Rotor-Bar Breakage Mechanism and Prognosis in an Induction Motor

[EN] This paper proposes a condition-based maintenance and prognostics and health management (CBM/ PHM) procedure for a rotor bar in an induction motor. The methodology is based on the results of a fatigue test intended to reproduce in the most natural way a bar breakage in order to carry out a comparison between transient and stationary diagnosis methods for incipient fault detection. Newly developed techniques in stator-current transient analysis have allowed tracking the developing fault during the last part of the test, identifying the failure mechanism, and establishing a physical model of the process. This nonlinear failure model is integrated in a particle filtering algorithm to diagnose the defect at an early stage and predict the remaining useful life of the bar. An initial generalization of the results to conditions differing from the ones under which the fatigue test was developed is studied.Climente Alarcon, V.; Antonino-Daviu, J.; Strangas, EG.; Riera-Guasp, M. (2015). Rotor-Bar Breakage Mechanism and Prognosis in an Induction Motor. IEEE Transactions on Industrial Electronics. 62(3):1814-1825. doi:10.1109/TIE.2014.2336604S1814182562

Rotor-bar breakage mechanism and prognosis in an induction motor

© 1982-2012 IEEE. This paper proposes a condition-based maintenance and prognostics and health management (CBM/PHM) procedure for a rotor bar in an induction motor. The methodology is based on the results of a fatigue test intended to reproduce in the most natural way a bar breakage in order to carry out a comparison between transient and stationary diagnosis methods for incipient fault detection. Newly developed techniques in stator-current transient analysis have allowed tracking the developing fault during the last part of the test, identifying the failure mechanism, and establishing a physical model of the process. This nonlinear failure model is integrated in a particle filtering algorithm to diagnose the defect at an early stage and predict the remaining useful life of the bar. An initial generalization of the results to conditions differing from the ones under which the fatigue test was developed is studied

Study of thermal stresses developed during a fatigue test on an electrical motor rotor cage

© 2018 Structural defects in the rotor cage of large electrical machines significantly impact their expected operational lifetime. This work presents the results of simulating the thermal stresses developed in a rotor cage during a fatigue test in which a bar breakage was achieved. A combined model featuring electrical, thermal and mechanical stages as well as three different meshes reflecting a progressing narrowing of one of the bars in its junction to the end ring are used for this purpose. The experimentally implemented startup and plug stopping transients are reproduced as well as, for comparison, the stall operation. The resulting stress levels are in agreement with the progression of the damage and concur with the stator measurements. Based on the analysis of the simulated rotor magnitudes, a strategy to diminish the thermal stresses in a damaged cage is proposed

Cost-Effective Reduced Envelope of the Stator Current via Synchronous Sampling for the Diagnosis of Rotor Asymmetries in Induction Machines Working at Very Low Slip

[EN] Fault diagnosis of rotor asymmetries of induction machines (IMs) using the stator current relies on the detection of the characteristic signatures of the fault harmonics in the current spectrum. In some scenarios, such as large induction machines running at a very low slip, or unloaded machines tested offline, this technique may fail. In these scenarios, the fault harmonics are very close to the frequency of the fundamental component, and have a low amplitude, so that they may remain undetected, buried under the fundamental's leakage, until the damage is severe. To avoid false positives, a proven approach is to search for the fault harmonics in the current envelope, instead of the current itself, because in this case the spectrum is free from the leakage of the fundamental. Besides, the fault harmonics appear at a very low frequency. Nevertheless, building the current spectrum is costly in terms of computing complexity, as in the case of the Hilbert transform, or hardware resources, as in the need for simultaneously sampling three stator currents in the case of the extended current Park's vector approach (EPVA). In this work, a novel method is proposed to avoid this problem. It is based on sampling a phase current just twice per current cycle, with a fixed delay with respect to its zero crossings. It is shown that the spectrum of this reduced set of current samples contains the same fault harmonics as the spectrum of the full-length current envelope, despite using a minimal amount of computing resources. The proposed approach is cost-effective, because the computational requirements for building the current envelope are reduced to less than 1% of those required by other conventional methods, in terms of storage and computing time. In this way, it can be implemented with low-cost embedded devices for on-line fault diagnosis. The proposed approach is introduced theoretically and validated experimentally, using a commercial induction motor with a broken bar under different load and supply conditions. Besides, the proposed approach has been implemented on a low-cost embedded device, which can be accessed on-line for remote fault diagnosis.This research was funded by the Spanish "Ministerio de Ciencia, Innovacion y Universidades (MCIU)", the "Agencia Estatal de Investigacion (AEI)" and the "Fondo Europeo de Desarrollo Regional (FEDER)" in the framework of the "Proyectos I+D+i - Retos Investigacion 2018", project reference RTI2018-102175-B-I00 (MCIU/AEI/FEDER, UE).Burriel-Valencia, J.; Puche-Panadero, R.; Martinez-Roman, J.; Sapena-Bano, A.; Pineda-Sanchez, M. (2019). Cost-Effective Reduced Envelope of the Stator Current via Synchronous Sampling for the Diagnosis of Rotor Asymmetries in Induction Machines Working at Very Low Slip. Sensors. 19(16)(3471):1-16. https://doi.org/10.3390/s19163471S11619(16)3471Chang, H.-C., Jheng, Y.-M., Kuo, C.-C., & Hsueh, Y.-M. (2019). Induction Motors Condition Monitoring System with Fault Diagnosis Using a Hybrid Approach. Energies, 12(8), 1471. doi:10.3390/en12081471Artigao, E., Koukoura, S., Honrubia-Escribano, A., Carroll, J., McDonald, A., & Gómez-Lázaro, E. (2018). Current Signature and Vibration Analyses to Diagnose an In-Service Wind Turbine Drive Train. Energies, 11(4), 960. doi:10.3390/en11040960Climente-Alarcon, V., Antonino-Daviu, J. A., Strangas, E. G., & Riera-Guasp, M. (2015). Rotor-Bar Breakage Mechanism and Prognosis in an Induction Motor. IEEE Transactions on Industrial Electronics, 62(3), 1814-1825. doi:10.1109/tie.2014.2336604Culbert, I., & Letal, J. (2017). Signature Analysis for Online Motor Diagnostics: Early Detection of Rotating Machine Problems Prior to Failure. IEEE Industry Applications Magazine, 23(4), 76-81. doi:10.1109/mias.2016.2600684Pandarakone, S. E., Mizuno, Y., & Nakamura, H. (2017). Distinct Fault Analysis of Induction Motor Bearing Using Frequency Spectrum Determination and Support Vector Machine. IEEE Transactions on Industry Applications, 53(3), 3049-3056. doi:10.1109/tia.2016.2639453Kang, T.-J., Yang, C., Park, Y., Hyun, D., Lee, S. B., & Teska, M. (2018). Electrical Monitoring of Mechanical Defects in Induction Motor-Driven V-Belt–Pulley Speed Reduction Couplings. IEEE Transactions on Industry Applications, 54(3), 2255-2264. doi:10.1109/tia.2018.2805840Puche-Panadero, R., Pineda-Sanchez, M., Riera-Guasp, M., Roger-Folch, J., Hurtado-Perez, E., & Perez-Cruz, J. (2009). Improved Resolution of the MCSA Method Via Hilbert Transform, Enabling the Diagnosis of Rotor Asymmetries at Very Low Slip. IEEE Transactions on Energy Conversion, 24(1), 52-59. doi:10.1109/tec.2008.2003207Mirzaeva, G., & Saad, K. I. (2018). Advanced Diagnosis of Stator Turn-to-Turn Faults and Static Eccentricity in Induction Motors Based on Internal Flux Measurement. IEEE Transactions on Industry Applications, 54(4), 3961-3970. doi:10.1109/tia.2018.2821098Mirzaeva, G., & Saad, K. I. (2018). Advanced Diagnosis of Rotor Faults and Eccentricity in Induction Motors Based on Internal Flux Measurement. IEEE Transactions on Industry Applications, 54(3), 2981-2991. doi:10.1109/tia.2018.2805730Jian, X., Li, W., Guo, X., & Wang, R. (2019). Fault Diagnosis of Motor Bearings Based on a One-Dimensional Fusion Neural Network. Sensors, 19(1), 122. doi:10.3390/s19010122Yan, X., Sun, Z., Zhao, J., Shi, Z., & Zhang, C.-A. (2019). Fault Diagnosis of Active Magnetic Bearing–Rotor System via Vibration Images. Sensors, 19(2), 244. doi:10.3390/s19020244Martinez, J., Belahcen, A., & Muetze, A. (2017). Analysis of the Vibration Magnitude of an Induction Motor With Different Numbers of Broken Bars. IEEE Transactions on Industry Applications, 53(3), 2711-2720. doi:10.1109/tia.2017.2657478Delgado-Arredondo, P. A., Morinigo-Sotelo, D., Osornio-Rios, R. A., Avina-Cervantes, J. G., Rostro-Gonzalez, H., & Romero-Troncoso, R. de J. (2017). Methodology for fault detection in induction motors via sound and vibration signals. Mechanical Systems and Signal Processing, 83, 568-589. doi:10.1016/j.ymssp.2016.06.032Ghanbari, T. (2016). Autocorrelation function-based technique for stator turn-fault detection of induction motor. IET Science, Measurement & Technology, 10(2), 100-110. doi:10.1049/iet-smt.2015.0118Abd-el -Malek, M., Abdelsalam, A. K., & Hassan, O. E. (2017). Induction motor broken rotor bar fault location detection through envelope analysis of start-up current using Hilbert transform. Mechanical Systems and Signal Processing, 93, 332-350. doi:10.1016/j.ymssp.2017.02.014Leite, V. C. M. N., Borges da Silva, J. G., Veloso, G. F. C., Borges da Silva, L. E., Lambert-Torres, G., Bonaldi, E. L., & de Lacerda de Oliveira, L. E. (2015). Detection of Localized Bearing Faults in Induction Machines by Spectral Kurtosis and Envelope Analysis of Stator Current. IEEE Transactions on Industrial Electronics, 62(3), 1855-1865. doi:10.1109/tie.2014.2345330Aydin, I., Karakose, M., & Akin, E. (2011). A new method for early fault detection and diagnosis of broken rotor bars. Energy Conversion and Management, 52(4), 1790-1799. doi:10.1016/j.enconman.2010.11.018Duan, J., Shi, T., Zhou, H., Xuan, J., & Zhang, Y. (2018). Multiband Envelope Spectra Extraction for Fault Diagnosis of Rolling Element Bearings. Sensors, 18(5), 1466. doi:10.3390/s18051466Wang, J., Liu, S., Gao, R. X., & Yan, R. (2012). Current envelope analysis for defect identification and diagnosis in induction motors. Journal of Manufacturing Systems, 31(4), 380-387. doi:10.1016/j.jmsy.2012.06.005Sapena-Bano, A., Pineda-Sanchez, M., Puche-Panadero, R., Martinez-Roman, J., & Kanovic, Z. (2015). Low-Cost Diagnosis of Rotor Asymmetries in Induction Machines Working at a Very Low Slip Using the Reduced Envelope of the Stator Current. IEEE Transactions on Energy Conversion, 30(4), 1409-1419. doi:10.1109/tec.2015.2445216Wu, T. Y., Lai, C. H., & Liu, D. C. (2016). Defect diagnostics of roller bearing using instantaneous frequency normalization under fluctuant rotating speed. Journal of Mechanical Science and Technology, 30(3), 1037-1048. doi:10.1007/s12206-016-0206-6M. A. Cruz, A. J. Marques Cardoso, S. (2000). Rotor Cage Fault Diagnosis in Three-Phase Induction Motors by Extended Park’s Vector Approach. Electric Machines & Power Systems, 28(4), 289-299. doi:10.1080/073135600268261Henao, H., Capolino, G.-A., Fernandez-Cabanas, M., Filippetti, F., Bruzzese, C., Strangas, E., … Hedayati-Kia, S. (2014). Trends in Fault Diagnosis for Electrical Machines: A Review of Diagnostic Techniques. IEEE Industrial Electronics Magazine, 8(2), 31-42. doi:10.1109/mie.2013.2287651Cruz, S. M. A., & Cardoso, A. J. M. (2001). Stator winding fault diagnosis in three-phase synchronous and asynchronous motors, by the extended Park’s vector approach. IEEE Transactions on Industry Applications, 37(5), 1227-1233. doi:10.1109/28.952496Tsoumas, I. P., Georgoulas, G., Mitronikas, E. D., & Safacas, A. N. (2008). Asynchronous Machine Rotor Fault Diagnosis Technique Using Complex Wavelets. IEEE Transactions on Energy Conversion, 23(2), 444-459. doi:10.1109/tec.2007.895872Corne, B., Vervisch, B., Derammelaere, S., Knockaert, J., & Desmet, J. (2018). The reflection of evolving bearing faults in the stator current’s extended park vector approach for induction machines. Mechanical Systems and Signal Processing, 107, 168-182. doi:10.1016/j.ymssp.2017.12.010Georgakopoulos, I. P., Mitronikas, E. D., & Safacas, A. N. (2011). Detection of Induction Motor Faults in Inverter Drives Using Inverter Input Current Analysis. IEEE Transactions on Industrial Electronics, 58(9), 4365-4373. doi:10.1109/tie.2010.2093476Choi, S., Akin, B., Rahimian, M. M., & Toliyat, H. A. (2011). Implementation of a Fault-Diagnosis Algorithm for Induction Machines Based on Advanced Digital-Signal-Processing Techniques. IEEE Transactions on Industrial Electronics, 58(3), 937-948. doi:10.1109/tie.2010.2048837White, D., William, P., Hoffman, M., & Balkir, S. (2013). Low-Power Analog Processing for Sensing Applications: Low-Frequency Harmonic Signal Classification. Sensors, 13(8), 9604-9623. doi:10.3390/s130809604Wu, F., & Zhao, J. (2016). A Real-Time Multiple Open-Circuit Fault Diagnosis Method in Voltage-Source-Inverter Fed Vector Controlled Drives. IEEE Transactions on Power Electronics, 31(2), 1425-1437. doi:10.1109/tpel.2015.2422131Estima, J. O., & Marques Cardoso, A. J. (2013). A New Algorithm for Real-Time Multiple Open-Circuit Fault Diagnosis in Voltage-Fed PWM Motor Drives by the Reference Current Errors. IEEE Transactions on Industrial Electronics, 60(8), 3496-3505. doi:10.1109/tie.2012.2188877Naha, A., Samanta, A. K., Routray, A., & Deb, A. K. (2017). Low Complexity Motor Current Signature Analysis Using Sub-Nyquist Strategy With Reduced Data Length. IEEE Transactions on Instrumentation and Measurement, 66(12), 3249-3259. doi:10.1109/tim.2017.2737879Moussa, M. A., Boucherma, M., & Khezzar, A. (2017). A Detection Method for Induction Motor Bar Fault Using Sidelobes Leakage Phenomenon of the Sliding Discrete Fourier Transform. IEEE Transactions on Power Electronics, 32(7), 5560-5572. doi:10.1109/tpel.2016.2605821Shahbazi, M., Saadate, S., Poure, P., & Zolghadri, M. (2016). Open-circuit switch fault tolerant wind energy conversion system based on six/five-leg reconfigurable converter. Electric Power Systems Research, 137, 104-112. doi:10.1016/j.epsr.2016.04.004Kamel, T., Biletskiy, Y., & Chang, L. (2015). Fault Diagnoses for Industrial Grid-Connected Converters in the Power Distribution Systems. IEEE Transactions on Industrial Electronics, 62(10), 6496-6507. doi:10.1109/tie.2015.2420627Nguyen, H., Kim, J., & Kim, J.-M. (2018). Optimal Sub-Band Analysis Based on the Envelope Power Spectrum for Effective Fault Detection in Bearing under Variable, Low Speeds. Sensors, 18(5), 1389. doi:10.3390/s1805138

Combined Model for Simulating the Effect of Transients on a Damaged Rotor Cage

© 2017 IEEE. The expansion of the transient operation of electrical machines as, for instance, in vehicle traction applications, demands an accurate computation of the thermal behavior under these conditions in order to enhance the economy of the design and provide a precise estimation of the overload capacity. In addition, heavy transients have been identified as specially damaging for the rotor cage of large induction motors. The aim of this paper is the development of a model able to simulate in detail the thermal and mechanical effects of a heavy transient on an induction's motor rotor featuring a damaged (with a reduced section on one of its ends) rotor bar. Some preliminary results that provide a qualitative understanding of the development of a bar breakage during a fatigue test are presented

Bayesian Filtering Methods For Dynamic System Monitoring and Control

Real-time system monitoring and control represent two of the most important issues that characterize modern industries in critical areas of civilian and military interest, including the power grid, energy, healthcare, aerospace, and infrastructure. During the past decade, there has been a rapid development of robust dynamic system monitoring and control methods for fault diagnosis and failure prognosis. Among various monitoring and control policies, condition-based maintenance (CBM) has been studied by many researchers due to its ability to enable a large amount of monitoring data for real-time diagnostics and prognostics. A considerable amount of literature has been published on the subject, providing a large volume of dynamic system control methods. Previously published studies are limited by assumptions that can generally be distinguished into three main categories: i) predefined system failure thresholds, ii) simplified latent dynamics, and iii) unrealistic parametric forms that describe the evolution of system dynamics through time. This thesis provides an array of solution approaches that overcome the aforementioned assumptions in a smart and effective way by introducing novel quantitative frameworks for real-time monitoring, control, and decision-making for dynamic systems. The proposed frameworks are categorized into two main phases of a comprehensive framework. The first phase contains two original Bayesian filtering methods for condition monitoring and control of systems with either linear or non-linear degradation dynamics. The former is designed only for systems with linear latent and observable dynamics and utilizes Kalman filtering for state-parameter inference. It considers a failure process that is purely stochastic and is based on logistic regression. This process is directly affected by the latent system dynamics, therefore avoiding the need for a priori failure thresholds. The latter takes into consideration multiple levels of system dynamics that evolve either linearly or non-linearly. A hybrid particle filter is developed for state-parameter inference, while an Extreme Learning Machine artificial neural network is utilized to relate sensor observations to latent system dynamics. Both frameworks are tested and validated on synthetic and real-world time-series datasets. The second phase of this thesis introduces an original method for optimal control and decision-making that employs Bayesian filtering-based deep reinforcement learning with fully stochastic environments. Sets of deep reinforcement learning agents were trained to develop control policies. Bayesian filtering methods from the first phase were utilized to provide environment states that use the estimates from latent system dynamics. This method is used in two different applications for maintenance cost minimization and estimating the remaining useful life of a system under condition monitoring. Results obtained from applying the framework on simulated and real-world time-series data suggest that the proposed Bayesian filtering-based deep reinforcement learning algorithm can be trained even with limited data, which can be useful for real-time control and decision making for many dynamic systems

복잡한 공학 시스템에 대한 오경보를 고려한 리질리언스 해석 및 설계 방법론 연구

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 기계항공공학부, 2018. 2. 윤병동.it estimates a healthy engineered to be faulty, resulting unnecessary system shutdown, inspection, and – in the case of incorrect inspection – unnecessary system repair or replacement. Although false alarms make a system unavailable with capital loss, it has not been considered in resilience engineering. To cope with false alarm problems, this research is elaborated to advance the resilience engineering considering false alarms. Specifically, this consists of three research thrusts: 1) resilience analysis considering false alarms, 2) resilience-driven system design considering false alarms (RDSD-FA), and 3) resilience-driven system design considering time-dependent false alarms (RDSD-TFA). In the first research thrust, a resilience measure is newly formulated considering false alarms. This enables the evaluation of resilience decrease due to false alarms, resulting in accurate analysis of system resilience. Based upon the new resilience measure, RDSD-FA is proposed in the second research thrust. This aims at designing a resilient system to satisfy a target resilience level while minimizing life-cycle cost. This is composed of three hierarchical tasks: resilience allocation problem, reliability-based design optimization (RBDO), and PHM design. The third research thrust presents RDSD-TFA that considers time-dependent variability of an engineered system. This makes one to estimate life-cycle cost in an accurate and rigorous manner, and to design an engineered system more precisely while minimizing its life-cycle cost. The framework of RDSD-TFA consists of four tasks: system analysis, PHM analysis, life-cycle simulation, and design optimization. Through theoretical analysis and case studies, the significance of false alarms in engineering resilience and the effectiveness of the proposed ideas are demonstrated.공학 시스템은 생애주기에 걸쳐 다양한 불확실성에 노출되며, 이로 인해 목표 성능을 충족시키지 못할 경우 사회적, 경계적, 인적 소실을 야기하게 된다. 이에 대한 해결 방안 중 하나로 리질리언스 주도 설계 기술 (resilience-driven system design이하 RDSD)이 개발되었다. RDSD는 건전성 예측 및 관리 기술 (prognostics & health management이하 PHM)을 설계에 도입함으로써 비용 효율적인 고장 예방을 가능케 하였다. 하지만, RDSD는 PHM의 고장 오경보 현상을 고려하지 않는 한계점을 갖는다. 고장 오경보는 건전한 시스템을 고장이라 추정하는 현상으로, 불필요한 시스템 정지 및 검사 비용을 야기하여, PHM과 RDSD의 기술적 효용성을 떨어트리게 된다. 따라서, RDSD의 기술적 약진과 실적용을 도모하기 위해서는 고장 오경보 현상을 해결해야 한다. 본 논문에서는 고장 오경보의 고려를 통해 리질리언스 해석 및 설계 방법론을 개선하고자 하며, 이를 위해 세 가지 연구 주제를 제안한다. 첫 번째 주제는 오경보를 고려한 리질리언스 분석으로, 공학 시스템의 리질리언스 시나리오 분석에 기반해 리질리언스 지수를 새롭게 정식화 한다. 이 지수는 고장 오경보로 인한 리질리언스의 저하를 분석함으로써, 정확한 리질리언스 추정을 가능케 한다. 두 번째 주제는 고장 오경보를 고려한 리질리언스 주도 설계 방법론이다. 이는 3단계의 계층적 요소로 구성된다. 먼저 목표 리질리언스 지수를 만족하면서 생애주기비용을 최소화하기 위해, 목표 신뢰도와 목표 오경보 및 유실경보율을 최적화한다. 이후 신뢰성 기반 최적 설계 (reliability-based design optimization)를 통해 목표 신뢰도를 확보하고, PHM 설계를 통해 할당된 목표 오경보 및 유실경보율을 충족시킨다. 세 번째 주제는 시변(時變) 오경보를 고려한 리질리언스 주도 설계 방법론이다. 기존의 설계 방법론들은 시스템의 건전성 상태를 시불변(時不變)하다 간주하였으나, 실제 시스템은 운행에 따라 점진적으로 건전성이 저하된다. 본 연구에서는 시변성을 분석하기 위해 시변 오경보율 및 유실경보율에 대한 개념을 새롭게 제안하였으며, 생애주기 시뮬레이션을 통한 총 유지보수 비용 분석 방법론을 개발하였다. 이를 통해 생애주기비용을 보다 엄밀하고 정확하게 추정할 수 있게 되었으며, 이를 최소화하는 방향으로 시스템과 PHM의 설계를 최적화였다. 본 연구에서 제안한 방법론들은 이론적 분석과 사례 연구를 통해 그 효용성을 입증하였다.Most engineered systems are designed with a passive and fixed design capacity and, therefore, may become unreliable in the presence of adverse events. In order to handle this issue, the resilience-driven system design (RDSD) has been proposed to make engineered systems adaptively reliable by incorporating the prognostics and health management (PHM) method. PHM tracks the health degradation of an engineered system, and provides health state information supporting decisions on condition-based maintenance. Meanwhile, one of the issues awaiting solution in the field of PHM, as well as in RDSD, is to address false alarms. A false alarm is an erroneous report on the health state of an engineered systemChapter 1. Introduction 1 1.1 Motivation 1 1.2 Research Scope and Overview 3 1.3 Dissertation Layout 6 Chapter 2. Literature Review 7 2.1 Resilience Engineering (Analysis and Design) 7 2.1.1 Resilience Analysis for Mechanical Systems 8 2.1.2 Resilience-Driven System Design (RDSD) for Mechanical Systems 15 2.2 False and Missed Alarms in Prognostics and Health Management 27 2.2.1 Definition of False and Missed Alarms 27 2.2.2 Quantification of False and Missed Alarms 32 2.3 Summary and Discussion 35 Chapter 3. Resilience Analysis Considering False Alarms 37 3.1 Resilience Measure Considering False Alarms 37 3.2 Case Studies 42 3.2.1 Numerical ample 42 3.2.2 Electro-Hydrtatic Actuator (EHA) 44 3.3 Summary and Discussion 53 Chapter 4. Resilience-Driven System Design Considering False Alarms (RDSD-FA) 55 4.1 Overview of RDSD-FA Framework 55 4.2 Resilience Allocation Problem Considering False Alarms 56 4.3 Prognostics and Health Management (PHM) Design Considering False Alarms 60 4.4 Case study: Electro-Hydrostatic Actuator (EHA) 61 4.4.1 Step 1: Resilience Allocation Considering False Alarms 61 4.4.2 Step 2: Reliability-Based Design Optimization 64 4.4.3 Step 3: PHM Design Considering False Alarms 68 4.4.4 Comparison of Design Results from RDSD and RDSD-FA 73 4.5 Summary and Discussion 75 Chapter 5. Resilience-Driven System Design Considering Time-Dependent False Alarms (RDSD-TFA) 77 5.1 Time-Dependent False and Missed Alarms in PHM 79 5.2 Resilience-Driven System Design Considering Time-Dependent False Alarms (RDSD-TFA) 83 5.2.1 Overview of RDSD-TFA Framework 83 5.2.2 Task 1: System Analysis 86 5.2.3 Task 2: PHM Analysis 89 5.2.4 Task 3: Life-Cycle Simulation 91 5.2.5 Task 4: Design Optimization 97 5.3 Case studies 98 5.3.1 Numerical Example of Life-Cycle Simulation 98 5.3.2 Electro-Hydrostatic Actuator (EHA) 107 5.4 Summary and Discussion 123 Chapter 6. Conclusions 126 6.1 Summary and Contributions 126 6.2 Suggestions for Future Research 129 References 132 Appendix 154 Abstract(Korean) 157Docto

Metodología de diagnóstico de motores de inducción alimentados por convertidor para la detección de fallos incipientes: basada en conjuntos pequeños de datos y clases desequilibradas

Los fallos mecánicos pueden desarrollarse a lo largo de la vida últil de un motor de inducción provocando elevados costes. Por lo tanto, es necesario desarrollar técnicas de diagnóstico para llevar a cabo una detección incipiente. La mayor problemática para desarrollar estos sistemas es que muchos motores están alimentados por inversor y disponen de una cantidad limitada y desequilibrada de datos para constituir una herramienta de diagnóstico. Esta tesis desarrolla varias metodologías de diagnóstico. La primera de ellas orientada a reducir la incertidumbre en la localización de las frecuencias de fallo de barra a través de la aplicación combinada de la transformada Múltiple Signal Classification (MUSIC) y la Fast Fourier Transform (FFT). Posteriormente, se propone una metodología de diagnóstico que incluye las siguientes etapas: (i) En primer lugar se desarrolla una etapa de selección de características con el algortimo de Random Forest que permite determinar para qué conjunto y número de variables se consiguen obtener las mejores prestaciones. Esta técnica es comparada con otras como el criterio de Ganancia de Información, análisis de varianza (ANOVA) y el algoritmo Relief-F (ii) Con el objetivo de reducir el sesgo del clasificador en su etapa de entrenamiento, se equilibra el conjunto de datos con una técnica denominada Synthetic Minority Oversam-pling TEchnique (SMOTE) la cual genera observaciones sintéticas adicionales que permiten equilibrar las clases del conjunto de entrenamiento. (iii) Finalmente, se propone un novedoso algoritmo, AdaBoost, cuya principal ventaja es que puede ser usado en conjuntos de datos de diferentes casos sin modificar susDepartamento de Ingeniería EléctricaDoctorado en Ingeniería Industria

Rotor bar breakage data obtained from fatigue test

The data hereby provided was acquired during a fatigue test developed at the Department of Electrical Engineering, Universitat Politècnica de València (Universidad Politécnica de Valencia, Spain) in 2011 by PhD student Vicente Climente-Alarcon under supervision of Prof. Martin Riera-Guasp. The fatigue test involved subjecting a 1.5 kW, 1 pole pair induction motor to severe cycling until a bar breakage naturally developed. The cycling consisted of a Direct-on-line (DOL) startup followed by a stationary operation period of at least 10 seconds. A plug stopping was added at the later stages of the test. To maximize the possible damage to the rotor cage, a high load inertia caused heavy (long) startup and stopping transients. ******* Epochs ******* Since the bar breakage did not develop naturally, the rotor cage was successively weakened, defining three stages in the fatigue test: original rotor, lathed rotor and weakened bar. The data shared here corresponds only to roughly the last one, beginning at cycle 79008, in which the end of one bar was narrowed by boring holes in its connection to the end ring. Some previous cycles (79000 to 79007) are also shared. All the details about the fatigue test can be found (in Spanish) in: Climente Alarcón, V. (2012). Aportación al mantenimiento predictivo de motores de inducción mediante modernas técnicas de análisis de la señal. Universitat Politècnica de València. doi:10.4995/Thesis/10251/15915. The state of the rotor during the provided cycles is as follows: Cycles State 79000-79007 Lathed 79008-79112 Lathed and one 3 mm diameter hole 79113-80187 Lathed and two 3 mm diameter holes 80188-80404 Lathed and two 3 mm diameter holes (current sensors changed) 80405-80857 Lathed and two 4 mm diameter holes 80858-81215 Lathed, one 4 mm and one 4.5 mm diameter holes. Bar completely broken @ 81071 81216-81653 Bar broken 81654-81882 Broken bar rests removed 81883-82265 Bar cut also at the other end ******* Data ******* The data provided here only contains the current waveform in phase C excluding the plug stopping, ambient temperature at the beginning of the cycle, motor temperature at the beginning of the cycle and maximum motor temperature during the provided data cycle (which coincides with the motor temperature at the end of the stationary period). Stationary rotational speed, as captured by an encoder, is also included. For an easier handling, all these magnitudes are stored in a single column in each file, for instance: Contents of file 81571.txt 81571 -> Cycle number, must coincide with the name of the file 26.6 -> Ambient temperature at the beginning of the cycle in ºC 78.2 -> Motor temperature at the beginning of the cycle in ºC 85.3 -> Motor temperature at the end of the recorded period in ºC 48.0 -> Rotational speed during the stationary period in Hz 0.000000 -> Beginning of the current waveform in phase C in A 0.002083 0.000000 -0.002083 0.004167 0.004167 0.002083 · · The current waveform is sampled at 5 kSamples/second and it spans around 20 seconds, of which at least 10 correspond to stationary operation. Different sensors were used during the fatigue test, and hence the precision of the current measurements may vary in the provided data. However, the cycles 80188-82265, including the bar breakage itself, were captured using the same sensors and configuration. The motor temperature corresponds to the value measured inside the connection box on the stator yoke. ******* Publications ******* Further details can be found in the publications where data from the fatigue test have been analyzed: V. Climente-Alarcon, J. A. Antonino-Daviu, E. Strangas, M. Riera-Guasp, “Bar breakage mechanism and prognosis in an induction motor,” in Proc. SDEMPED, Valencia, Spain, 2013, pp. 538–545, doi: 10.1109/DEMPED.2013.6645775 V. Climente-Alarcon, J. A. Antonino-Daviu, A. Haavisto, A. Arkkio, “Evolution of high order fault harmonics during a bar breakage with compensation,” presented at the International Conf. Electrical Machines ICEM, Berlin, Germany, Sep. 2–5, 2014, doi: 10.1109/ICELMACH.2014.6960441 V. Climente-Alarcon, J. A. Antonino-Daviu, E.G. Strangas, M. Riera-Guasp, “Rotor-bar breakage mechanism and prognosis in an induction motor,” IEEE Trans. Ind. Electron., vol. 62, no. 3, Mar. 2015, pp. 1814-1825, doi: 10.1109/TIE.2014.2336604 V. Climente-Alarcon, D. Nair, R. Sundaria, J. A. Antonino-Daviu, Antero Arkkio, “Combined Model for Simulating the Effect of Transients on a Damaged Rotor Cage,” IEEE Trans. Ind. Appl., vol. 53, no. 4, Jul.-Aug. 2017, pp. 3528-3537, doi: 10.1109/TIA.2017.2691001 V. Climente-Alarcon, A. Arkkio, J. Antonino-Daviu, “Study of thermal stresses developed during a fatigue test on an electrical motor rotor cage,” International Journal of Fatigue, vol. 120, Mar. 2019, pp. 56-64, doi: 10.1016/j.ijfatigue.2018.11.00