Enhanced genetic algorithm-based back propagation neural network to diagnose conditions of multiple-bearing system

Condition diagnosis of critical system such as multiple-bearing system is one of the most important maintenance activities in industry because it is essential that faults are detected early before the performance of the whole system is affected. Currently, the most significant issues in condition diagnosis are how to improve accuracy and stability of accuracy, as well as lessen the complexity of the diagnosis which would reduce processing time. Researchers have developed diagnosis techniques based on metaheuristic, specifically, Back Propagation Neural Network (BPNN) for single bearing system and small numbers of condition classes. However, they are not directly applicable or effective for multiple-bearing system because the diagnosis accuracy achieved is unsatisfactory. Therefore, this research proposed hybrid techniques to improve the performance of BPNN in terms of accuracy and stability of accuracy by using Adaptive Genetic Algorithm and Back Propagation Neural Network (AGA-BPNN), and multiple BPNN with AGA-BPNN (mBPNNAGA- BPNN). These techniques are tested and validated on vibration signal data of multiple-bearing system. Experimental results showed the proposed techniques outperformed the BPPN in condition diagnosis. However, the large number of features from multiple-bearing system has affected the complexity of AGA-BPNN and mBPNN-AGA-BPNN, and significantly increased the amount of required processing time. Thus to investigate further, whether the number of features required can be reduced without compromising the diagnosis accuracy and stability, Grey Relational Analysis (GRA) was applied to determine the most dominant features in reducing the complexity of the diagnosis techniques. The experimental results showed that the hybrid of GRA and mBPNN-AGA-BPNN achieved accuracies of 99% for training, 100% for validation and 100% for testing. Besides that, the performance of the proposed hybrid accuracy increased by 11.9%, 13.5% and 11.9% in training, validation and testing respectively when compared to the standard BPNN. This hybrid has lessened the complexity which reduced nearly 55.96% of processing time. Furthermore, the hybrid has improved the stability of the accuracy whereby the differences in accuracy between the maximum and minimum values were 0.2%, 0% and 0% for training, validation and testing respectively. Hence, it can be concluded that the proposed diagnosis techniques have improved the accuracy and stability of accuracy within the minimum complexity and significantly reduced processing time

Failure Prognosis of Wind Turbine Components

Wind energy is playing an increasingly significant role in the World\u27s energy supply mix. In North America, many utility-scale wind turbines are approaching, or are beyond the half-way point of their originally anticipated lifespan. Accurate estimation of the times to failure of major turbine components can provide wind farm owners insight into how to optimize the life and value of their farm assets. This dissertation deals with fault detection and failure prognosis of critical wind turbine sub-assemblies, including generators, blades, and bearings based on data-driven approaches. The main aim of the data-driven methods is to utilize measurement data from the system and forecast the Remaining Useful Life (RUL) of faulty components accurately and efficiently. The main contributions of this dissertation are in the application of ALTA lifetime analysis to help illustrate a possible relationship between varying loads and generators reliability, a wavelet-based Probability Density Function (PDF) to effectively detecting incipient wind turbine blade failure, an adaptive Bayesian algorithm for modeling the uncertainty inherent in the bearings RUL prediction horizon, and a Hidden Markov Model (HMM) for characterizing the bearing damage progression based on varying operating states to mimic a real condition in which wind turbines operate and to recognize that the damage progression is a function of the stress applied to each component using data from historical failures across three different Canadian wind farms

Linear feature selection and classification using PNN and SFAM neural networks for a nearly online diagnosis of bearing naturally progressing degradations.

International audienceIn this work, an effort is made to characterize seven bearing states depending on the energy entropy of Intrinsic Mode Functions (IMFs) resulted from the Empirical Modes Decomposition (EMD).Three run-to-failure bearing vibration signals representing different defects either degraded or different failing components (roller, inner race and outer race) with healthy state lead to seven bearing states under study. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are used for feature reduction. Then, six classification scenarios are processed via a Probabilistic Neural Network (PNN) and a Simplified Fuzzy Adaptive resonance theory Map (SFAM) neural network. In other words, the three extracted feature data bases (EMD, PCA and LDA features) are processed firstly with SFAM and secondly with a combination of PNN-SFAM. The computation of classification accuracy and scattering criterion for each scenario shows that the EMD-LDA-PNN-SFAM combination is the suitable strategy for online bearing fault diagnosis. The proposed methodology reveals better generalization capability compared to previous works and it’s validated by an online bearing fault diagnosis. The proposed strategy can be applied for the decision making of several assets

Machine Learning in Tribology

Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an increasing number of advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand existing knowledge. Therefore, modern machine learning (ML) or artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient or even real-time way. Thus, their potential also goes beyond purely academic aspects into actual industrial applications. To help pave the way, this article collection aimed to present the latest research on ML or AI approaches for solving tribology-related issues generating true added value beyond just buzzwords. In this sense, this Special Issue can support researchers in identifying initial selections and best practice solutions for ML in tribology

Roles of polymorphisms and expression of genes coding for chemokines CX3C ligand 1 and CXC ligand 16 and their receptors in the development and progression of multiple sclerosis in Serbia

Multipla skleroza je hroniĉna inflamatorna, autoimunska, demijelinizaciona i neurodegenerativna bolest centralnog nervnog sistema (CNS-a). Hemokini i njihovi receptori predstavljaju znaĉajne medijatore inflamacije koji uĉestvuju u patogenezi odreĊenih hroniĉnih inflamatornih i autoimunskih bolesti meĊu kojima je i multipla skleroza. Ciljni hemokini u ovoj studiji, CX3C ligand 1 (CX3CL1) i CXC ligand 16 (CXCL16), specifiĉni su po tome što postoje u dve forme - kao transmembranski adhezivni molekuli i kao solubilni hemoatraktanti koji nastaju nakon proteolitiĉkog seĉenja vanćelijskih hemokinskih domena njihovih transmembranskih formi. U toku inflamatornog odgovora, na membrani endotelnih vaskularnih ćelija eksprimirani su CX3CL1 i CXCL16, a na membrani leukocita receptori za CX3CL1 (CX3CR1) i CXCL16 (CXCR6), te ovi hemokini i njihovi receptori posreduju u prodiranju leukocita iz krvi u tkivo zahvaćeno inflamacijom, podsticanjem hemotaksije i adhezije leukocita za aktivirani endotel krvnog suda. Ova studija obuhvata genetsko-epidemiološku analizu polimorfizama zamena pojedinaĉnih nukleotida u kodirajućim regionima gena, koje rezultuju zamenama aminokiselina. To su polimorfizmi V249I i T280M u genu za CX3CR1, i I123T i A181V u genu za CXCL16. U prethodnim studijama je pokazano da ovi genski polimorfizmi menjaju funkcionalna svojstva CX3CR1 i CXCL16, kao i da su asocirani sa patogenezom odreĊenih hroniĉnih inflamatornih bolesti. Uzimajući to u obzir, ova studija je imala za cilj da po prvi put ispita asocijaciju navedenih polimorfizama u genima za CX3CR1 i CXCL16 sa nastankom i progresijom multiple skleroze. Primenom alel-specifiĉne PCR metode i PIRA PCR-RFLP metode detektovani su genotipovi polimorfizama V249I i T280M u genu za CX3CR1, kod zdravih kontrola i pacijenata sa multiplom sklerozom. UtvrĊeno je da haplotip I249T280 u genu za CX3CR1 ima znaĉajno veću uĉestalost kod pacijenata sa relapsno-remitentnom (RR) formom, u odnosu na pacijente sa sekundarno-progresivnom (SP) formom multiple skleroze, što znaĉi da ovaj haplotip ima protektivni efekat na progresiju RR u SP formu bolesti...Multiple sclerosis is a chronic inflammatory, autoimmune, demyelinating and neurodegenerative disease of the central nervous system (CNS). Chemokines and their receptors are important mediators of inflammation, which are involved in pathogenesis of certain chronic inflammatory and autoimmune diseases including multiple sclerosis. Chemokines of interest in this study, CX3C ligand 1 (CX3CL1) and CXC ligand 16 (CXCL16), are specific in that they can exist either as transmembrane adhesion molecules or soluble chemoattractants being generated by proteolytic cleavage of their transmembrane forms’ extracellular domains. During the inflammatory response, CX3CL1 and CXCL16 are expressed on the surface of vascular endothelium, while the leukocytes produce membrane receptors for CX3CL1 (CX3CR1) and CXCL16 (CXCR6). Therefore, these chemokines and their receptors mediate the infiltration of leukocytes from blood into the inflamed tissue areas, by stimulation of both chemotaxis and adhesion of leukocytes to the activated endothelium of blood vessels. This study is based on genetic epidemiological analysis of single nucleotide polymorphisms, which are located in the coding regions of genes and result in amino acids’ substitutions. These are V249I and T280M substitutions in the gene coding for CX3CR1, and I123T and A181V substitutions in the gene coding for CXCL16. In previous studies these polymorphisms have been associated with the functional properties of CX3CR1 and CXCL16 as well as the pathogenesis of certain chronic inflammatory diseases. Therefore, this study aimed to investigate the association of the polymorphisms in CX3CR1 and CXCL16 genes with the development and progression of multiple sclerosis. Using the allele-specific PCR and PIRA PCR-RFLP methods, genotypes of CX3CR1 V249I and T280M polymorphisms were detected in healthy controls and patients with multiple sclerosis. Following statistical analysis showed significantly higher frequency of CX3CR1 I249T280 haplotype in patients with relapsingremitting (RR) form, compared to patients with secondary-progressive (SP) form of multiple sclerosis, so this haplotype had a protective effect on progression of RR to SP form of the disease..

The application of time encoded signals to automated machine condition classification using neural networks

This thesis considers the classification of physical states in a simplified gearbox using acoustical data and simple time domain signal shape characterisation techniques allied to a basic feedforward multi-layer perceptron neural network. A novel extension to the signal coding scheme (TES), involving the application of energy based shape descriptors, was developed. This sought specifically to improve the techniques suitability to the identification of mechanical states and was evaluated against the more traditional minima based TES descriptors. The application of learning based identification techniques offers potential advantages over more traditional programmed techniques both in terms of greater noise immunity and in the reduced requirement for highly skilled operators. The practical advantages accrued by using these networks are studied together with some of the problems associated in their use within safety critical monitoring systems.Practical trials were used as a means of developing the TES conversion mechanism and were used to evaluate the requirements of the neural networks being used to classify the data. These assessed the effects upon performance of the acquisition and digital signal processing phases as well as the subsequent training requirements of networks used for accurate condition classification. Both random data selection and more operator intensive performance based selection processes were evaluated for training. Some rudimentary studies were performed on the internal architectural configuration of the neural networks in order to quantify its influence on the classification process, specifically its effect upon fault resolution enhancement.The techniques have proved to be successful in separating several unique physical states without the necessity for complex state definitions to be identified in advance. Both the computational demands and the practical constraints arising from the use of these techniques fall within the bounds of a realisable system

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field