370 research outputs found
Fuzzy current analysis-based fault diagnostic of induction motor using hardware co-simulation with field programmable gate array
Introduction. Presently, signal analysis of stator current of induction motor has become a popular technique to assess the health state of asynchronous motor in order to avoid failures. The classical implementations of failure detection algorithms for rotating machines, based on microprogrammed sequential systems such as microprocessors and digital signal processing have shown their limitations in terms of speed and real time constraints, which requires the use of new technologies providing more efficient diagnostics such as application specific integrated circuit or field programmable gate array (FPGA). The purpose of this work is to study the contribution of the implementation of fuzzy logic on FPGA programmable logic circuits in the diagnosis of asynchronous machine failures for a phase unbalance and a missing phase faults cases. Methodology. In this work, we propose hardware architecture on FPGA of a failure detection algorithm for asynchronous machine based on fuzzy logic and motor current signal analysis by taking the RMS signal of stator current as a fault indicator signal. Results. The validation of the proposed architecture was carried out by a co-simulation hardware process between the ML402 boards equipped with a Virtex-4 FPGA circuit of the Xilinx type and Xilinx system generator under MATLAB/Simulink. Originality. The present work combined the performance of fuzzy logic techniques, the simplicity of stator current signal analysis algorithms and the execution power of ML402 FPGA board, for the fault diagnosis of induction machine achieving the best ratios speed/performance and simplicity/performance. Practical value. The emergence of this method has improved the performance of fault detection for asynchronous machine, especially in terms of hardware resource consumption, real-time online detection and speed of detection.Вступ. В даний час аналіз сигналу струму статора асинхронного двигуна став популярним методом оцінки стану працездатності асинхронного двигуна, щоб уникнути відмов. Класичні реалізації алгоритмів виявлення несправностей машин, що обертаються, засновані на мікропрограмних послідовних системах, таких як мікропроцесори і цифрова обробка сигналів, показали свої обмеження з точки зору швидкості та обмежень у реальному часі, що вимагає використання нових технологій, що забезпечують більш ефективну діагностику. наприклад, інтегральна схема для конкретної програми або програмована вентильна матриця (FPGA). Метою даної є дослідження внеску реалізації нечіткої логіки на програмованих логічних схемах FPGA в діагностику відмов асинхронних машин при несиметрії фаз і обривах фази. Методологія. У цій роботі ми пропонуємо апаратну архітектуру на FPGA алгоритму виявлення відмов асинхронної машини на основі нечіткої логіки та аналізу сигналів струму двигуна, приймаючи середньоквадратичний сигнал статора струму як сигнал індикатора несправності. Результати. Валідація запропонованої архітектури проводилася шляхом апаратного моделювання між платами ML402, оснащеними схемою Virtex-4 FPGA типу Xilinx та генератором системи Xilinx під керуванням MATLAB/Simulink. Оригінальність. Дана робота поєднала в собі ефективність методів нечіткої логіки, простоту алгоритмів аналізу сигналів струму статора та виконавчу потужність плати ML402 FPGA для діагностики несправностей асинхронних машин, досягаючи найкращих співвідношень швидкість/продуктивність та простота/продуктивність. Практична цінність. Поява цього методу покращила продуктивність виявлення несправностей асинхронної машини, особливо з точки зору споживання апаратних ресурсів, онлайн-виявлення в реальному часі та швидкості виявлення
Fault Detection of Inter-Turn Short-Circuited Stator Windings in Permanent Magnet Synchronous Machines
Vannkraftverk leverer grønn og pålitelig energi til befolkningen i Norge, og bidrar med rundt 88 % av landets årlige strømbehov. Uventede avbrudd og stans for kraftverkene vil resultere i store økonomiske tap, samt at kraftverkene ikke får levert nødvendig kraft til nettet. Med fremveksten av Industri 4.0 benytter industriene nyskapende teknologier som skytjenester, Kunstig Intelligens (KI) og tingenes internett for å forbedre de ulike operasjonene i selskapet. Innen vannkraft-industrien vil KI-baserte systemer bli brukt som grunnlag for prediktive vedlikehold. I dag utføres det meste av vedlikeholdsarbeid i henhold til en planlagt tidsplan, og industrien ser derfor på bruk av maskinlærings-metoder for tidlig feilgjenkjenning i vannkraftverkene. Denne masteroppgaven ser på anvendelsen av maskinlærings-algoritmer for å tidlig forutsi kortslutninger i aramturviklingene i en Permanent Magnet Synkronmaskin (PMSM), ved bruk av trefaset strøm-data. Data A ble samlet inn i et internt laboratorium med en Permanent Magnet Synkrongenerator (PMSG) som hadde en implementert 4.8 % kortslutning i aramturviklingen. Dataen bestod av sunne og defekte datasett med RMSverdier for den trefasede strømmen. Data B ble hentet fra et tidligere arbeid av den samme typen PMSM med en 6.0 % kortslutning i aramturviklingen. Data B bestod av signal-verdier for den trefasede strømmen.
Ved bruk av Python ble de to datasettene visuelt inspisert og forbehandlet ved hjelp av ‘Z-score’-metoden for å fjerne avvikende verdier. Denne prosessen hadde imidlertid ingen merkbar effekt på nøyaktigheten til maskinlærings-modellene. Enkel signalbehandling i tidsplanet ble anvendt på strømdataene, men klarte ikke å oppdage kortslutningsfeilen implementert på den andre faseviklingen. Statistiske parameter som gjennomsnitt, standard avvik, skjevhet, kurtose, toppverdifaktor, peak-to-peak, RMS, klaringsfaktor, formfaktor og impulsfaktor ble beregnet for alle tre fasene. En Principal Component Analysis (PCA)- algoritme ble anvendt på datasettene med de statistiske parameterne og reduserte Data A fra 18 parameter til tre Principal Components. Data B ble redusert fra 33 parametere til fire Principal Components. Før dataen kjøres i maskinlørings-modellene, ble feilindikatorer som flagger verdier utenfor den 95. persentilen av gjennomsnittsverdiene til parameterne lagt til i datasettet . Fire overvåkede maskinlærings-modeller – ‘Random Forest’, ‘Decision Trees’, ‘k-NN’ og ‘Naive Bayes’ – ble kjørt for datasettene. Random Forest- og Decision Tree-modellene hadde en tendens til å overtilpasse maskinlærings-prediksjonene på datasettene som inneholdt de statistisk parameterne. Datasettet med PCA-komponentene reduserte overtilpasningen av disse modellene og forbedret nøyaktigheten til Naive Bayes-modellen. Ettersom Naive Bayes-modellen ga varierende resultater og ble ansett som inkonsekvent, samt overtilpasnings-tendensene til Random Forest og Decision Tree, ble k-NN-modellen vurdert som den mest pålitelige av maskinlærings-modellene. De beste feilindikatorene for Data A var kurtose- og skjevhet-indikatorene, mens klaringsfaktor og formfaktor ga best nøyaktighet for Data B.
Videre arbeid bør unngå bruk av data som inneholder RMS-verdier, og fokusere på bruk av signalbaserte verdier slik som i Data B. Dataprosessering og feilmerking bør også utføres i frekvensplanet, ettersom en stor svakhet ved avhandlingen er at metodikken kun ble anvendt i tidplanet. Andre ytelsesindikatorer som robusthet bør også brukes for å vurdere ytelsen til maskinlærings-modellene
A precise, General, Non-Invasive and Automatic Speed Estimation Method for MCSA Steady-State Diagnosis and Efficiency Estimation of Induction Motors in the 4.0 Industry
Tesis por compendio[ES] Hay dos aspectos cruciales a la hora de operar motores de inducción en la industria: la estimación de su eficiencia (para minimizar el consumo de energía) y su diagnóstico (para evitar paradas intempestivas y reducir los costes de mantenimiento). Para estimar la eficiencia del motor es necesario medir tensiones y corrientes. Por ello, resulta conveniente y muy útil utilizar la misma corriente para diagnosticar también el motor (Motor Current Signature Analysis: MCSA). En este sentido, la técnica MCSA más adecuada es aquella basada en la localización de armónicos de fallo en el espectro de la corriente de línea del estator en régimen permanente, pues esta es la condición de funcionamiento de la mayoría de los motores de inducción de la industria. Por otro lado, dado que la frecuencia de estos armónicos depende de la velocidad, resulta imprescindible conocer esta magnitud con precisión, ya que esto permite localizar correctamente los armónicos de fallo, y, por tanto, reducir las posibilidades de falsos positivos/negativos. A su vez, una medida precisa de la velocidad también permite calcular con precisión la potencia mecánica, lo que se traduce en una estimación más exacta del rendimiento. Por último, para adaptarse a las necesidades de la Industria 4.0, en la que se monitoriza continuamente un gran número de motores, la velocidad también debe ser obtenida de manera no invasiva, automática y para cualquier motor de inducción. A este respecto, dado que la medición precisa de la velocidad a través de un encóder es invasiva y costosa, las técnicas de estimación de velocidad sin sensores (SSE en inglés) se convierten en la mejor opción.
En la primera parte de esta tesis se realiza un análisis exhaustivo de las familias de técnicas SSE presentes en la literatura técnica. Como se demuestra en ella, aquellos métodos basados en armónicos de ranura (RSHs en inglés) y en armónicos laterales de frecuencia rotacional (RFSHs) son potencialmente los únicos que pueden satisfacer todos los requisitos mencionados anteriormente. Sin embargo, como también se demuestra en esta parte, y hasta esta tesis, siempre había existido un compromiso entre la precisión (característica de los RSHs) y la aplicabilidad general del método (característica de los RFSHs).
En la segunda parte, y núcleo de esta tesis, se presenta una metodología que acaba con este compromiso, proporcionando así el primer método de estimación de velocidad preciso, general, no invasivo y automático para el diagnóstico en estado estacionario MCSA y la estimación de la eficiencia de motores de inducción que operan en un contexto de Industria 4.0. Esto se consigue desarrollando una novedosa técnica basada en RSHs que, por primera vez en la literatura técnica, elimina la necesidad de conocer/estimar el número de ranuras del rotor, lo que había impedido hasta la fecha que estos métodos fueran de aplicación general. Esta técnica proporciona además un procedimiento fiable y automático para localizar la familia de RSHs en el espectro de la corriente de línea de un motor de inducción. De igual forma y sin la ayuda de un experto, la técnica es capaz de determinar los parámetros necesarios para estimar la velocidad a partir de los RSHs, utilizando medidas tomadas en régimen estacionario. La metodología es validada utilizando motores con diferentes características y tipos de alimentaciones, empleando para ello simulaciones, pruebas de laboratorio y 105 motores industriales. Además, se muestra un caso de aplicación industrial en el que el algoritmo desarrollado se implementa en un sistema de monitorización continua mediante MCSA, lo que acaba conduciendo al descubrimiento de un nuevo fallo en motores sumergibles de pozo profundo: el desgaste de los anillos de cortocircuito. Por último, se presenta una segunda aplicación directa para este tipo de motores derivada del procedimiento de detección de RSHs: el uso de estos armónicos para diagnosticar, en fase temprana, cortocircuitos entre espiras.[CA] Hi ha dos aspectes crucials a l'hora d'operar motors d'inducció en la indústria: l'estimació de la seua eficiència (per a minimitzar el consum d'energia) i el seu diagnòstic (per a evitar parades intempestives i reduir els costos de manteniment). Per a estimar l'eficiència del motor és necessari mesurar tensions i corrents. Per això, resulta convenient i molt útil utilitzar el mateix corrent per a diagnosticar també el motor (Motor Current Signature Analysis: MCSA). En aquest sentit, la tècnica MCSA més adequada és aquella basada en la localització d'harmònics de fallada en l'espectre del corrent de línia de l'estator en règim permanent, ja que aquesta és la condició de funcionament de la majoria dels motors d'inducció de la indústria. D'altra banda, atés que la freqüència d'aquests harmònics depén de la velocitat, resulta imprescindible conéixer aquesta magnitud amb precisió, ja que això permet localitzar correctament els harmònics de fallada i, per tant, reduir les possibilitats de falsos positius/negatius. Al seu torn, una mesura precisa de la velocitat també permet calcular amb precisió la potència mecànica, la qual cosa es tradueix en una estimació més exacta del rendiment. Finalment, per a adaptar-se a les necessitats de la Indústria 4.0, en la qual es monitora contínuament un gran nombre de motors, la velocitat també ha de ser obtinguda de manera no invasiva, automàtica i per a qualsevol motor d'inducció. En aquest sentit, atès que el mesurament precís de la velocitat a través d'un encóder és invasiva i costosa, les tècniques d'estimació de velocitat sense sensors (SSE en anglés) es converteixen en la millor opció.
En la primera part d'aquesta tesi es realitza una anàlisi exhaustiva de totes les famílies de tècniques SSE presents en la literatura tècnica. Com es demostra en ella, aquells mètodes basats en harmònics de ranura (RSHs en anglès) i harmònics laterals de freqüència rotacional (RFSHs en anglés) són els més prometedors, ja que son potencialment els únics que poden satisfer tots els requisits esmentats anteriorment. No obstant això, com també es demostra en aquesta part, i fins a aquesta tesi, sempre havia existit un compromís entre la precisió (característica dels RSHs) i l'aplicabilitat general del mètode (característica dels RFSHs).
En la segona part, i nucli d'aquesta tesi, es presenta una metodologia que acaba amb aquest compromís, proporcionant així el primer mètode d'estimació de velocitat precís, general, no invasiu i automàtic per al diagnòstic en estat estacionari MCSA i l'estimació de l'eficiència de motors d'inducció que operen en un context d'Indústria 4.0. Això s'aconsegueix desenvolupant una nova tècnica basada en RSHs que, per primera vegada en la literatura tècnica, elimina la necessitat de conéixer/estimar el nombre de ranures del rotor, cosa que havia impedit fins avui que aquests mètodes foren d'aplicació general. Aquesta tècnica proporciona a més un procediment fiable i automàtic per a localitzar la família de RSHs en l'espectre del corrent de línia d'un motor d'inducció. De la mateixa forma i sense l'ajuda d'un expert, la tècnica és capaç de determinar els paràmetres necessaris per a estimar la velocitat a partir dels RSHs, utilitzant mesures preses en règim estacionari. La metodologia és validada utilitzant motors amb diferents característiques i condicions d'alimentació, emprant per a això simulacions, proves de laboratori i 105 motors industrials. A més, es mostra un cas real d'aplicació industrial en el qual l'algoritme desenvolupat és implementat en un sistema de monitoratge continu mitjançant MCSA, la qual cosa acaba conduint al descobriment d'una nova fallada en motors submergibles de pou profund: el desgast dels anells de curtcircuit. Finalment, es presenta una segona aplicació directa per a aquest tipus de motors derivada del procediment de detecció de RSHs: l'ús d'aquests harmònics per a diagnosticar, en fase primerenca, curtcircuits entre espires.[EN] There are two crucial aspects when operating induction motors in industry: efficiency estimation (to minimize energy consumption) and diagnosis (to avoid untimely outages and reduce maintenance costs). To estimate the motor's efficiency, it is necessary to measure voltages and currents. Hence, it is convenient and very useful using the same current to also diagnose the motor (Motor Current Signature Analysis: MCSA). In this regard, the most suitable MCSA technique is that based on locating fault harmonics in the spectrum of the stator line current under steady-state, as this is the operating condition of most induction motors in industry. Since the frequency of these harmonics depends on the speed, it becomes essential to be able to know this magnitude with precision, as this makes it possible to correctly locate the fault harmonics, and therefore, reduce the chances of false positives/negatives. In turn, an accurate speed information also allows to calculate the mechanical power with precision, which results in a more accurate estimation of the motor performance. Finally, to adapt to the needs of 4.0 Industry, where large numbers of motors are continuously monitored, the speed must not only be obtained very accurately, but also non-invasively, automatically (without the need for an expert) and for any induction motor. In this regard, since precise speed measurement through a shaft sensor is invasive and expensive, Sensorless Speed Estimation (SSE) techniques become the best option.
The first part of this thesis conducts a thorough analysis of all the families of SSE techniques present in the technical literature. As demonstrated therein, those techniques based on Slotting and Rotational Frequency Sideband Harmonics are the most promising, as they can potentially meet all the aforementioned requirements. However, as also proved in this part, and up to this thesis, there had always been a trade-off between accuracy, characteristic of Rotor Slot Harmonics (RSHs), and general applicability, characteristic of Rotational Frequency Sideband Harmonics (RFSHs).
The second part, and core of this thesis, presents a methodology that ends with this trade-off between accuracy and general applicability, thus providing the first precise, general, noninvasive and automatic speed estimation method for MCSA steady-state diagnosis and efficiency estimation of induction motors that operate in a 4.0 Industry context. This is achieved by developing a novel RSH-based technique that, for the first time in technical literature, eliminates the need to know/estimate the number of rotor slots, which had so far prevented these techniques to be generally applicable. This technique also provides a reliable and automatic procedure to, from among the high number of significant harmonics present in the spectrum of the line current of an induction motor, locate the RSHs family. Also automatically and without the help of an expert, the technique is able to determine the parameters needed to estimate speed from RSHs, using only measurements taken during the motor normal operation at steady-state. The methodology is validated using motors with different characteristics and supply conditions, by simulations, lab tests and with 105 industrial motors. Furthermore, a real industrial case of application is shown as well, where the speed estimation algorithm is implemented in a continuous motor condition monitoring system via MCSA, which eventually leads to the discovery of a new fault in deep-well submersible motors: the wear of end-rings. Finally, a second direct application derived from the reliable and automatic procedure to detect RSHs is presented: the use of these harmonics to diagnose early-stage inter-turn faults in induction motors of deep-well submersible pumps.Bonet Jara, J. (2023). A precise, General, Non-Invasive and Automatic Speed Estimation Method for MCSA Steady-State Diagnosis and Efficiency Estimation of Induction Motors in the 4.0 Industry [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/194269Compendi
Machine learning approach for dynamic event identification in power systems with wide area measurement systems
Orientador: Prof. Dr. Alexandre Rasi AokiCoorientador: Prof. Dr. Ricardo SchumacherDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa : Curitiba, 13/02/2023Inclui referênciasResumo: Ao longo dos últimos dez anos, a disponibilidade de WAMS (Wide Area Measurement Systems) tem constantemente aumentado e, com isso, a necessidade de se otimizar seu uso em relação a uma ampla gama de capacidades requeridas nos centros de operação. Concorrentemente, o sistema brasileiro tem observado diversos eventos em múltiplos níveis de criticalidade e, portanto, formas de rapidamente identificar irregularidades na rede elétrica têm sido requisitadas pelos operadores. Todavia, mesmo com tal diversidade de eventos registrados por PMUs (Phasor Measurement Unit), há dificuldades em se consolidar um banco de dados de eventos e, ademais, sistemas diferem uns dos outros - isto é, os volumes de dados requeridos para machine learning e a especificidade de cada sistema criam desafios para a construção de aplicações para detecção e identificação de eventos em uma dada rede. De tal maneira, o presente trabalho propõe uma forma de endereçar tais restrições e habilitar o uso de modelos de machine learning na vida real ao modelar um sistema real, simular uma grande quantia de eventos (como medição de PMU) e executar o processo de aprendizado de máquina com esses dados simulados. Tendo posse de qualquer conjunto de dados que contenha medições de evento da mesma PMU simulada, uma validação da aplicabilidade e performance do modelo obtido pode ser feita. Assim, um processo reprodutível e escalável foi definido pelo trabalho a partir de um estudo de caso no corredor Salto Caxias, um subsistema da rede elétrica paranaense operado pela COPEL, que forneceu três conjuntos de dados contendo eventos registrados em uma PMU dessa área. Alguns componentes como barras, linhas de transmissão, transformadores, geradores, PSSs, excitadores e controles de turbina foram modelados dentro da Power System Toolbox, embasada em MATLAB, para simulação de eventos. O algoritmo de machine learning selecionado para provar o conceito estabelecido foi rede neural artificial, definindo-se quatro classes possíveis para reconhecimento - "Curto-circuito", "Perda de Carga", "Perda de Linha" e "Normal". Com o modelo de machine learning definido e treinado, se aplicaram os dados de eventos reais nele. Os resultados mostram que as métricas da rede neural no processo de aprendizado foram geralmente suficientes para aplicação em vida real, mas que sua performance nos conjuntos de dados de eventos reais foi abaixo da registrada com os dados simulados. Todavia, considerando-se que os dados reais providenciados são de eventos longínquos à PMU observada e ao próprio sistema modelado, distorções e atenuações de sinal são inerentes. Assim, pode-se dizer que o método proposto é aplicável, com mais etapas de pré-processamento de dados, a qualquer dado sistema - caso ele seja minuciosamente modelado e haja disponibilidade de conjuntos de dados de eventos internos ao sistema.Abstract: Over the last ten years, the availability of WAMS (Wide Area Measurement Systems) has steadily increased and, with it, the need to optimize its usage concerning a large array of capabilities required at the operation centers. Concurrently, the Brazilian system has witnessed various events at multiple levels of criticality, and, thus, ways to quickly identify irregularities in the grid have been more and more requested by power transmission and distribution companies. The introduction of machine learning models and algorithms in such a context has been explored by the scientific community. However, even with such a diversity of events and their PMU (Phasor Measurement Unit) measurements, there is hardship in consolidating an event database and systems differ from each other - that is, the data volume required for machine learning and the specificity of each power system create challenges in constructing applications for detection and identification of events in a given grid. As such, the present work proposes a way to address those constraints and further enable the real-life application of machine learning models in a power system with WAMS through the modeling of a real-life system, simulating a large database of events as if they were registered through a PMU in said system and training machine learning models on this simulated data. If one has any dataset containing event measurements from the same PMU (which was simulated), a validation of model performance and applicability can be performed. A reproducible and scalable process was defined to achieve this through one case study for the Salto Caxias subsystem of the Paraná state grid, operated by COPEL, who provided the author with three event datasets captured from a PMU in the aforementioned system. Some components of the system were modeled in MATLAB-based Power System Toolbox for dynamic simulation, such as generators, PSSs, exciters, and turbine governors in addition to buses, transmission lines, and transformers. The selected algorithm for this proof-of-concept was artificial neural network, defining four distinct possible classes it can recognize - "Short-circuit", "Load Loss", "Line Loss" and "Normal". With the machine learning model defined and trained, its application was executed on real event datasets. The results show that the metrics of the neural network model on the learning process were generally sufficient for real-life solutions, but its performance on the real event datasets was below that of the performance on simulated data. However, considering that the provided datasets were from events that happened far away from the selected PMU and its modeled system, signal distortions and attenuations are present. Thus, it can be stated that the proposed method is applicable, with further data preprocessing, to any given system - as long as it is thoroughly modeled and there is availability of datasets of events that happened within it
Artificial Intelligence-Based Methods for Power System Security Assessment with Limited Dataset
This thesis concerns the relationship between the load, load model, and power system stability. It investigates the possibility of developing a dynamic load model to represent the power system load characteristic during system faults when the power system operates at a high percentage of the power generation from wind farms, solar power, and vehicle-to-grid technology. Additionally, with artificial intelligence supporting the seamless integration of an increasingly distributed and multi-directional power system to unlock the vast potential of renewables, new approaches are proposed to improve the training performance for the applications of artificial neural networks in non-intrusive load monitoring and dynamic security assessment.
An improved hybrid load model is proposed to represent the load characteristics in the above power system operation. Genetic algorithms and the multi-curve identification method are applied to determine the parameters of the load model, aiming to minimize the error between the estimated and measured values. The results indicate that the proposed hybrid load model has a reasonably low fitting error to represent the load dynamics.
In addition, new approaches are proposed to tackle the challenges posed by limited data when training artificial neural networks (ANNs) for their application in power systems. The knowledge transfer approach is utilized to support the ANN training to generate synthetic data for non-intrusive load monitoring. The results indicate that this approach improves the issue of mode collapse and reduces the need for lengthy training iterations, making the ANN effective for generating synthetic data from limited data. Moreover, the knowledge transfer approach also supports ANN training with limited data for dynamic security assessment. Kernel principal component analysis is employed to eliminate the dimensionality reduction step. The results indicate an improvement in the training performance
Artificial neural network-based fault diagnosis of gearbox using empirical mode decomposition from vibration response
This paper presents a gearbox defect diagnosis based on vibration behaviour. In order to record the vibration response under various circumstances, an industrial gearbox was used as the basis for an experimental setup. The signals resulting from gear wear were processed using an empirical mode decomposition for two operating time intervals (zero-hour running time and thirty-hour running time). The first three intrinsic mode functions and the corresponding frequency response were detected. The ten statistical parameters most sensitive to gear wear were selected using an evaluation method based on Euclidean distance. Using the identified features, an artificial neural network (ANN) was trained to track the gearbox for the selected future data set. The neural network received its input from the statistical parameters, and its output was the number of gearbox running hours. To achieve faster convergence, the radial basis function and the backpropagation neural network were compared. The superiority of the proposed strategy is demonstrated by comparing the performance of ANN. For monitoring the condition of industrial gears, the proposed strategy is found to be effective and trustworthy
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