Intelligent pattern recognition of a SLM machine process and sensor data

Selective Laser Melting is an additive manufacturing process, in which the research has been increasing over the past few years to meet customer-specific requirements. Therefore, new manufacturing parameters have been monitored raising the number of sensors in the machines. Consequently, it leads to a bigger amount of data and difficulties to perform manual data analysis. In order to improve the analysis, this paper illustrates a possibility of pattern recognition using a different historical process and sensors data from a SLM machine. The results are evaluated using an intelligent tool for algorithms configuration and data analysis developed at Fraunhofer IPK

Fuzzy Logic

Fuzzy Logic is becoming an essential method of solving problems in all domains. It gives tremendous impact on the design of autonomous intelligent systems. The purpose of this book is to introduce Hybrid Algorithms, Techniques, and Implementations of Fuzzy Logic. The book consists of thirteen chapters highlighting models and principles of fuzzy logic and issues on its techniques and implementations. The intended readers of this book are engineers, researchers, and graduate students interested in fuzzy logic systems

Machine learning and deep learning based methods toward Industry 4.0 predictive maintenance in induction motors: Α state of the art survey

Purpose: Developments in Industry 4.0 technologies and Artificial Intelligence (AI) have enabled data-driven manufacturing. Predictive maintenance (PdM) has therefore become the prominent approach for fault detection and diagnosis (FD/D) of induction motors (IMs). The maintenance and early FD/D of IMs are critical processes, considering that they constitute the main power source in the industrial production environment. Machine learning (ML) methods have enhanced the performance and reliability of PdM. Various deep learning (DL) based FD/D methods have emerged in recent years, providing automatic feature engineering and learning and thereby alleviating drawbacks of traditional ML based methods. This paper presents a comprehensive survey of ML and DL based FD/D methods of IMs that have emerged since 2015. An overview of the main DL architectures used for this purpose is also presented. A discussion of the recent trends is given as well as future directions for research. Design/methodology/approach: A comprehensive survey has been carried out through all available publication databases using related keywords. Classification of the reviewed works has been done according to the main ML and DL techniques and algorithms Findings: DL based PdM methods have been mainly introduced and implemented for IM fault diagnosis in recent years. Novel DL FD/D methods are based on single DL techniques as well as hybrid techniques. DL methods have also been used for signal preprocessing and moreover, have been combined with traditional ML algorithms to enhance the FD/D performance in feature engineering. Publicly available datasets have been mostly used to test the performance of the developed methods, however industrial datasets should become available as well. Multi-agent system (MAS) based PdM employing ML classifiers has been explored. Several methods have investigated multiple IM faults, however, the presence of multiple faults occurring simultaneously has rarely been investigated. Originality/value: The paper presents a comprehensive review of the recent advances in PdM of IMs based on ML and DL methods that have emerged since 2015Peer Reviewe

Tekerlek içi elektrik motorlarında yapay zeka tabanlı arıza teşhisi

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Elektrik motorları yaygın kullanımıyla insan hayatının vazgeçilmez bir parçasıdır. Tekerlek içi elektrik motorları elektrik enerjisi ile ulaşım sektörünün kesişim noktasında, ulaşım sektöründe gittikçe yaygınlaşacağı ön görülen BLDC (brushless direct current, fırçasız doğru akım) motorlardır. Tekerlek içi elektrik motorları, yapıları itibariyle elektrikli araçlarda kullanıma uygundur. İnsan hayatı ile doğrudan veya dolaylı yollarla bağlantılı olan ulaşım sektörü büyük öneme sahiptir. Bu sebeple tekerlek içi elektrik motorlarının daha az devre dışı kalan ve güvenli motorlar olması gerekmektedir. Tekerlek içi elektrik motorları için arıza teşhisi çalışmalarının amacı motorun daha güvenilir ve verimli olmasını sağlamaktır. Bunun için, motorların devre dışı sürelerinin hızlı atlatılması gerekmektedir. Bu tez çalışmasında, öncelikle arızalı durumlar arasında farklar oluşturabilecek giriş değişkenleri belirlenmiş, yapay zeka tekniklerinin giriş değişkenleri olarak tespit edilmiştir. Tasarlanan ve gerçekleştirilen test ve deney düzeneği sayesinde, tekerlek içi elektrik motorunun arıza teşhisi için yapay zeka tekniklerinin giriş değişkenleri olarak belirlenen tork, devir sayısı, besleme akımı, faz akımları ve besleme gerilimi gibi değişkenlerin ölçümlerinin yapılabilmesi ve motorun mekanik olarak yüklenebilmesi sağlanmıştır. Arıza teşhisi çalışması için ileri beslemeli geri yayılımlı yapay sinir ağı, kaskat ileri beslemeli yapay sinir ağı, Elman yapay sinir ağı, katman yinelemeli yapay sinir ağı ve bulanık mantık yöntemi kullanılmıştır. Kullanılan yapay zeka tekniklerinin başarı düzeyleri, gerçekleştirilen testlerle ölçülülüp karşılaştırılarak, en başarılı sonuçları veren yapay zeka tekniği, gerçek zamanlı arıza teşhisinde de uygulanmıştır. Arıza teşhisi sisteminin çıkışı olarak tespit edilen bulanık mantık için 6 durum ve yapay sinir ağları için 14 durum (13'ü arıza, 1'i normal), yüksek başarı yüzdeleriyle teşhis edilmiştir. İleri beslemeli yapay sinir ağı en başarılı ağ olarak tespit edilmiştir. Daha sonra, tasarlanan gerçek zamanlı arıza teşhisi sistemine dahil edilen ileri beslemeli yapay sinir ağı, 14 ayrı durumun teşhisini başarıyla gerçekleştirmiştir. Bu çalışma, tekerlek içi elektrik motorlarında oluşabilecek arızaların başlangıç aşamasında teşhisi sayesinde, arızaların genişlemesi engellenerek arıza ve bakım maliyetinin düşürülmesi, verimi düşüren arızaların teşhisi sayesinde verim artışı ve motorların daha güvenli kullanımı konularında katkılar sağlayacaktır.Electrical motors are a commonly used indispensable part of human life. Hub motors (in-wheel BLDC motors) are the members of BLDC (Brushless Direct Current) motors family, located at the intersection point of transportation area and electrical energy. They are also used in electrical vehicles and expected to be used more frequently in time. Hub motors are suitable for electrical vehicles structurally. Transportation is very important because of direct and indirect relation with human life. Therefore, hub motors must be more reliable and must be operating with less downtimes. The aim of fault diagnosis studies for hub motors is to make the hub motors more reliable and efficient. Hence, less downtime position for hub motors can be achieved. In this thesis, input variables of artificial intelligence techniques were determined firstly for detecting the differences of various faults by detecting the differences of input signals. Test set was designed for acquiring the determined data of torque, speed, source current, coil currents and source voltage as input variables for fault diagnosis of hub motor. Loading the hub motor mechanically is also possible with this test set. Feed-forward backpropagation neural network, cascade feed-forward neural network, Elman neural network, layer recurrent neural network and fuzzy logic based systems were designed and used for fault diagnosis of hub motor. Success percentages for fault diagnosis of all artificial intelligence techniques were tested and compared with eachother to choose the best performance technique for designing a real-time fault diagnosis system. Feed-forward backpropagation neural network was detected as the most successful artificial intelligence technique and used in the designed real time fault diagnosis system. 14 situations as 13 faults and normal situation, were successfully diagnosed. This study supports hub motors about safety and efficiency, with diagnosis of faults at beginning phase, with decreasing maintenance-mending costs, and with diagnosis of faults which reduce efficiency

ROBUST FAULT ANALYSIS FOR PERMANENT MAGNET DC MOTOR IN SAFETY CRITICAL APPLICATIONS

Robust fault analysis (FA) including the diagnosis of faults and predicting their level of severity is necessary to optimise maintenance and improve reliability of Aircraft. Early diagnosis of faults that might occur in the supervised process renders it possible to perform important preventative actions. The proposed diagnostic models were validated in two experimental tests. The first test concerned a single localised and generalised roller element bearing fault in a permanent magnet brushless DC (PMBLDC) motor. Rolling element bearing defect is one of the main reasons for breakdown in electrical machines. Vibration and current are analysed under stationary and non-stationary load and speed conditions, for a variety of bearing fault severities, and for both local and global bearing faults. The second test examined the case of an unbalance rotor due to blade faults in a thruster, motor based on a permanent magnet brushed DC (PMBDC) motor. A variety of blade fault conditions were investigated, over a wide range of rotation speeds. The test used both discrete wavelet transform (DWT) to extract the useful features, and then feature reduction techniques to avoid redundant features. This reduces computation requirements and the time taken for classification by the application of an orthogonal fuzzy neighbourhood discriminant analysis (OFNDA) approach. The real time monitoring of motor operating conditions is an advanced technique that presents the real performance of the motor, so that the dynamic recurrent neural network (DRNN) proposed predicts the conditions of components and classifies the different faults under different operating conditions. The results obtained from real time simulation demonstrate the effectiveness and reliability of the proposed methodology in accurately classifying faults and predicting levels of fault severity.the Iraqi Ministry of Higher Education and Scientific Researc

IoT Applications Computing

The evolution of emerging and innovative technologies based on Industry 4.0 concepts are transforming society and industry into a fully digitized and networked globe. Sensing, communications, and computing embedded with ambient intelligence are at the heart of the Internet of Things (IoT), the Industrial Internet of Things (IIoT), and Industry 4.0 technologies with expanding applications in manufacturing, transportation, health, building automation, agriculture, and the environment. It is expected that the emerging technology clusters of ambient intelligence computing will not only transform modern industry but also advance societal health and wellness, as well as and make the environment more sustainable. This book uses an interdisciplinary approach to explain the complex issue of scientific and technological innovations largely based on intelligent computing