS model could be utilized as a promising tool for energy generation applications