Machine learning approach for predictive maintenance of the electrical submersible pumps (ESPs)

Electrical submersible pumps (ESPs) are considered the second-most widely used artificial lift method in the petroleum industry. As with any pumping artificial lift method, ESPs exhibit failures. The maintenance of ESPs expends a lot of resources, and manpower and is usually triggered and accompanied by the reactive process monitoring of multivariate sensor data. This paper presents a methodology to deploy the principal component analysis and extreme gradient boosting trees (XGBoosting) in predictive maintenance in order to analyze real-time sensor data to predict failures in ESPs. The system contributes to an efficiency increase by reducing the time required to dismantle the pumping system, inspect it, and perform failure analysis. This objective is achieved by applying the principal component analysis as an unsupervised technique; then, its output is pipelined with an XGBoosting model for further prediction of the system status. In comparison to traditional approaches that have been utilized for the diagnosis of ESPs, the proposed model is able to identify deeper functional relationships and longer-term trends inferred from historical data. The novel workflow with the predictive model can provide signals 7 days before the actual failure event, with an F1-score more than 0.71 on the test set. Increasing production efficiencies through the proactive identification of failure events and the avoidance of deferment losses can be accomplished by means of the real-time alarming system presented in this work

Oil and Gas flow Anomaly Detection on offshore naturally flowing wells using Deep Neural Networks

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe Oil and Gas industry, as never before, faces multiple challenges. It is being impugned for being dirty, a pollutant, and hence the more demand for green alternatives. Nevertheless, the world still has to rely heavily on hydrocarbons, since it is the most traditional and stable source of energy, as opposed to extensively promoted hydro, solar or wind power. Major operators are challenged to produce the oil more efficiently, to counteract the newly arising energy sources, with less of a climate footprint, more scrutinized expenditure, thus facing high skepticism regarding its future. It has to become greener, and hence to act in a manner not required previously. While most of the tools used by the Hydrocarbon E&P industry is expensive and has been used for many years, it is paramount for the industry’s survival and prosperity to apply predictive maintenance technologies, that would foresee potential failures, making production safer, lowering downtime, increasing productivity and diminishing maintenance costs. Many efforts were applied in order to define the most accurate and effective predictive methods, however data scarcity affects the speed and capacity for further experimentations. Whilst it would be highly beneficial for the industry to invest in Artificial Intelligence, this research aims at exploring, in depth, the subject of Anomaly Detection, using the open public data from Petrobras, that was developed by experts. For this research the Deep Learning Neural Networks, such as Recurrent Neural Networks with LSTM and GRU backbones, were implemented for multi-class classification of undesirable events on naturally flowing wells. Further, several hyperparameter optimization tools were explored, mainly focusing on Genetic Algorithms as being the most advanced methods for such kind of tasks. The research concluded with the best performing algorithm with 2 stacked GRU and the following vector of hyperparameters weights: [1, 47, 40, 14], which stand for timestep 1, number of hidden units 47, number of epochs 40 and batch size 14, producing F1 equal to 0.97%. As the world faces many issues, one of which is the detrimental effect of heavy industries to the environment and as result adverse global climate change, this project is an attempt to contribute to the field of applying Artificial Intelligence in the Oil and Gas industry, with the intention to make it more efficient, transparent and sustainable

Exploring the adoption of a conceptual data analytics framework for subsurface energy production systems: a study of predictive maintenance, multi-phase flow estimation, and production optimization

Als die Technologie weiter fortschreitet und immer stärker in der Öl- und Gasindustrie integriert wird, steht eine enorme Menge an Daten in verschiedenen Wissenschaftsdisziplinen zur Verfügung, die neue Möglichkeiten bieten, informationsreiche und handlungsorientierte Informationen zu gewinnen. Die Konvergenz der digitalen Transformation mit der Physik des Flüssigkeitsflusses durch poröse Medien und Pipeline hat die Entwicklung und Anwendung von maschinellem Lernen (ML) vorangetrieben, um weiteren Mehrwert aus diesen Daten zu gewinnen. Als Folge hat sich die digitale Transformation und ihre zugehörigen maschinellen Lernanwendungen zu einem neuen Forschungsgebiet entwickelt. Die Transformation von Brownfields in digitale Ölfelder kann bei der Energieproduktion helfen, indem verschiedene Ziele erreicht werden, einschließlich erhöhter betrieblicher Effizienz, Produktionsoptimierung, Zusammenarbeit, Datenintegration, Entscheidungsunterstützung und Workflow-Automatisierung. Diese Arbeit zielt darauf ab, ein Rahmenwerk für diese Anwendungen zu präsentieren, insbesondere durch die Implementierung virtueller Sensoren, Vorhersageanalytik mithilfe von Vorhersagewartung für die Produktionshydraulik-Systeme (mit dem Schwerpunkt auf elektrischen Unterwasserpumpen) und präskriptiven Analytik für die Produktionsoptimierung in Dampf- und Wasserflutprojekten. In Bezug auf virtuelle Messungen ist eine genaue Schätzung von Mehrphasenströmen für die Überwachung und Verbesserung von Produktionsprozessen entscheidend. Diese Studie präsentiert einen datengetriebenen Ansatz zur Berechnung von Mehrphasenströmen mithilfe von Sensormessungen in elektrischen untergetauchten Pumpbrunnen. Es wird eine ausführliche exploratorische Datenanalyse durchgeführt, einschließlich einer Ein Variablen Studie der Zielausgänge (Flüssigkeitsrate und Wasseranteil), einer Mehrvariablen-Studie der Beziehungen zwischen Eingaben und Ausgaben sowie einer Datengruppierung basierend auf Hauptkomponentenprojektionen und Clusteralgorithmen. Feature Priorisierungsexperimente werden durchgeführt, um die einflussreichsten Parameter in der Vorhersage von Fließraten zu identifizieren. Die Modellvergleich erfolgt anhand des mittleren absoluten Fehlers, des mittleren quadratischen Fehlers und des Bestimmtheitskoeffizienten. Die Ergebnisse zeigen, dass die CNN-LSTM-Netzwerkarchitektur besonders effektiv bei der Zeitreihenanalyse von ESP-Sensordaten ist, da die 1D-CNN-Schichten automatisch Merkmale extrahieren und informative Darstellungen von Zeitreihendaten erzeugen können. Anschließend wird in dieser Studie eine Methodik zur Umsetzung von Vorhersagewartungen für künstliche Hebesysteme, insbesondere bei der Wartung von Elektrischen Untergetauchten Pumpen (ESP), vorgestellt. Conventional maintenance practices for ESPs require extensive resources and manpower, and are often initiated through reactive monitoring of multivariate sensor data. Um dieses Problem zu lösen, wird die Verwendung von Hauptkomponentenanalyse (PCA) und Extreme Gradient Boosting Trees (XGBoost) zur Analyse von Echtzeitsensordaten und Vorhersage möglicher Ausfälle in ESPs eingesetzt. PCA wird als unsupervised technique eingesetzt und sein Ausgang wird weiter vom XGBoost-Modell für die Vorhersage des Systemstatus verarbeitet. Das resultierende Vorhersagemodell hat gezeigt, dass es Signale von möglichen Ausfällen bis zu sieben Tagen im Voraus bereitstellen kann, mit einer F1-Bewertung größer als 0,71 im Testset. Diese Studie integriert auch Model-Free Reinforcement Learning (RL) Algorithmen zur Unterstützung bei Entscheidungen im Rahmen der Produktionsoptimierung. Die Aufgabe, die optimalen Injektionsstrategien zu bestimmen, stellt Herausforderungen aufgrund der Komplexität der zugrundeliegenden Dynamik, einschließlich nichtlinearer Formulierung, zeitlicher Variationen und Reservoirstrukturheterogenität. Um diese Herausforderungen zu bewältigen, wurde das Problem als Markov-Entscheidungsprozess reformuliert und RL-Algorithmen wurden eingesetzt, um Handlungen zu bestimmen, die die Produktion optimieren. Die Ergebnisse zeigen, dass der RL-Agent in der Lage war, den Netto-Barwert (NPV) durch kontinuierliche Interaktion mit der Umgebung und iterative Verfeinerung des dynamischen Prozesses über mehrere Episoden signifikant zu verbessern. Dies zeigt das Potenzial von RL-Algorithmen, effektive und effiziente Lösungen für komplexe Optimierungsprobleme im Produktionsbereich zu bieten.As technology continues to advance and become more integrated in the oil and gas industry, a vast amount of data is now prevalent across various scientific disciplines, providing new opportunities to gain insightful and actionable information. The convergence of digital transformation with the physics of fluid flow through porous media and pipelines has driven the advancement and application of machine learning (ML) techniques to extract further value from this data. As a result, digital transformation and its associated machine-learning applications have become a new area of scientific investigation. The transformation of brownfields into digital oilfields can aid in energy production by accomplishing various objectives, including increased operational efficiency, production optimization, collaboration, data integration, decision support, and workflow automation. This work aims to present a framework of these applications, specifically through the implementation of virtual sensing, predictive analytics using predictive maintenance on production hydraulic systems (with a focus on electrical submersible pumps), and prescriptive analytics for production optimization in steam and waterflooding projects. In terms of virtual sensing, the accurate estimation of multi-phase flow rates is crucial for monitoring and improving production processes. This study presents a data-driven approach for calculating multi-phase flow rates using sensor measurements located in electrical submersible pumped wells. An exhaustive exploratory data analysis is conducted, including a univariate study of the target outputs (liquid rate and water cut), a multivariate study of the relationships between inputs and outputs, and data grouping based on principal component projections and clustering algorithms. Feature prioritization experiments are performed to identify the most influential parameters in the prediction of flow rates. Model comparison is done using the mean absolute error, mean squared error and coefficient of determination. The results indicate that the CNN-LSTM network architecture is particularly effective in time series analysis for ESP sensor data, as the 1D-CNN layers are capable of extracting features and generating informative representations of time series data automatically. Subsequently, the study presented herein a methodology for implementing predictive maintenance on artificial lift systems, specifically regarding the maintenance of Electrical Submersible Pumps (ESPs). Conventional maintenance practices for ESPs require extensive resources and manpower and are often initiated through reactive monitoring of multivariate sensor data. To address this issue, the study employs the use of principal component analysis (PCA) and extreme gradient boosting trees (XGBoost) to analyze real-time sensor data and predict potential failures in ESPs. PCA is utilized as an unsupervised technique and its output is further processed by the XGBoost model for prediction of system status. The resulting predictive model has been shown to provide signals of potential failures up to seven days in advance, with an F1 score greater than 0.71 on the test set. In addition to the data-driven modeling approach, The present study also in- corporates model-free reinforcement learning (RL) algorithms to aid in decision-making in production optimization. The task of determining the optimal injection strategy poses challenges due to the complexity of the underlying dynamics, including nonlinear formulation, temporal variations, and reservoir heterogeneity. To tackle these challenges, the problem was reformulated as a Markov decision process and RL algorithms were employed to determine actions that maximize production yield. The results of the study demonstrate that the RL agent was able to significantly enhance the net present value (NPV) by continuously interacting with the environment and iteratively refining the dynamic process through multiple episodes. This showcases the potential for RL algorithms to provide effective and efficient solutions for complex optimization problems in the production domain. In conclusion, this study represents an original contribution to the field of data-driven applications in subsurface energy systems. It proposes a data-driven method for determining multi-phase flow rates in electrical submersible pumped (ESP) wells utilizing sensor measurements. The methodology includes conducting exploratory data analysis, conducting experiments to prioritize features, and evaluating models based on mean absolute error, mean squared error, and coefficient of determination. The findings indicate that a convolutional neural network-long short-term memory (CNN-LSTM) network is an effective approach for time series analysis in ESPs. In addition, the study implements principal component analysis (PCA) and extreme gradient boosting trees (XGBoost) to perform predictive maintenance on ESPs and anticipate potential failures up to a seven-day horizon. Furthermore, the study applies model-free reinforcement learning (RL) algorithms to aid decision-making in production optimization and enhance net present value (NPV)

A summary of artificial lift failure, remedies and run life improvements in conventional and unconventional wells.

Artificial lift (AL) systems are crucial for enhancing oil and gas production from reservoirs. However, the failure of these systems can lead to significant losses in production and revenue. This paper explores the different types of AL failures and the causes behind them. The article discusses the traditional methods of identifying and mitigating these failures and highlights the need for new designs and technologies to improve the run life of AL systems. Advances in AL system design and materials, as well as new methods for monitoring and predicting failures using data analytics and machine learning techniques, have been discussed. The findings of this work provide valuable insights for researchers and practitioners in the development of more reliable and efficient AL systems

Predicting Non-Newtonian Fluid Electric Submersible Pump failure using Deep Learning and Artificial Neural Network

The monitoring of electric submersible pumps (ESPs) is essential for optimal petroleum artificial lifting operations. Most ESP research are aimed at operation improvement and optimization of the centrifuge multi-stage pump motor and the load that the pump has to discharge which is a function of the pumps mechanical properties and characteristics, liquid compositions, pressure and temperature. ESPs failure often lead to oil production losses or “oil deferment” which affects revenue for all the parties involved. Also, pulling the ESP out of the wellbore of interest, requires mobilization of a rig because it is installed several hundred meters down the wellbore. To prevent these loses, a predictive approach is needed to avert these scenarios. In the current decade, machine learning algorithms studies have spurred real- time technologies research interest due to their abilities to predict future outcomes using already existing data sets. This study presents a predictive approach for Electric Submersible Pump failure during artificial lift operations. The study creates an “algorithm” that helps to predict via Machine learning, the failure of an ESP with the assumption that failure is usually caused by pressure build-ups. A deep learning model for predicting ESP failure was proposed and artificial neural network was used in developing the suggested model. Based on the outcomes of this study, it can be concluded that the selected AI algorithm and its characteristics, are suitable for applications in detecting ESP failure before it happens using upstream-data

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

Current-based Techniques for Condition Monitoring of Pumps

[ES] Las bombas hidráulicas son el núcleo de muchos procesos en la industria y el sector servicios. Conviene tener en cuenta que los motores eléctricos son responsables del 69% del consumo de energía eléctrica en la industria, siendo en torno a un 22% de motores utilizados para el accionamiento de bombas. Los fallos de estas bombas pueden provocar averías en el proceso y, por lo tanto, implican altos costes económicos para el operador de la planta. Además, un funcionamiento defectuoso de las bombas conlleva una reducción de la eficiencia energética de la planta. De forma habitual, se utilizan principalmente dos tipos de estrategias orientadas al mantenimiento de maquinaria. Una estrategia de mantenimiento (mantenimiento preventivo) consiste en la sustitución de las piezas desgastadas en un intervalo de tiempo fijo. Este tipo de estrategia presenta muchas desventajas asociadas a la escasa optimización en el uso de los recursos y al consiguiente impacto económico. Por otro lado, la estrategia basada en la condición del equipo (mantenimiento basado en la condición) liga el reemplazo de las piezas desgastadas al estado del equipo, el cual es monitorizado a través de señales adquiridas mediante sensores. Sin embargo, el uso de sensores tiene algunos inconvenientes, como costes de inversión adicionales, posibles problemas en el montaje del sensor y posibles fallos del mismo. El análisis de la señal de corriente no se ha utilizado de forma habitual en la práctica para evaluar el estado de la bomba, aunque en muchas aplicaciones se dispone de sensores de corriente ya instalados que se podrían utilizar a tal fin. Se ha demostrado que técnicas basadas en el análisis de la corriente resultan de gran utilidad para diagnosticar varios tipos de fallos en motores eléctricos. De hecho, el análisis de la firma de corriente del motor se utiliza hoy en día ampliamente en la industria, especialmente para el diagnóstico de fallos en motores de inducción. En la presente tesis, se evalúa la utilización de la técnica de análisis de corrientes para el diagnóstico de fallos típicos relacionados con las bombas en diferentes aplicaciones. Se investigan tres tipos de bombas diferentes: bombas en línea de rotor húmedo, bombas de rotor seco y bombas sumergibles. En la tesis se han adaptado diversas técnicas, previamente empleadas para la detección de fallos en motores, al diagnóstico de fallos en la propia bomba. Los resultados indican que fallos como obstrucción de la bomba, fisura del impulsor y desgaste de los cojinetes influyen especialmente en dos frecuencias del espectro de corriente, las cuales pueden utilizarse como base de estrategias de mantenimiento basadas en la condición. En concreto, en las bombas de rotor húmedo, estos dos indicadores de fallo varían sensiblemente en función del punto de carga hidráulica de la bomba. Con la ayuda de un método de extracción de características basado en la motor reference frame theory, se demuestra que las mencionadas frecuencias pueden analizarse en tiempo real en un entorno industrial. Además, se presentan directrices para la monitorización en la nube y se valida con la ayuda de ensayos de laboratorio. Adicionalmente, se demuestra que los fallos son también detectables al analizar la corriente de arranque mediante herramientas de descomposición tiempo-frecuencia. Este hito no se había abordado anteriormente en la literatura técnica del área en lo referente a la detección de fallos en bombas. En conclusión, los resultados de este trabajo demuestran que los métodos de diagnóstico basados en la corriente pueden detectar con éxito diversos tipos de fallo en bombas, lo cual constituye un punto de gran interés para las industrias que utilicen estos activos en sus procesos.[CA] Les bombes hidràuliques són el nucli de molts processos en la indústria i en el sector dels serveis. Cal mencionar que els motors elèctrics són responsables del 69% del consum de la energia elèctrica en la indústria, sent al voltant del 22% dels motors utilitzats per l'accionament de bombes. Les fallades d'aquestes bombes poden causar avaries en els processos, i per tant, representen un alt cost econòmic per a l'operador de la planta. A més a més, un funcionament defectuós en les bombes representa una reducció de l'eficiència energètica de la planta. De manera habitual, s'utilitzen principalment dos tipus d'estratègies orientades al manteniment de la maquinària. Una estratègia de manteniment (manteniment preventiu) consisteix en la canvi de les peces desgastades en un interval fixe de temps. Aquest tipus d'estratègia presenta molts desavantatges associats a la reduïda optimització en el ús dels recursos i el seu impacte econòmic. D'altra banda, la estratègia basada en la condició dels equipaments (manteniment basat en la condició) enllaça la substitució de les peces desgastades al estat de l'equip, el qual es monitoritzat per mig de senyals adquirides per sensors. No obstant això, el ús de sensors té alguns inconvenients com costos d'inversió addicionals, possibles problemes al muntatge i possibles fallades. L'anàlisi dels senyals de corrent no s'utilitzen de manera habitual en la pràctica per avaluar l'estat de la bomba, encara que en moltes aplicacions, estos sensors es troben instal·lats i es podrien fer servir per a aquesta finalitat. Ha estat demostrat que les tècniques basades en l'anàlisi de la corrent són de gran utilitat per el diagnosi de diversos tipus de fallades en motors elèctrics. De fet, l'anàlisi de la firma de la corrent del motor s'utilitza àmpliament en l'indústria, especialment per el diagnosi de fallades en motors d'inducció. En la present tesi, s'avalua l'utilització de la tècnica d'anàlisi de corrents per el diagnosi de fallades típiques relacionades en bombes per a diferents aplicacions. Se investiguen tres tipus de bombes diferents: bombes en línia de rotor humit, bombes de rotor sec i bombes submergibles. En aquesta tesi se han adaptat diverses tècniques, prèviament utilitzades en el diagnosi de màquines elèctriques, per al diagnosi de la pròpia bomba. Els resultat indiquen que les fallades com obstrucció de la bomba, la fissura de l'impulsor i el desgast dels coixinets influeixen especialment en dos freqüències de l'espectre de la corrent, les quals es poden utilitzar com a base per a una estratègia de manteniment basada en la condició. Particularment, en les bombes de rotor humit, aquestos dos indicadors de fallada varíen sensiblement en funció del punt de càrrega hidràulica de la bomba. En l'ajuda de un mètode d'extracció de característiques basat en la "motor reference frame theory", es demostra que les mencionades freqüències es poden analitzar en temps real en un entorn industrial. A més a més, es presenten directrius per la monitorització en el núvol i es valida en l'ajuda de assajos en el laboratori. Addicionalment, es demostra que les fallades són també detectables quan s'analitza la corrent d'arrancada mitjançant ferramentes de descomposició temps-freqüència. Aquest fet no ha estat analitzat prèviament en cap tipus de literatura tècnica dins del camp de detecció de fallades en bombes. En conclusió, els resultats d'aquest treball demostren que els mètodes de diagnosi basats en la corrent poden detectar en èxit diversos tipus de fallades en bombes, el qual constitueix un punt d'interés per a l'indústria que utilitzen aquest tipus de actiu en els seus processos.[EN] Pumps are the heart of many processes in industry and service sector. Electric motors are responsible for 69% of electric energy consumption in industry, with 22% of them being used for the operation of pumps. Pump faults can lead to process breakdowns and are thus related to high costs for the plant operator. Furthermore, faulty operation of pumps reduces the energy efficiency of the plant. In many cases, a time-based maintenance strategy is applied, which means that typical wear parts are replaced within defined time cycles, which comes with some drawbacks such as poor resource efficiency and high costs. Condition-based maintenance strategies - meaning that the replacement of parts is planned based on the condition of the pump - are often based on the evaluation of sensor signals like vibration or noise. However, the use of sensors also has some drawbacks, such as additional investment costs, frequent problems with the sensor mounting, and possible sensor faults. There is no widespread use of the current signal to make statements about the pump condition, although current sensors are installed in many applications anyway. As for motor fault diagnosis, different current-based techniques have demonstrated their function. Today, motor current signature analysis is used in industry, especially for the diagnosis of induction motors. In this thesis, the current-based diagnosis of typical pump-related faults in different applications is evaluated. In total, three different pump types are investigated: a wet-rotor pump, a dry-runner inline pump, and a submersible pump. The techniques used for motor fault detection are adapted for the diagnosis of pump-related faults. The results indicate that the faults clogging, impeller crack, and bearing wear, in particular, influence two frequencies in the current spectrum, which can be used as a basis for a condition-based maintenance strategy. Especially in wet-rotor pumps, these two fault indicators strongly vary depending on the hydraulic load point of the pump. With the help of a feature extraction method based on the adapted reference frame theory, this work demonstrates that the two frequencies can be analyzed in real time in a field environment. Furthermore, a concept for cloud monitoring is presented and validated with the help of a laboratory test. Additionally, it is demonstrated that the faults are visible if the starting current is evaluated in a time-frequency map, which has not been considered before in the literature on pump-related faults. In summary, the findings of this work indicate that current-based diagnosis methods can successfully detect typical faults in pumps, a fact that is of high interest for companies using these assets in their industrial processes.Becker, V. (2022). Current-based Techniques for Condition Monitoring of Pumps [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19063

A Study on Comparison of Classification Algorithms for Pump Failure Prediction

The reliability of pumps can be compromised by faults, impacting their functionality. Detecting these faults is crucial, and many studies have utilized motor current signals for this purpose. However, as pumps are rotational equipped, vibrations also play a vital role in fault identification. Rising pump failures have led to increased maintenance costs and unavailability, emphasizing the need for cost-effective and dependable machinery operation. This study addresses the imperative challenge of defect classification through the lens of predictive modeling. With a problem statement centered on achieving accurate and efficient identification of defects, this study’s objective is to evaluate the performance of five distinct algorithms: Fine Decision Tree, Medium Decision Tree, Bagged Trees (Ensemble), RUS-Boosted Trees, and Boosted Trees. Leveraging a comprehensive dataset, the study meticulously trained and tested each model, analyzing training accuracy, test accuracy, and Area Under the Curve (AUC) metrics. The results showcase the supremacy of the Fine Decision Tree (91.2% training accuracy, 74% test accuracy, AUC 0.80), the robustness of the Ensemble approach (Bagged Trees with 94.9% training accuracy, 99.9% test accuracy, and AUC 1.00), and the competitiveness of Boosted Trees (89.4% training accuracy, 72.2% test accuracy, AUC 0.79) in defect classification. Notably, Support Vector Machines (SVM), Artificial Neural Networks (ANN), and k-Nearest Neighbors (KNN) exhibited comparatively lower performance. Our study contributes valuable insights into the efficacy of these algorithms, guiding practitioners toward optimal model selection for defect classification scenarios. This research lays a foundation for enhanced decision-making in quality control and predictive maintenance, fostering advancements in the realm of defect prediction and classification

Distributed environmental monitoring

With increasingly ubiquitous use of web-based technologies in society today, autonomous sensor networks represent the future in large-scale information acquisition for applications ranging from environmental monitoring to in vivo sensing. This chapter presents a range of on-going projects with an emphasis on environmental sensing; relevant literature pertaining to sensor networks is reviewed, validated sensing applications are described and the contribution of high-resolution temporal data to better decision-making is discussed

PV monitoring system for a water pumping scheme with lithium-ion battery using free open-source software and IoT technologies.

[EN] The development of photovoltaic (PV) technology is now a reality. The inclusion of lithium-ion batteries in grid-connected PV systems is growing, and the sharp drop in prices for these batteries will enable their use in applications such as PV water pumping schemes (PVWPS). A technical solution for the monitoring and tracking of PV systems is shown in this work, and a novel quasi-real-time monitoring system for a PVWPS with a Li-ion battery is proposed in which open-source Internet of Things (IoT) tools are used. The purpose of the monitoring system is to provide a useful tool for the operation, management, and development of these facilities. The experimental facility used to test the monitoring system includes a 2.4 kWpk photovoltaic field, a 3.6 kVA hybrid inverter, a 3.3 kWh/3 kW lithium-ion battery, a 2.2 kVA variable speed driver, and a 1.5 kW submersible pump. To address this study, data acquisition is performed using commercial hardware solutions that communicate using a Modbus-RTU protocol over an RS485 bus and open software. A Raspberry Pi is used in the data gateway stage, including a PM2 free open-source process manager to increase the robustness and reliability of the monitoring system. Data storage is performed in a server using InfluxDB for open-source database storage and Grafana as open-source data visualization software. Data processing is complemented with a configurable data exporter program that enables users to select and copy the data stored in InfluxDB. Excel or .csv files can be created that include the desired variables with a defined time interval and with the desired data granularity. Finally, the initial results of the monitoring system are presented, and the possible uses of the acquired data and potential users of the system are identified and described.This research was funded by Universitat Politècnica de València (UPV; Program ADSIDEO-cooperation 2017, project titled ¿Characterization of sustainable systems for the pumping of water for human consumption in developing regions and/or refugee camps in Kenya through the implementation of isolated photovoltaic systems with new generation lithium-ion batteries¿).Gimeno Sales, FJ.; Orts-Grau, S.; Escriba-Aparisi, A.; González Altozano, P.; Balbastre Peralta, I.; Martínez-Márquez, CI.; Gasque Albalate, M.... (2020). PV monitoring system for a water pumping scheme with lithium-ion battery using free open-source software and IoT technologies. Sustainability. 12(24):1-28. https://doi.org/10.3390/su122410651S128122