Condition monitoring of hydraulic systems is an area that has grown substantially in the last few decades. This thesis presents a scheme that automatically generates the fault symptoms by on-line processing of raw sensor data from a real test rig. The main purposes of implementing condition monitoring in hydraulic systems are to increase productivity, decrease maintenance costs and increase safety. Since such systems are widely used in industry and becoming more complex in function, reliability of the systems must be supported by an efficient monitoring and maintenance scheme. This work proposes an accurate state space model together with a novel model-based fault diagnosis methodology. The test rig has been fabricated in the Process Automation and Robotics Laboratory at UBC. First, a state space model of the system is derived. The parameters of the model are obtained through either experiments or direct measurements and manufacturer specifications. To validate the model, the simulated and measured states are compared. The results show that under normal operating conditions the simulation program and real system produce similar state trajectories. For the validated model, a condition monitoring scheme based on the Unscented Kalman Filter (UKF) is developed. In simulations, both measurement and process noises are considered. The results show that the algorithm estimates the iii system states with acceptable residual errors. Therefore, the structure is verified to be employed as the fault diagnosis scheme. Five types of faults are investigated in this thesis: loss of load, dynamic friction load, the internal leakage between the two hydraulic cylinder chambers, and the external leakage at either side of the actuator. Also, for each leakage scenario, three levels of leakage are investigated in the tests. The developed UKF-based fault monitoring scheme is tested on the practical system while different fault scenarios are singly introduced to the system. A sinusoidal reference signal is used for the actuator displacement. To diagnose the occurred fault in real time, three criteria, namely residual moving average of the errors, chamber pressures, and actuator characteristics, are considered. Based on the presented experimental results and discussions, the proposed scheme can accurately diagnose the occurred faults. / Applied Science, Faculty of / Mechanical Engineering, Department of / Graduate

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<p>Roller bearings are the common components used in the mechanical systems for mechanical processing and production. The running state of roller bearings often determines the machining accuracy and productivity on a manufacturing line. Roller bearing failure may lead to the shutdown of production lines, resulting in serious economic losses. Therefore, the research on roller bearing fault diagnosis has a great value. This thesis research first proposes a method of signal frequency spectral resampling to tackle the problem of bearing fault detection at different rotating speeds using a single speed dataset for training the network such as the one dimensional convolutional neural network (1D CNN). Second, this research work proposes a technique to connect the graph structures constructed from spectral components of the different bearing fault frequency bands into a sparse graph structure, so that the fault identification can be carried out effectively through a graph neural network in terms of the computation load and classification rate. Finally, the frequency spectral resampling method for feature extraction is validated using our self-collected datasets. The performance of the graph neural network with our proposed sparse graph structure is validated using the Case Western Reserve University (CWRU) dataset as well as our self-collected datasets. The results show that our proposed method achieves higher bearing fault classification accuracy than those recently proposed by other researchers using machine learning approaches and neural networks.</p>

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<p>  </p> <p>The engine is the heart of the vehicle; any problems with this component will cause significant damage and may even result in the car being junked. The engine repair cost is enormous, and there is no guarantee that the existing engine will be repaired or replaced. Fault diagnosis in engines is critical; there have been numerous techniques and tools used for fault diagnosis in this revolutionary world, which require some extra cost to detect and still cannot detect faults such as knocking. The engine can have several problems but knocking is the major issue that blows up the engine and results in the breakdown of the vehicle. Our research focuses on this key issue which not only costs thousands of dollars but also results in waste. According to experts, at a very early stage, knocking can be detected by human senses, either visually or audibly. The most noticeable feature in detecting engine faults is the knocking sound.  Artificial intelligence deep learning neural networks are well known for their ability to simulate humans; we can utilize this domain to train the networks on sound to detect engine knocking. Many neural networks have been designed for various purposes, one of which is classification. The best widely used and reliable network is the convolution neural network (CNN) which takes input as images and classifies them respectively. Engine sounds have been collected from Google’s Machine Perception research. Our research shows that a prominent feature in building these networks is data. Understanding data and making the most of it is central to data science. A better model is created by meaningful data, not just by designing a complex network. We have used a new algorithmic method of extracting sound and feeding it into all variants of CNN, which we call dependent vehicle sound extraction, in which we use fast Fourier transform (FFT), short-time Fourier transform (STFT), and Mel-frequency cepstral coefficients (MFCCs) for processing input sound signals. We validated the utilization of deep learning networks with a unique dependent vehicle feature extraction technique to detect engine knocking with accurate classification.</p>

Plant safety is the most important concern of chemical industries. Process faults can cause economic loses as well as human and environmental damages. Most of the operational faults are normally considered in the process design phase by applying methodologies such as Hazard and Operability Analysis (HAZOP). However, it should be expected that failures may occur in an operating plant. For this reason, it is of paramount importance that plant operators can promptly detect and diagnose such faults in order to take the appropriate corrective actions. In addition, preventive maintenance needs to be considered in order to increase plant safety. Fault diagnosis has been faced with both analytic and data-based models and using several techniques and algorithms. However, there is not yet a general fault diagnosis framework that joins detection and diagnosis of faults, either registered or non-registered in records. Even more, less efforts have been focused to automate and implement the reported approaches in real practice. According to this background, this thesis proposes a general framework for data-driven Fault Detection and Diagnosis (FDD), applicable and susceptible to be automated in any industrial scenario in order to hold the plant safety. Thus, the main requirement for constructing this system is the existence of historical process data. In this sense, promising methods imported from the Machine Learning field are introduced as fault diagnosis methods. The learning algorithms, used as diagnosis methods, have proved to be capable to diagnose not only the modeled faults, but also novel faults. Furthermore, Risk-Based Maintenance (RBM) techniques, widely used in petrochemical industry, are proposed to be applied as part of the preventive maintenance in all industry sectors. The proposed FDD system together with an appropriate preventive maintenance program would represent a potential plant safety program to be implemented. Thus, chapter one presents a general introduction to the thesis topic, as well as the motivation and scope. Then, chapter two reviews the state of the art of the related fields. Fault detection and diagnosis methods found in literature are reviewed. In this sense a taxonomy that joins both Artificial Intelligence (AI) and Process Systems Engineering (PSE) classifications is proposed. The fault diagnosis assessment with performance indices is also reviewed. Moreover, it is exposed the state of the art corresponding to Risk Analysis (RA) as a tool for taking corrective actions to faults and the Maintenance Management for the preventive actions. Finally, the benchmark case studies against which FDD research is commonly validated are examined in this chapter. The second part of the thesis, integrated by chapters three to six, addresses the methods applied during the research work. Chapter three deals with the data pre-processing, chapter four with the feature processing stage and chapter five with the diagnosis algorithms. On the other hand, chapter six introduces the Risk-Based Maintenance techniques for addressing the plant preventive maintenance. The third part includes chapter seven, which constitutes the core of the thesis. In this chapter the proposed general FD system is outlined, divided in three steps: diagnosis model construction, model validation and on-line application. This scheme includes a fault detection module and an Anomaly Detection (AD) methodology for the detection of novel faults. Furthermore, several approaches are derived from this general scheme for continuous and batch processes. The fourth part of the thesis presents the validation of the approaches. Specifically, chapter eight presents the validation of the proposed approaches in continuous processes and chapter nine the validation of batch process approaches. Chapter ten raises the AD methodology in real scaled batch processes. First, the methodology is applied to a lab heat exchanger and then it is applied to a Photo-Fenton pilot plant, which corroborates its potential and success in real practice. Finally, the fifth part, including chapter eleven, is dedicated to stress the final conclusions and the main contributions of the thesis. Also, the scientific production achieved during the research period is listed and prospects on further work are envisaged. / La seguridad de planta es el problema más inquietante para las industrias químicas. Un fallo en planta puede causar pérdidas económicas y daños humanos y al medio ambiente. La mayoría de los fallos operacionales son previstos en la etapa de diseño de un proceso mediante la aplicación de técnicas de Análisis de Riesgos y de Operabilidad (HAZOP). Sin embargo, existe la probabilidad de que pueda originarse un fallo en una planta en operación. Por esta razón, es de suma importancia que una planta pueda detectar y diagnosticar fallos en el proceso y tomar las medidas correctoras adecuadas para mitigar los efectos del fallo y evitar lamentables consecuencias. Es entonces también importante el mantenimiento preventivo para aumentar la seguridad y prevenir la ocurrencia de fallos. La diagnosis de fallos ha sido abordada tanto con modelos analíticos como con modelos basados en datos y usando varios tipos de técnicas y algoritmos. Sin embargo, hasta ahora no existe la propuesta de un sistema general de seguridad en planta que combine detección y diagnosis de fallos ya sea registrados o no registrados anteriormente. Menos aún se han reportado metodologías que puedan ser automatizadas e implementadas en la práctica real. Con la finalidad de abordar el problema de la seguridad en plantas químicas, esta tesis propone un sistema general para la detección y diagnosis de fallos capaz de implementarse de forma automatizada en cualquier industria. El principal requerimiento para la construcción de este sistema es la existencia de datos históricos de planta sin previo filtrado. En este sentido, diferentes métodos basados en datos son aplicados como métodos de diagnosis de fallos, principalmente aquellos importados del campo de “Aprendizaje Automático”. Estas técnicas de aprendizaje han resultado ser capaces de detectar y diagnosticar no sólo los fallos modelados o “aprendidos”, sino también nuevos fallos no incluidos en los modelos de diagnosis. Aunado a esto, algunas técnicas de mantenimiento basadas en riesgo (RBM) que son ampliamente usadas en la industria petroquímica, son también propuestas para su aplicación en el resto de sectores industriales como parte del mantenimiento preventivo. En conclusión, se propone implementar en un futuro no lejano un programa general de seguridad de planta que incluya el sistema de detección y diagnosis de fallos propuesto junto con un adecuado programa de mantenimiento preventivo. Desglosando el contenido de la tesis, el capítulo uno presenta una introducción general al tema de esta tesis, así como también la motivación generada para su desarrollo y el alcance delimitado. El capítulo dos expone el estado del arte de las áreas relacionadas al tema de tesis. De esta forma, los métodos de detección y diagnosis de fallos encontrados en la literatura son examinados en este capítulo. Asimismo, se propone una taxonomía de los métodos de diagnosis que unifica las clasificaciones propuestas en el área de Inteligencia Artificial y de Ingeniería de procesos. En consecuencia, se examina también la evaluación del performance de los métodos de diagnosis en la literatura. Además, en este capítulo se revisa y reporta el estado del arte correspondiente al “Análisis de Riesgos” y a la “Gestión del Mantenimiento” como técnicas complementarias para la toma de medidas correctoras y preventivas. Por último se abordan los casos de estudio considerados como puntos de referencia en el campo de investigación para la aplicación del sistema propuesto. La tercera parte incluye el capítulo siete, el cual constituye el corazón de la tesis. En este capítulo se presenta el esquema o sistema general de diagnosis de fallos propuesto. El sistema es dividido en tres partes: construcción de los modelos de diagnosis, validación de los modelos y aplicación on-line. Además incluye un modulo de detección de fallos previo a la diagnosis y una metodología de detección de anomalías para la detección de nuevos fallos. Por último, de este sistema se desglosan varias metodologías para procesos continuos y por lote. La cuarta parte de esta tesis presenta la validación de las metodologías propuestas. Específicamente, el capítulo ocho presenta la validación de las metodologías propuestas para su aplicación en procesos continuos y el capítulo nueve presenta la validación de las metodologías correspondientes a los procesos por lote. El capítulo diez valida la metodología de detección de anomalías en procesos por lote reales. Primero es aplicada a un intercambiador de calor escala laboratorio y después su aplicación es escalada a un proceso Foto-Fenton de planta piloto, lo cual corrobora el potencial y éxito de la metodología en la práctica real. Finalmente, la quinta parte de esta tesis, compuesta por el capítulo once, es dedicada a presentar y reafirmar las conclusiones finales y las principales contribuciones de la tesis. Además, se plantean las líneas de investigación futuras y se lista el trabajo desarrollado y presentado durante el periodo de investigación.

Vacuum technology is ubiquitous in the high tech industries and scientific endeavors. Since vacuum pumps are critical to operation, semiconductor manufacturers desire reliable operations, ability to schedule downtime, and less costly maintenance services. To better cope with difficult maintenance issues, interests in novel fault diagnosis techniques are growing. This study concerns model based fault diagnosis and isolation (MB-FDI) of dry vacuum pumps in the semiconductor industry. Faults alter normal operation of a vacuum pump resulting in performance deviations, discovered by measurements. Simulations using an appropriate mathematical model with suitably chosen parameters can mimic faulty behavior. This research focuses on the construction of a detailed multi-stage dry vacuum pump model for MB-FDI, and the development of a simple and efficient FDI method to analyze common incipient faults such as particulate deposition and gas leak inside the pump. The pump model features 0-D thermo-fluid dynamics, scalable geometric representations of Roots blower, claw pumps and inter-stage port interfaces, a unified pipe model seamlessly connecting from free molecular to turbulent regimes, sophisticated internal leakage model considering true pump geometry and tribological aspects, and systematic assembly of a multi-stage configuration using single stage pump models. Design of a simple FDI technique for the dry vacuum pump includes staged fault simulations using faulty pump models, parametric study of faulty pump behaviors, and design of a health indicator based on classification. The main research contributions include the developments of an accurate multi-stage dry pump model with many features not found in existing pump models, and the design of a simple MB-FDI technique to detect and isolate the common faults found in dry vacuum pumps. The proposed dry pump model can pave the way for the future development of advanced MB-FDI methods, also performance improvement of existing dry vacuum pumps. The proposed fault classification charts can serve as a quick guideline for vacuum pump manufactures to isolate roots causes from faulty symptoms. / text

The work presented in the thesis concerns the design and development of a novel thruster fault diagnosis and accommodation system (PDAS) for overactuated, open-frame underwater vehicles. The remotely operated vehicles (ROVs) considered in this thesis have four thrusters for motion in the horizontal plane with three controllable degrees of freedom (DoF). Due to the redundancy resulting from this configuration, for the case of a partial fault or a total fault in a single thruster it is possible to reallocate control among operable thrusters in order that the ROV pilot is able to maintain control of the faulty ROV and to continue with missions. The proposed PDAS consists of two subsystems: a fault diagnosis subsystem (FDS) and a fault accommodation subsystem (FAS). The FDS uses fault detector units to monitor thruster states. Robust and reliable interrogation of thruster states, and subsequent identification of faults, is accomplished using methods based on the integration of selforganising maps and fuzzy logic clustering. The FAS uses information provided by the FDS to perform an appropriate redistribution of thruster demands in order to accommodate faults. The FAS uses a hybrid approach for control allocation, which integrates the pseudoinverse method and the fixed-point iterations method. A control energy cost function is used as the optimisation criteria. In fault-free and faulty cases the FAS finds the optimal solution, which minimises this criteria. The concept of feasible region is developed in order to visualise thruster velocity saturation bounds. The PDAS provides a dynamic update of saturation bounds using a complex three-dimensional visualisation of the feasible region (attainable command set), such that the ROV pilot is informed with the effects of thruster fault accommodation, incorporated in the new shape of the attainable command set. In this way the ROV pilot can easy adapt to newly created changes and continue the mission in the presence of a fault. The prototype of the PDAS was developed in the MATLAB environment as a Simulink model, which includes a nonlinear model of an ROV with 6 DOF, propulsion system and a hand control unit. The hand control unit was simulated in hardware using a joystick as input device to generate command signals. Different fault conditions are simulated in order to investigate the performance of the PDAS. A virtual underwater world was developed, which enabled tuning, testing and evaluation of the PDAS using simulations of two underwater vehicles (FALCON, Seaeye Marine Ltd. and URIS, University of Girona) in a 'realistic' underwater environment. The performance of the PDAS was demonstrated and evaluated via tank trials of the FALCON ROV in QinetiQ Ocean Basin Tank at Haslar, where the existing control software was enhanced with the PDAS algorithm. The results of real-world experiments confirmed the effectiveness of the PDAS in maintaining vehicle manoeuvrability and in preserving the vehicle mission in the presence of thruster faults.