Failure Detection by signal similarity measurement of Brushless DC motors

During the last years the Brushless DC (BLDC) motors are gaining popularity as a solution for providing mechanical power, starting from low cost mobility solutions like the electric bikes, to high performance and high reliability aeronautical Electro- Mechanical Actuators (EMAs). In this framework, the availability of fault detection tools suited for these types of machines appears necessary. There is already a vast literature on this topic, but only a small percentage of the proposed techniques are developed to a Technology Readiness Level (TRL) sufficiently high to be implementable in industrial applications. The investigation on the state of the art carried out during the first phase of the present work, tries to collect the articles which are closer to a possible implementation. This choice has been influenced by the author experience when dealing with fault detection papers, which often are oriented towards a more academic public and do not concentrate on the implementation. The methodology used in this work to compile the state of the art has been the Systematic Literature Review (SLR) and it is still not diffused in the engineering world. For this reason a dedicated description has been inserted in the respective chapter of the thesis. From this study, some characteristics needed for the fault detection on electric machine have been listed and a new technique for demagnetisation detection on BLDC motors has been proposed. In the second part of the thesis, it is presented an algorithm to detect demagnetisation based on the dissimilarity between the voltages of the various electric turns of the motor due to this failure. The exposed method presents the advantages of not needing domain transforms or previous knowledge of the motor (made exception for the number of pole-pairs). Furthermore the proposed indicators are fast to be computed and require only the acquisition of motor phases voltages for a mechanical turn. The hypotheses made about the effect of a demagnetisation with Finite Element Method (FEM) have also been confirmed through simulations analysis and the proposed method to detect demagnetisation has been validated with experimental tests on a real motor. 2 Applications and Limitations The presented indicators have been studied, simulated and experimented only on an outrunner, low power BLDC motor. Anyway it is not excluded that, with some adaptation, they could be used on any BLDC motor or also on different types of motors; indeed this is an argument for a future work. Another important consideration is that, in order to detect demagnetisation, the motor should have a number of pole pairs greater than 2. This because the algorithm compares the electric turns between them and it is obviously necessary to have more than one. Another characteristic is that it can only detect partial demagnetisation. The demagnetisation of all the magnets to the same level, although very improbable, would not cause those differences in the voltage signals needed for fault detection. Various tests have been executed both at fixed and variable speed. In the first case it was possible to define a threshold to discern between the healthy and the demagnetised motor, while in the second case, even if the indicators are still separated, it was not possible to define a fixed threshold. Hence, if no classification algorithms are used (Support Vector Machine (SVM), Neural Network (NN), Artificial Intelligence (AI), etc.), the indicator shall be computed when the motor is running in steady state conditions. 3 Advantages The method of fault detection by using the proposed indicators has the main advantage of being straightforwardly applicable with no need of extra hardware. Another important characteristic to be highlighted is that the only previous needed knowledge of the motor is the number of pole-pairs. Also the intermediate data are easy to understand as they represent physical variables of the motor in the time domain. Thanks to this, also no domain transformations for frequency analysis are needed, saving computation time. The algorithm to compute the indicators is composed by few steps, it is fast to execute and does not need complex programming or libraries. Indeed the execution time for the PC implementation is already very low and an optimised implementation in a lower level programming language could easily fit in a microcontroller and be executed at even higher speed, permitting both real time monitoring and punctual testing during maintenance. Furthermore it uses only few and easily obtainable data, which makes it suitable for every industrial implementation and interesting for further academic researches. Having a maximum theoretical value for the indicator is also an important advantage, because it permits to evaluate a motor without previous knowledge of the same; indeed a healthy motor should have an ixc value always very close to this maximum value. It is worth to notice that the proposed indicators have been validated with experimental tests in various conditions, showing both good performances and space for further improvements. Finally, although it is true that constant speed is required for a correct analysis, it is needed for just a mechanical turn, i.e. for few milliseconds. For example if the motor is running at 3000 RPM, a complete turn is executed in 20 ms

Model-Based Fault Detection and Identification for Prognostics of Electromechanical Actuators Using Genetic Algorithms

Traditional hydraulic servomechanisms for aircraft control surfaces are being gradually replaced by newer technologies, such as Electro-Mechanical Actuators (EMAs). Since field data about reliability of EMAs are not available due to their recent adoption, their failure modes are not fully understood yet; therefore, an effective prognostic tool could help detect incipient failures of the flight control system, in order to properly schedule maintenance interventions and replacement of the actuators. A twofold benefit would be achieved: Safety would be improved by avoiding the aircraft to fly with damaged components, and replacement of still functional components would be prevented, reducing maintenance costs. However, EMA prognostic presents a challenge due to the complexity and to the multi-disciplinary nature of the monitored systems. We propose a model-based fault detection and isolation (FDI) method, employing a Genetic Algorithm (GA) to identify failure precursors before the performance of the system starts being compromised. Four different failure modes are considered: dry friction, backlash, partial coil short circuit, and controller gain drift. The method presented in this work is able to deal with the challenge leveraging the system design knowledge in a more effective way than data-driven strategies, and requires less experimental data. To test the proposed tool, a simulated test rig was developed. Two numerical models of the EMA were implemented with different level of detail: A high fidelity model provided the data of the faulty actuator to be analyzed, while a simpler one, computationally lighter but accurate enough to simulate the considered fault modes, was executed iteratively by the GA. The results showed good robustness and precision, allowing the early identification of a system malfunctioning with few false positives or missed failures.https://susy.mdpi

A fuzzy set theory-based fast fault diagnosis approach for rotators of induction motors

Induction motors have been widely used in industry, agriculture, transportation, national defense engineering, etc. Defects of the motors will not only cause the abnormal operation of production equipment but also cause the motor to run in a state of low energy efficiency before evolving into a fault shutdown. The former may lead to the suspension of the production process, while the latter may lead to additional energy loss. This paper studies a fuzzy rule-based expert system for this purpose and focuses on the analysis of many knowledge representation methods and reasoning techniques. The rotator fault of induction motors is analyzed and diagnosed by using this knowledge, and the diagnosis result is displayed. The simulation model can effectively simulate the broken rotator fault by changing the resistance value of the equivalent rotor winding. And the influence of the broken rotor bar fault on the motors is described, which provides a basis for the fault characteristics analysis. The simulation results show that the proposed method can realize fast fault diagnosis for rotators of induction motors

Advances and Technologies in High Voltage Power Systems Operation, Control, Protection and Security

The electrical demands in several countries around the world are increasing due to the huge energy requirements of prosperous economies and the human activities of modern life. In order to economically transfer electrical powers from the generation side to the demand side, these powers need to be transferred at high-voltage levels through suitable transmission systems and power substations. To this end, high-voltage transmission systems and power substations are in demand. Actually, they are at the heart of interconnected power systems, in which any faults might lead to unsuitable consequences, abnormal operation situations, security issues, and even power cuts and blackouts. In order to cope with the ever-increasing operation and control complexity and security in interconnected high-voltage power systems, new architectures, concepts, algorithms, and procedures are essential. This book aims to encourage researchers to address the technical issues and research gaps in high-voltage transmission systems and power substations in modern energy systems

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

Advanced Mathematics and Computational Applications in Control Systems Engineering

Control system engineering is a multidisciplinary discipline that applies automatic control theory to design systems with desired behaviors in control environments. Automatic control theory has played a vital role in the advancement of engineering and science. It has become an essential and integral part of modern industrial and manufacturing processes. Today, the requirements for control precision have increased, and real systems have become more complex. In control engineering and all other engineering disciplines, the impact of advanced mathematical and computational methods is rapidly increasing. Advanced mathematical methods are needed because real-world control systems need to comply with several conditions related to product quality and safety constraints that have to be taken into account in the problem formulation. Conversely, the increment in mathematical complexity has an impact on the computational aspects related to numerical simulation and practical implementation of the algorithms, where a balance must also be maintained between implementation costs and the performance of the control system. This book is a comprehensive set of articles reflecting recent advances in developing and applying advanced mathematics and computational applications in control system engineering

Machine Learning based Early Fault Diagnosis of Induction Motor for Electric Vehicle Application

Electrified vehicular industry is growing at a rapid pace with a global increase in production of electric vehicles (EVs) along with several new automotive cars companies coming to compete with the big car industries. The technology of EV has evolved rapidly in the last decade. But still the looming fear of low driving range, inability to charge rapidly like filling up gasoline for a conventional gas car, and lack of enough EV charging stations are just a few of the concerns. With the onset of self-driving cars, and its popularity in integrating them into electric vehicles leads to increase in safety both for the passengers inside the vehicle as well as the people outside. Since electric vehicles have not been widely used over an extended period of time to evaluate the failure rate of the powertrain of the EV, a general but definite understanding of motor failures can be developed from the usage of motors in industrial application. Since traction motors are more power dense as compared to industrial motors, the possibilities of a small failure aggravating to catastrophic issue is high. Understanding the challenges faced in EV due to stator fault in motor, with major focus on induction motor stator winding fault, this dissertation presents the following: 1. Different Motor Failures, Causes and Diagnostic Methods Used, With More Importance to Artificial Intelligence Based Motor Fault Diagnosis. 2. Understanding of Incipient Stator Winding Fault of IM and Feature Selection for Fault Diagnosis 3. Model Based Temperature Feature Prediction under Incipient Fault Condition 4. Design of Harmonics Analysis Block for Flux Feature Prediction 5. Flux Feature based On-line Harmonic Compensation for Fault-tolerant Control 6. Intelligent Flux Feature Predictive Control for Fault-Tolerant Control 7. Introduction to Machine Learning and its Application for Flux Reference Prediction 8. Dual Memorization and Generalization Machine Learning based Stator Fault Diagnosi

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

Machine Learning in Sensors and Imaging

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens

Acoustic Condition Monitoring & Fault Diagnostics for Industrial Systems

Condition monitoring and fault diagnostics for industrial systems is required for cost reduction, maintenance scheduling, and reducing system failures. Catastrophic failure usually causes significant damage and may cause injury or fatality, making early and accurate fault diagnostics of paramount importance. Existing diagnostics can be improved by augmenting or replacing with acoustic measurements, which have proven advantages over more traditional vibration measurements including, earlier detection of emerging faults, increased diagnostic accuracy, remote sensors and easier setup and operation. However, industry adoption of acoustics remains in relative infancy due to vested confidence and reliance on existing measurement and, perceived difficulties with noise contamination and diagnostic accuracy. Researched acoustic monitoring examples typically employ specialist surface-mount transducers, signal amplification, and complex feature extraction and machine learning algorithms, focusing on noise rejection and fault classification. Usually, techniques are fine-tuned to maximise diagnostic performance for the given problem. The majority investigate mechanical fault modes, particularly Roller Element Bearings (REBs), owing to the mechanical impacts producing detectable acoustic waves. The first contribution of this project is a suitability study into the use of low-cost consumer-grade acoustic sensors for fault diagnostics of six different REB health conditions, comparing against vibration measurements. Experimental results demonstrate superior acoustic performance throughout but particularly at lower rotational speed and axial load. Additionally, inaccuracies caused by dynamic operational parameters (speed in this case), are minimised by novel multi-Support Vector Machine training. The project then expands on existing work to encompass diagnostics for a previously unreported electrical fault mode present on a Brush-Less Direct Current motor drive system. Commonly studied electrical faults, such as a broken rotor bar or squirrel cage, result from mechanical component damage artificially seeded and not spontaneous. Here, electrical fault modes are differentiated as faults caused by issues with the power supply, control system or software (not requiring mechanical damage or triggering intervention). An example studied here is a transient current instability, generated by non-linear interaction of the motor electrical parameters, parasitic components and digital controller realisation. Experimental trials successfully demonstrate real-time feature extraction and further validate consumer-grade sensors for industrial system diagnostics. Moreover, this marks the first known diagnosis of an electrically-seeded fault mode as defined in this work. Finally, approaching an industry-ready diagnostic system, the newly released PYNQ-Z2 Field Programmable Gate Array is used to implement the first known instance of multiple feature extraction algorithms that operate concurrently in continuous real-time. A proposed deep-learning algorithm can analyse the features to determine the optimum feature extraction combination for ongoing continuous monitoring. The proposed black-box, all-in-one solution, is capable of accurate unsupervised diagnostics on almost any application, maintaining excellent diagnostic performance. This marks a major leap forward from fine-tuned feature extraction performed offline for artificially seeded mechanical defects to multiple real-time feature extraction demonstrated on a spontaneous electrical fault mode with a versatile and adaptable system that is low-cost, readily available, with simple setup and operation. The presented concept represents an industry-ready all-in-one acoustic diagnostic solution, that is hoped to increase adoption of acoustic methods, greatly improving diagnostics and minimising catastrophic failures