Vibration Monitoring: Gearbox identification and faults detection

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Efficient and Robust Signal Detection Algorithms for the Communication Applications

Signal detection and estimation has been prevalent in signal processing and communications for many years. The relevant studies deal with the processing of information-bearing signals for the purpose of information extraction. Nevertheless, new robust and efficient signal detection and estimation techniques are still in demand since there emerge more and more practical applications which rely on them. In this dissertation work, we proposed several novel signal detection schemes for wireless communications applications, such as source localization algorithm, spectrum sensing method, and normality test. The associated theories and practice in robustness, computational complexity, and overall system performance evaluation are also provided

Nonlinear models and algorithms for RF systems digital calibration

Focusing on the receiving side of a communication system, the current trend in pushing the digital domain ever more closer to the antenna sets heavy constraints on the accuracy and linearity of the analog front-end and the conversion devices. Moreover, mixed-signal implementations of Systems-on-Chip using nanoscale CMOS processes result in an overall poorer analog performance and a reduced yield. To cope with the impairments of the low performance analog section in this "dirty RF" scenario, two solutions exist: designing more complex analog processing architectures or to identify the errors and correct them in the digital domain using DSP algorithms. In the latter, constraints in the analog circuits' precision can be offloaded to a digital signal processor. This thesis aims at the development of a methodology for the analysis, the modeling and the compensation of the analog impairments arising in different stages of a receiving chain using digital calibration techniques. Both single and multiple channel architectures are addressed exploiting the capability of the calibration algorithm to homogenize all the channels' responses of a multi-channel system in addition to the compensation of nonlinearities in each response. The systems targeted for the application of digital post compensation are a pipeline ADC, a digital-IF sub-sampling receiver and a 4-channel TI-ADC. The research focuses on post distortion methods using nonlinear dynamic models to approximate the post-inverse of the nonlinear system and to correct the distortions arising from static and dynamic errors. Volterra model is used due to its general approximation capabilities for the compensation of nonlinear systems with memory. Digital calibration is applied to a Sample and Hold and to a pipeline ADC simulated in the 45nm process, demonstrating high linearity improvement even with incomplete settling errors enabling the use of faster clock speeds. An extended model based on the baseband Volterra series is proposed and applied to the compensation of a digital-IF sub-sampling receiver. This architecture envisages frequency selectivity carried out at IF by an active band-pass CMOS filter causing in-band and out-of-band nonlinear distortions. The improved performance of the proposed model is demonstrated with circuital simulations of a 10th-order band pass filter, realized using a five-stage Gm-C Biquad cascade, and validated using out-of-sample sinusoidal and QAM signals. The same technique is extended to an array receiver with mismatched channels' responses showing that digital calibration can compensate the loss of directivity and enhance the overall system SFDR. An iterative backward pruning is applied to the Volterra models showing that complexity can be reduced without impacting linearity, obtaining state-of-the-art accuracy/complexity performance. Calibration of Time-Interleaved ADCs, widely used in RF-to-digital wideband receivers, is carried out developing ad hoc models because the steep discontinuities generated by the imperfect canceling of aliasing would require a huge number of terms in a polynomial approximation. A closed-form solution is derived for a 4-channel TI-ADC affected by gain errors and timing skews solving the perfect reconstruction equations. A background calibration technique is presented based on cyclo-stationary filter banks architecture. Convergence speed and accuracy of the recursive algorithm are discussed and complexity reduction techniques are applied

Spectrum Sensing in Cognitive Radio: Bootstrap and Sequential Detection Approaches

In this thesis, advanced techniques for spectrum sensing in cognitive radio are addressed. The problem of small sample size in spectrum sensing is considered, and resampling-based methods are developed for local and collaborative spectrum sensing. A method to deal with unknown parameters in sequential testing for spectrum sensing is proposed. Moreover, techniques are developed for multiband sensing, spectrum sensing in low signal to noise ratio, and two-bits hard decision combining for collaborative spectrum sensing. The assumption of using large sample size in spectrum sensing often raises a problem when the devised test statistic is implemented with a small sample size. This is because, for small sample sizes, the asymptotic approximation for the distribution of the test statistic under the null hypothesis fails to model the true distribution. Therefore, the probability of false alarm or miss detection of the test statistic is poor. In this respect, we propose to use bootstrap methods, where the distribution of the test statistic is estimated by resampling the observed data. For local spectrum sensing, we propose the null-resampling bootstrap test which exhibits better performances than the pivot bootstrap test and the asymptotic test, as common approaches. For collaborative spectrum sensing, a resampling-based Chair-Varshney fusion rule is developed. At the cognitive radio user, a combination of independent resampling and moving-block resampling is proposed to estimate the local probability of detection. At the fusion center, the parametric bootstrap is applied when the number of cognitive radio users is large. The sequential probability ratio test (SPRT) is designed to test a simple hypothesis against a simple alternative hypothesis. However, the more realistic scenario in spectrum sensing is to deal with composite hypotheses, where the parameters are not uniquely defined. In this thesis, we generalize the sequential probability ratio test to cope with composite hypotheses, wherein the thresholds are updated in an adaptive manner, using the parametric bootstrap. The resulting test avoids the asymptotic assumption made in earlier works. The proposed bootstrap based sequential probability ratio test minimizes decision errors due to errors induced by employing maximum likelihood estimators in the generalized sequential probability ratio test. Hence, the proposed method achieves the sensing objective. The average sample number (ASN) of the proposed method is better than that of the conventional method which uses the asymptotic assumption. Furthermore, we propose a mechanism to reduce the computational cost incurred by the bootstrap, using a convex combination of the latest K bootstrap distributions. The reduction in the computational cost does not impose a significant increase on the ASN, while the protection against decision errors is even better. This work is motivated by the fact that the sequential probability ratio test produces a smaller sensing time than its counterpart of fixed sample size test. A smaller sensing time is preferable to improve the throughput of the cognitive radio network. Moreover, multiband spectrum sensing is addressed, more precisely by using multiple testing procedures. In a context of a fixed sample size, an adaptive Benjamini-Hochberg procedure is suggested to be used, since it produces a better balance between the familywise error rate and the familywise miss detection, than the conventional Benjamini-Hochberg. For the sequential probability ratio test, we devise a method based on ordered stopping times. The results show that our method has smaller ASNs than the Bonferroni procedure. Another issue in spectrum sensing is to detect a signal when the signal to noise ratio is very low. In this case, we derive a locally optimum detector that is based on the assumption that the underlying noise is Student's t-distributed. The resulting scheme outperforms the energy detector in all scenarios. Last but not least, we extend the hard decision combining in collaborative spectrum sensing to include a quality information bit. In this case, the multiple thresholds are determined by a distance measure criterion. The hard decision combining with quality information performs better than the conventional hard decision combining

Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications

Моделювання системи предиктивного обслуговування турбіни

Магістерська дисертація складається зі вступу, п’яти розділів, висновку, списку використаних джерел (29 найменувань). Робота містить 32 таблиці, 77 рисунків та формули. Повний обсяг магістерської дисертації складає 109 сторінок. Об’єкт дослідження – вібродіагностика та предиктивний аналіз несправностей турбоагрегату. Предмет дослідження – система вібродіагностики та предиктивного обслуговування парової турбіни Т-100/120-130. Мета дослідження – розробка системи предиктивного обслуговування турбоагрегату. Основні задачі: Для досягнення поставленої мети необхідно вирішити наступні задачі: - розробка системи контролю та моніторингу вібрації; - розробка моделі вібродіагностики; - розробка моделі прогнозування стану обладнання з використанням методів машинного навчання. Актуальність На сьогоднішній день, затрати на обслуговування обладнання, а також затрати підприємства в зв’язку з простоєм обладнання обчислюються неймовірними сумами. Впровадження систем предиктивного обслуговування дозволяє мінімізувати час простою обладнання шляхом прогнозування виникнення дефектів. За рахунок цього можна заздалегідь планувати ремонтні та профілактичні роботи, значно скоротити час їх проведення, а також оптимізувати процес закупівлі, транспортування та зберігання запасних комплектуючих. Методи дослідження: методи математичного і комп’ютерного моделювання динамічних процесів, методи вібродіагностики, методи предиктивного обслуговування, методи машинного навчання. Наукова новизна отриманих результатів полягає у виборі ефективних засобів обрахунку вібропараметрів та використання методів машинного навчання для прогнозування стану обладнання. Практичне значення отриманих результатів 1. Розроблено та реалізовано програмний продукт системи вібродіагностики. 2. Розроблено та реалізовано програмний продукт системи предиктивного обслуговування турбоагрегату. Апробації результатів дослідження Результат дослідження впроваджується на контролерах Siemens Simatic S7-400 при автоматизації системи вібродіагностики та предиктивного обслуговування парової турбіни Т-100/120-130, підприємством ТОВ «ENERTEX». Додається відповідний акт про впровадження. Публікації 1. Темчур В.С., Поліщук І.А. «Вимоги до обладнання автоматизації, яке працює у вибухонебезпечних умовах»// Матеріали ХVІІІ Міжнародної науково-практичної конференції молодих вчених і студентів 2020 року. У 2 т. – К. : 7 КПІ ім. Ігоря Сікорського, 2020. – Т. 2. – С. 59. 2. Темчур В.С., Поліщук І.А. «Оптимізація економічності згоряння палива парового котла за допомогою машинного навчання»// Матеріали ХІХ Міжнар. наук.-практ. конф. молод. вчених і студ., м. Київ, 20–23 квіт. 2021 р. – Київ : КПІ ім. Ігоря Сікорського, Вид-во «Політехніка», 2021. – Т. 2. – С. 40-41. 3. Темчур В.С., Поліщук І.А. «Предиктивне обслуговування обладнання на основі аналізу вібрації» // Матеріали ХХ Міжнар. наук.-практ. конф. молод. вчених і студ., м. Київ, 26–29 квіт. 2022 р. – Київ : КПІ ім. Ігоря Сікорського, Вид-во «Політехніка», 2022. – Т. 2. 4. Темчур В.С., Поліщук І.А. «Застосування методів машинного навчання для предиктивного обслуговування турбоагрегату» // Вчені записки Таврійського національного університету ім. В.І. Вернадського. Серія: Технічні науки. Том 33 (72), №4, 2022.The master's dissertation consists of an introduction, five chapters, a conclusion, a list of sources used (29 titles). The work contains 32 tables, 77 figures and formulas. The full volume of the master's dissertation is 109 pages. The object of research is vibration diagnostics and predictive analysis of turbine unit malfunctions. The subject of research is the system of vibration diagnostics and predictive maintenance of the steam turbine T-100 / 120-130. The purpose of research is to develop a system of predictive maintenance of the turbine unit. Main tasks: To achieve this goal it is necessary to solve the following tasks: - development of vibration control and monitoring system; - development of vibration diagnostics model; - development of a model for forecasting the state of equipment using machine learning methods. Actuality To date, the cost of maintaining the equipment, as well as the cost of the enterprise due to equipment downtime is calculated in incredible amounts. The introduction of predictive service systems can minimize equipment downtime by predicting the occurrence of defects. Due to this, it is possible to plan repair and maintenance work in advance, significantly reduce the time of their implementation, as well as optimize the process of purchasing, transporting and storing spare parts. Research methods: methods of mathematical and computer modeling of dynamic processes, methods of vibrodiagnostics, methods of predictive maintenance, methods of machine learning. The scientific novelty of the obtained results is the choice of effective means of calculating vibration parameters and the use of machine learning methods to predict the state of equipment. The practical significance of the results obtained 1. Developed and implemented a software product for vibration diagnostics. 2. Developed and implemented a software product for predictive maintenance of the turbine unit. Approbation of research results The result of the study is implemented on the Siemens Simatic S7-400 controllers in the automation of the vibration diagnostics system and predictive maintenance of the steam turbine T-100 / 120-130, at the enterprise "ENERTEX". The corresponding act of implementation is attached. Publications 1. Temchur, Polishchuk "Requirements for automation equipment operating in explosive atmospheres" // Proceedings of the XVIII International Scientific and Practical Conference of Young Scientists and Students 2020. In 2 volumes - K .: 7 KPI. Igor Sikorsky, 2020. - Vol. 2. - P. 59. 2. Temchur VS, Polishchuk IA "Optimization of fuel economy of steam boiler with the help of machine learning" // Materials of the XIX International. scientific-practical conf. young. Scientists and Students, Kyiv, April 20-23. 2021 - Kyiv: KPI named after Igor Sikorsky, Polytechnic Publishing House, 2021. - Vol. 2. - P. 40-41. 3. Temchur VS, Polishchuk IA "Predictive maintenance of equipment based on vibration analysis" // Materials of the XX International. scientific-practical conf. young. Scientists and Students, Kyiv, April 26-29. 2022 - Kyiv: KPI named after Igor Sikorsky, Polytechnic Publishing House, 2022. - Vol. 2. 4. Temchur VS, Polishchuk IA "Application of machine learning methods for predictive maintenance of the turbine unit" // Scientific notes of Tavriya National University. VI Vernadsky. Series: Technical Sciences. Volume 33 (72), №4, 2022

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Deep Space Telecommunications Systems Engineering

Descriptive and analytical information useful for the optimal design, specification, and performance evaluation of deep space telecommunications systems is presented. Telemetry, tracking, and command systems, receiver design, spacecraft antennas, frequency selection, interference, and modulation techniques are addressed