Observer-biased bearing condition monitoring: from fault detection to multi-fault classification

Bearings are simultaneously a fundamental component and one of the principal causes of failure in rotary machinery. The work focuses on the employment of fuzzy clustering for bearing condition monitoring, i.e., fault detection and classification. The output of a clustering algorithm is a data partition (a set of clusters) which is merely a hypothesis on the structure of the data. This hypothesis requires validation by domain experts. In general, clustering algorithms allow a limited usage of domain knowledge on the cluster formation process. In this study, a novel method allowing for interactive clustering in bearing fault diagnosis is proposed. The method resorts to shrinkage to generalize an otherwise unbiased clustering algorithm into a biased one. In this way, the method provides a natural and intuitive way to control the cluster formation process, allowing for the employment of domain knowledge to guiding it. The domain expert can select a desirable level of granularity ranging from fault detection to classification of a variable number of faults and can select a specific region of the feature space for detailed analysis. Moreover, experimental results under realistic conditions show that the adopted algorithm outperforms the corresponding unbiased algorithm (fuzzy c-means) which is being widely used in this type of problems. (C) 2016 Elsevier Ltd. All rights reserved.Grant number: 145602

Modeling and Recognition of Smart Grid Faults by a Combined Approach of Dissimilarity Learning and One-Class Classification

Detecting faults in electrical power grids is of paramount importance, either from the electricity operator and consumer viewpoints. Modern electric power grids (smart grids) are equipped with smart sensors that allow to gather real-time information regarding the physical status of all the component elements belonging to the whole infrastructure (e.g., cables and related insulation, transformers, breakers and so on). In real-world smart grid systems, usually, additional information that are related to the operational status of the grid itself are collected such as meteorological information. Designing a suitable recognition (discrimination) model of faults in a real-world smart grid system is hence a challenging task. This follows from the heterogeneity of the information that actually determine a typical fault condition. The second point is that, for synthesizing a recognition model, in practice only the conditions of observed faults are usually meaningful. Therefore, a suitable recognition model should be synthesized by making use of the observed fault conditions only. In this paper, we deal with the problem of modeling and recognizing faults in a real-world smart grid system, which supplies the entire city of Rome, Italy. Recognition of faults is addressed by following a combined approach of multiple dissimilarity measures customization and one-class classification techniques. We provide here an in-depth study related to the available data and to the models synthesized by the proposed one-class classifier. We offer also a comprehensive analysis of the fault recognition results by exploiting a fuzzy set based reliability decision rule

Modified Kohonen network algorithm for selection of the initial centres of Gustafson-Kessel algorithm in credit scoring

Credit risk assessment has become an important topic in financial risk administration. Fuzzy clustering analysis has been applied in credit scoring. Gustafson-Kessel (GK) algorithm has been utilised to cluster creditworthy customers as against non-creditworthy ones. A good clustering analysis implemented by good Initial Centres of clusters should be selected. To overcome this problem of Gustafson-Kessel (GK) algorithm, we proposed a modified version of Kohonen Network (KN) algorithm to select the initial centres. Utilising similar degree between points to get similarity density, and then by means of maximum density points selecting; the modified Kohonen Network method generate clustering initial centres to get more reasonable clustering results. The comparative was conducted using three credit scoring datasets: Australian, German and Taiwan. Internal and external indexes of validity clustering are computed and the proposed method was found to have the best performance in these three data sets

AI-enhanced diagnosis of challenging lesions in breast MRI: a methodology and application primer

Computer-aided diagnosis (CAD) systems have become an important tool in the assessment of breast tumors with magnetic resonance imaging (MRI). CAD systems can be used for the detection and diagnosis of breast tumors as a “second opinion” review complementing the radiologist’s review. CAD systems have many common parts such as image pre-processing, tumor feature extraction and data classification that are mostly based on machine learning (ML) techniques. In this review paper, we describe the application of ML-based CAD systems in MRI of the breast covering the detection of diagnostically challenging lesions such as non-mass enhancing (NME) lesions, multiparametric MRI, neo-adjuvant chemotherapy (NAC) and radiomics all applied to NME. Since ML has been widely used in the medical imaging community, we provide an overview about the state-ofthe-art and novel techniques applied as classifiers to CAD systems. The differences in the CAD systems in MRI of the breast for several standard and novel applications for NME are explained in detail to provide important examples illustrating: (i) CAD for the detection and diagnosis, (ii) CAD in multi-parametric imaging (iii) CAD in NAC and (iv) breast cancer radiomics. We aim to provide a comparison between these CAD applications and to illustrate a global view on intelligent CAD systems based on ANN in MRI of the breast

Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction

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Prediction of Banks Financial Distress

In this research we conduct a comprehensive review on the existing literature of prediction techniques that have been used to assist on prediction of the bank distress. We categorized the review results on the groups depending on the prediction techniques method, our categorization started by firstly using time factors of the founded literature, so we mark the literature founded in the period (1990-2010) as history of prediction techniques, and after this period until 2013 as recent prediction techniques and then presented the strengths and weaknesses of both. We came out by the fact that there was no specific type fit with all bank distress issue although we found that intelligent hybrid techniques considered the most candidates methods in term of accuracy and reputatio

Fuzzy Criteria in Multi-objective Feature Selection for Unsupervised Learning

Feature selection in which most informative variables are selected for model generation is an important step in pattern recognition. Here, one often tries to optimize multiple criteria such as discriminating power of the descriptor, performance of model and cardinality of a subset. In this paper we propose a fuzzy criterion in multi-objective unsupervised feature selection by applying the hybridized filter-wrapper approach (FC-MOFS). These formulations allow for an efficient way to pick features from a pool and to avoid misunderstanding of overlapping features via crisp clustered learning in a conventional multi-objective optimization procedure. Moreover, the optimization problem is solved by using non-dominated sorting genetic algorithm, type two (NSGA-II). The performance of the proposed approach is then examined on six benchmark datasets from multiple disciplines and different numbers of features. Systematic comparisons of the proposed method and representative non-fuzzified approaches are illustrated in this work. The experimental studies show a superior performance of the proposed approach in terms of accuracy and feasibility.Algorithms and the Foundations of Software technolog

Review of Different Methods of Abnormal Mass Detection in Digital Mammograms

Various images from massive image databases extract inherent, implanted information or different examples explicitly found in the images. These images may help the community in initial self-screening breast cancer, and primary health care can introduce this method to the community. This study aimed to review the different methods of abnormal mass detection in digital mammograms. One of best methods for the detection of breast malignancy and discovery at a nascent stage is digital mammography. Some of the mammograms with excellent images have a high intensity of resolution that enables preparing images with high computations. The fact that medical images are so common on computers is one of the main things that helps radiologists make diagnoses. Image preprocessing highlights the portion after extraction and arrangement in computerized mammograms. Moreover, the future scope of examination for paving could be the way for a top invention in computer-aided diagnosis (CAD) for mammograms in the coming years. This also distinguished CAD that helped identify strategies for mass widely covered in the study work. However, the identification methods for structural deviation in mammograms are complicated in real-life scenarios. These methods will benefit the public health program if they can be introduced to primary health care's public health screening system. The decision should be made as to which type of technology fits the level of the primary health care system