This dissertation investigates the effectiveness of a two stage fault identification methodology for rotating machines operating under non-stationary conditions with the use of a single vibration transducer. The proposed methodology transforms the machine vibration signal into a discrepancy signal by means of smart feature selection and statistical models. The discrepancy signal indicates the angular position and relative magnitude of irregular signal patterns which are assumed to be indicative of gear faults. The discrepancy signal is also independent of healthy vibration components, such as the meshing frequency, and effects of fluctuating operating conditions. The use of the discrepancy signal significantly reduces the complexity of fault detection and diagnosis. The first stage of the methodology involves extracting smart instantaneous operating condition specific features, while the second stage requires extracting smart instantaneous fault sensitive features. The instantaneous operating condition features are extracted from the coefficients of the low frequency region of the STFT of the vibration signal, since they are sensitive to operating condition changes and robust to the presence of faults. Then the sequence of operating conditions are classified using a hidden Markov model (HMM). The instantaneous fault features are then extracted from the coefficients in the wavelet packet transform (WPT) around the natural frequencies of the gearbox. These features are the converse to the operating condition features,since they are sensitive to the presence of faults and robust to the fluctuating operating conditions. The instantaneous fault features are sent to a set of Gaussian mixture models (GMMs), one GMM for each identified operating condition which enables the instantaneous fault features to be evaluated with respect to their operating condition. The GMMs generate a discrepancy signal, in the angular domain, from which gear faults may be detected and diagnosed by means of simple analysis techniques. The proposed methodology is validated using experimental data from an accelerated life test of a gearbox operated under fluctuating load and speed conditions. / Dissertation (MEng)--University of Pretoria, 2015. / Mechanical and Aeronautical Engineering / Unrestricted

The majority of condition monitoring techniques employed today consider the acquisitioning and analysis of structural responses as a means of profiling machine condition and performing fault detection. Modern research and newer technologies are driving towards non-contact and non-invasive methods for better machine characterisation. In particular, unshrouded rotors which are exposed to a full field of fluid interaction such as helicopter rotors and wind turbines, amongst others, benefit from such an approach. Current literature lacks investigations into the monitoring and detection of anomalous conditions using fluid dynamic behaviour. This is interesting when one considers that rotors of this nature are typically slender, implying that their structural behaviour is likely to be dependent on their aerodynamic behaviour and vice versa. This study sets out to investigate whether a seeded rotor fault can be inferred from the flow field. Studies of this nature have the potential to further a branch of condition monitoring techniques. It is envisaged that successful detection of rotor anomalies from the flow field will aid in better distinction between mass and aerodynamic imbalances experienced by rotor systems. Furthermore, the eventual goal is to better describe the adjustments made to helicopter rotor systems when performing rotor track and balance procedures. Time-dependent fluid dynamic data is numerically simulated around a helicopter tail rotor blade using URANS CFD with the OpenFOAM software package. Pressures are probed at locations in the field of the rotor and compared to results attained in an experimental investigation where good correlation is seen between the results. A blade is modelled with a seeded fault in the form of a single blade out of plane by 4°. Comparisons are drawn between the blade in its ‘healthy’ and ‘faulty’ configuration. It is observed that the fault can be detected by deviations in the amplitudes of the pressure signals for a single revolution at the probed locations in the field. These deviations manifest as increases in the frequency spectrum at frequencies equivalent to the rotational rate (1 per revolution frequencies). The results described are assessed for their fidelity when the pressure is probed at different locations in the domain of the rotor. Deviations in the pressure profiles over the surface of the blades are also seen for the asymmetric rotor configuration but may prove too sensitive for practical application. / Dissertation (MEng)--University of Pretoria, 2019. / Mechanical and Aeronautical Engineering / MEng / Unrestricted

The objective of this thesis is to identify, which hazards and failures in operation process will affect Reliability, Availability, Maintainability and Safety of floating offshore structures. The focus is on Dynamic Positioning (DP) system that has the responsibility of keeping the offshore structure in the upright position operation. DP system is one of the most critical subsystems on these types of structures in terms of safety of operation and failure risk costs. Reliability of the system in this thesis has been analyzed in qualitative and quantitativeb methods. In qualitative method to find the effective parameters on the reliability of the DPb system, Reliability Centered Maintenance (RCM ) and its application as a main tool have been used. To achieve the aim it has been tried to define the events and accidents which could be generated by the identified hazards then tried to determine the consequences of the realized accidents. In this step three categories are taken in to account including, safety, operation, and equipment. Next phase should be concentrated on considering and analyzing the relevant processes and the root causes which result in the identified hazard. After clarifying all probable root causes it has been tried to prioritize the root causes and specifying the necessary preventive actions. The aim of this step is either decreasing the occurrence of root causes or increasing the detectability of hazards. In the last part quantitative method has been used to measure the amounts of Reliability, Availability and Maintainability of the system, based on MTBF and MTTR of different components of the system and it has been tried to present the solutions to improve system reliability based on components RCM tables. Further, assuming DP system as human- machine system safety assessment has been included to indicate human factors in the reliability of the system beside probable failure of the components of the system.

The infrastructure of cyber-physical systems (CPS) is based on a meta-concept of cybermanufacturing systems (CMS) that synchronizes the Industrial Internet of Things (IIoTs), Cloud Computing, Industrial Control Systems (ICSs), and Big Data analytics in manufacturing operations. Artificial Intelligence (AI) can be incorporated to make intelligent decisions in the day-to-day operations of CMS. Cyberattack spaces in AI-based cybermanufacturing operations pose significant challenges, including unauthorized modification of systems, loss of historical data, destructive malware, software malfunctioning, etc. However, a cybersecurity framework can be implemented to prevent unauthorized access, theft, damage, or other harmful attacks on electronic equipment, networks, and sensitive data. The five main cybersecurity framework steps are divided into procedures and countermeasure efforts, including identifying, protecting, detecting, responding, and recovering. Given the major challenges in AI-enabled cybermanufacturing systems, three research objectives are proposed in this dissertation by incorporating cybersecurity frameworks. The first research aims to detect the in-situ additive manufacturing (AM) process authentication problem using high-volume video streaming data. A side-channel monitoring approach based on an in-situ optical imaging system is established, and a tensor-based layer-wise texture descriptor is constructed to describe the observed printing path. Subsequently, multilinear principal component analysis (MPCA) is leveraged to reduce the dimension of the tensor-based texture descriptor, and low-dimensional features can be extracted for detecting attack-induced alterations. The second research work seeks to address the high-volume data stream problems in multi-channel sensor fusion for diverse bearing fault diagnosis. This second approach proposes a new multi-channel sensor fusion method by integrating acoustics and vibration signals with different sampling rates and limited training data. The frequency-domain tensor is decomposed by MPCA, resulting in low-dimensional process features for diverse bearing fault diagnosis by incorporating a Neural Network classifier. By linking the second proposed method, the third research endeavor is aligned to recovery systems of multi-channel sensing signals when a substantial amount of missing data exists due to sensor malfunction or transmission issues. This study has leveraged a fully Bayesian CANDECOMP/PARAFAC (FBCP) factorization method that enables to capture of multi-linear interaction (channels × signals) among latent factors of sensor signals and imputes missing entries based on observed signals.

Induction motors (IM) find widespread use in modern industry and for this reason they have been subject to a significant amount of research interest in recent times. One particular aspect of this research is the fault detection and diagnosis (FDD) of induction motors for use in a condition based maintenance (CBM) strategy; by effectively tracking the condition of the motor, maintenance action need only be carried out when necessary. This type of maintenance strategy minimises maintenance costs and unplanned downtime. The benefits of an effective FDD for IM is clear and there have been numerous studies in this area but few which consider the problem in a practical sense with the aim of developing a single system that can be used to monitor motor condition under a range of different conditions, with different motor specifications and loads. This thesis aims to address some of these problems by developing a general FDD system for induction motor. The solution of this problem involved the development and testing of a new approach; the adaptive mixed-residual approach (AMRA). The main aim of the AMRA system is to avoid the vast majority of unplanned failures of the machine and therefore as opposed to tackling a single induction motor fault, the system is developed to detect all four of the most statistically prevalent induction motor fault types; rotor fault, stator fault, air-gap fault and bearing fault. The mixed-residual fault detection algorithm is used to detect these fault types which includes a combination of spectral and model-based techniques coupled with particle swarm optimisation (PSO) for automatic identification of motor parameters. The AMRA residuals are analysed by a fuzzy-logic classifier and the system requires only current and voltage inputs to operate. Validation results indicate that the system performs well under a range of load torques and different coupling methods proving it to have significant potential for use in industrial applications. / The full-text was made available at the end of the embargo period on 29th Sept 2017.

Skanska driver cirka 30 asfaltverk i Sverige. Något alla dessa verk har gemensamt är att driftsäkerheten är viktig då ett driftstopp på asfaltverket kan leda till höga kostnader ifallbeläggningsarbetet blir stillastående. Syftet med examensarbetet är att undersöka olika teknikersom används inom tillståndsbaserat underhåll och hur de kan appliceras på ett av Skanskas asfaltverk.Datainsamling skedde genom litteratursökning, dokumentstudier, observationer och intervjuer. Resultatet av litteratursökningen visade att det finns flera tekniker inom tillståndsbaserat underhåll. Några av de vanligaste teknikerna är mätning av vibrationer, temperatur och ultraljud. Alla inrapporterade driftstörning på Skanskas asfaltverk i region väst under 2022 kategoriserades efter vilken utrustning som orsakat stoppet. Analysen av störningarna visade att siktutrustningen stod för den största procentuella andelen av driftstoppstimmarna. Vid observationer och intervjuer framkom att haveri av sikten ofta leder till långa driftstopp och att det är svårt att upptäcka felen innan de inträffar. För att undersöka sikten på komponentnivå genomfördes en FEMA som visade att sprickbildning i plåtar, motorhaveri och felmonterade såll är de mest kritiska felorsakerna. För att mäta dessa fel valdes vibrationsmätning då denna metod ansågs ha störst sannolikhet att upptäcka de kritiska felen. Tre företag som arbetar med vibrationsmätning kontaktades för att diskutera olika lösningskoncept. Utifrån dessa tre lösningsförslag rekommenderades två koncept för fortsatt arbete. Det som skiljer lösningarna från varandra är att den ena innebär en större investering ekonomiskt och kompetensmässigt men ger möjlighet till mer detaljerad information. / Skanska operates approximately 30 asphalt plants in Sweden. One thing all these plants have in common is that operational reliability is important, as unplanned downtime at the asphalt plant can lead to high costs if asphalt paving work is interrupted. The purpose of the thesis is to investigate various techniques used in condition-based maintenance and how they can be applied to one of Skanska's asphalt plants.Data was collected through literature searches, document studies, observations, and interviews.The literature search showed that there are many technologies used in condition-basedmaintenance. Some of the most common techniques are vibration measurement, temperature measurement, and ultrasonic testing. All reported equipment failures at Skanska's asphalt plants in the west region in 2022 were categorized by the equipment that caused the downtime. Analysis of the failures showed that the screening equipment accounted for the largest percentage of downtime. Observations and interviews revealed that shutdowns of the screens are often long and that it is difficult to detect faults before they occur.To investigate the screen at the component level, a FMEA was carried out, which showed that cracking in plates, motor failures, and incorrectly installed screens are the most critical causes of failure. Vibration measurement was chosen to measure these faults as this method was considered to have the highest probability of detecting the critical faults. Three companies working with vibration measurement were contacted to discuss different solution concepts. Based on these three proposals, two concepts were recommended for further study. What differentiates the solutions from each other is that one entails a greater investment both financially and in terms of expertise but provides the opportunity for more detailed information.

The hard equation of railway safety versus the high commercial profits can only be achieved through the use of new inspection methods supported by modern technologies. The track and its components can have different types of troubles, such as rail surface defects, broken sleepers, missing fasteners, and irregular ballast levels. Each component of the track infrastructure plays a significant role, where the failure or the absence of any of them can pave the way to undesired situations. The rail is designed to carry and direct the train, the sleepers are meant to maintain the level of the rail, and the ballast mission is to keep all components floating on the surface of the ground. The fasteners are used to fasten the rail to the sleepers, and therefore too many missing fasteners can lead to sever unsteady tracks, which can, in turn, result in derailment. Therefore, there is a high demand for advanced inspection methods to monitor the railway track and its components continuously. The presence of such advanced inspection models would help the railway industry avoid obstacles such as high operation and maintenance costs, dangerous accidents, and uncomfortable passenger's experience.   This master thesis aims to present an efficient method to classify the track and its components by combining image processing techniques and deep learning algorithms. This method was able to detect the missing fasteners in the set of images captured by a line camera, continuously monitoring the rail and its associated fasteners. The experimental results obtained in this thesis showed that the proposed method is efficient and robust for detecting the track and its components in complex environments. The thesis also discusses the idea of building one complete model that can process and classify all track components at once. The image processing technique was employed to extract different components of the track, individually: fasteners, rail, ballast, and sleepers. The model was trained and used to classify the state of the fasteners. Classification of other components of the track will be a part of the future work.

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Orientador: Aparecido Carlos Gonçalves / Resumo: Pares engrenados são elementos de transmissão de potência amplamente utilizados em máquinas e equipamentos, todavia as falhas catastróficas desses componentes são comuns e dispendiosas. A análise de vibrações está entre as técnicas de diagnóstico de defeitos incipientes utilizadas em manutenção preditiva, posto que a presença de uma falha altera o comportamento dinâmico do sistema e o estado de degradação pode ser detectado pelo monitoramento dos sinais de vibração. Na indústria atual, onde as aquisições de dados, tanto para controle de processos, quanto para o monitoramento das condições de integridade de equipamentos, são realizadas em tempo real, faz-se necessário o desenvolvimento de métodos que aumentem a confiabilidade das tomadas de decisões em relação à identificação, localização e prognóstico de falhas. O objetivo deste trabalho é desenvolver um modelo matemático de par de engrenagens que auxilie no monitoramento da condição e validar o modelo dinâmico com dados de vibração de um multiplicador de velocidades obtidos experimentalmente. Para tanto, foi elaborada uma metodologia baseada no modelo dinâmico de par engrenado com 6 graus de liberdade para simulação de sinais de vibração; nesse modelo, inclui-se erros geométricos no perfil do dente; de maneira analítica, simula-se uma a trinca do dente de uma das engrenagens que ocasiona a queda de rigidez em função do tempo; desenvolveu-se também um experimento com um multiplicador de velocidades; e, por fim, algumas técnic... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Spur gears are transmission power elements widely used in machinery, however catastrophic failures of this components are just as common and onerous. Vibration analysis is a technique, in among of others, that can be used in diagnostics of incipient damages, common in predictive maintenance, because they change the dynamic behavior of the mechanical system, and the degradation state can be detected by vibration signal or noise. In the current industry production, in which real-time data acquisition - whether for processes control, or for health condition monitoring of equipment - is the reality, it is necessary to develop auxiliary methods that provide high reliability to identification, localization and failure prognostics. In this work, the main objective is to provide a spur gears’ model-based methodology for condition-monitoring and to validate a dynamic model with experimental vibration data of a gearbox. Hence, a dynamic model of spur meshing gears was developed considering a 6 degrees of freedom and time-varying meshing stiffness to simulate vibrations signals; a tooth profile error was also included; in this analytical model, a straight crack was simulated by reducing the meshing stiffness in a tooth; experiments with a gearbox experimental set were run; and, some signal processing was apllied in the vibration data. The results allowed the model validation with the comparison between simulate and experimental signals, in time-domain and frequency-domain / Mestre

This thesis deals with the reliability and maintenance of structures that are damaged by shocks arriving randomly in time. The degradation is modeled as a cumulative stochastic point process. Previous studies mostly adopted expected cost rate criterion for optimizing the maintenance policies, which ignores practical implications of discounting of maintenance cost over the life cycle of the system.Therefore, detailed analysis of expected discounted cost criterion has been done, which provides a more realistic basis for optimizing the maintenance. Examples of maintenance policies combining preventive maintenance with age- based replacement are analyzed. Derivation for general cases involving preventive maintenance damage level have been discussed. Special cases are also considered.