This dissertation develops a new methodology and associated solution tools to achieve optimal operations and maintenance strategies for wind turbines, helping reduce operational costs and enhance the marketability of wind generation. The integrated framework proposed includes two optimization models for enabling decision support capability, and one discrete event-based simulation model that characterizes the dynamic operations of wind power systems. The problems in the optimization models are formulated as a partially observed Markov decision process to determine an optimal action based on a wind turbine's health status and the stochastic weather conditions. The rst optimization model uses homogeneous parameters with an assumption of stationary weather characteristics over the decision horizon. We derive a set of closed-form expressions for the optimal policy and explore the policy's monotonicity. The second model allows time-varying weather conditions and other practical aspects. Consequently, the resulting strategy are season-dependent. The model is solved using a backward dynamic programming method. The bene ts of the optimal policy are highlighted via a case study that is based upon eld data from the literature and industry. We nd that the optimal policy provides options for cost-e ective actions, because it can be adapted to a variety of operating conditions. Our discrete event-based simulation model incorporates critical components, such as a wind turbine degradation model, power generation model, wind speed model, and maintenance model. We provide practical insights gained by examining di erent maintenance strategies. To the best of our knowledge, our simulation model is the rst discrete-event simulation model for wind farm operations. Last, we present the integration framework, which incorporates the optimization results in the simulation model. Preliminary results reveal that the integrated model has the potential to provide practical guidelines that can reduce the operation costs as well as enhance the marketability of wind energy.

In order to stay competitive, it is necessary for companies to continuously increase the effectiveness and efficiency of their production processes. High availability has, thus, increased in importance. Therefore, maintenance has gained in importance as a support function for ensuring, e.g., quality products and on-time deliveries. Maintenance, though, is a costly support function. It has been reported that as much as 70% of the total production cost can be spent on maintenance. Further, as much as one-third of the cost of maintenance is incurred unnecessarily due to bad planning, overtime cost, limited or misused preventive maintenance, and so on. In so, condition based maintenance is introduced as one solution for a more effective maintenance. In condition based maintenance, critical item characteristics are monitored in order to gain early indications of an incipient failure. Research, though, has shown that condition based maintenance has not been implemented on a wide basis. Therefore, the purpose of this research is to investigate how a condition based maintenance approach can be implemented in an industrial setting, and to develop a method that can assist companies in their implementation efforts. Further, the research has been divided in three research questions. They focus on: constituents of a condition based maintenance approach, decision-making prior implementation of condition based maintenance, and finally, the implementation of condition based maintenance in a company. By using a systems approach and a case study process, how condition based maintenance can be implemented as a routine has been investigated. The result is an implementation method in which four suggested phases are presented. The method starts with a feasibility test. It then continues with an analysis phase, an implementation phase, and an assessment phase. The conclusions can be summarized as follows: implementing condition based maintenance consists of many general enabling factors, including management support, education and training, good communication, and motivation etc.

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.

The main focus of this study is to improve the profitability, availability and condition monitoring of Liquefied Natural Gas (LNG) Floating Production Storage and Offloading platforms (FPSOs). Propane pre-cooled, mixed refrigerant (C3MR) liquefaction is the key process in the production of LNG on FPSOs. LNG liquefaction system equipment has the highest failure rates among the other FPSO equipment, and thus the highest maintenance cost. Improvements in the profitability, availability and condition monitoring were made in two ways: firstly, by making recommendations for the use of redundancy in order to improve system reliability (and hence availability); and secondly, by developing an effective condition-monitoring algorithm that can be used as part of a condition-based maintenance system. C3MR liquefaction system reliability modelling was undertaken using the time-dependent Markov approach. Four different system options were studied, with varying degrees of redundancy. The results of the reliability analysis indicated that the introduction of a standby liquefaction system could be the best option for liquefaction plants in terms of reliability, availability and profitability; this is because the annual profits of medium-sized FPSOs (3MTPA) were estimated to increase by approximately US$296 million, rising from about US$1,190 million to US$1,485.98 million, if redundancy were implemented. The cost-benefit analysis results were based on the average LNG prices (US$500/ton) in 2013 and 2014. Typically, centrifugal turbines, compressors and blowers are the main items of equipment in LNG liquefaction plants. Because centrifugal equipment tops the FPSO equipment failure list, a Condition Monitoring (CM) system for such equipment was proposed and tested to reduce maintenance and shutdown costs, and also to reduce flaring. The proposed CM system was based on a novel FFT-based segmentation, feature selection and fault identification algorithm. A 20 HP industrial air compressor system with a rotational speed of 15,650 RPM was utilised to experimentally emulate five different typical centrifugal equipment machine conditions in the laboratory; this involved training and testing the proposed algorithm with a total of 105 datasets. The fault diagnosis performance of the algorithm was compared with other methods, namely standard FFT classifiers and Neural Network. A sensitivity analysis was performed in order to determine the effect of the time length and position of the signals on the diagnostic performance of the proposed fault identification algorithm. The algorithm was also checked for its ability to identify machine degradation using datasets for which the algorithm was not trained. Moreover, a characterisation table that prioritises the different fault detection techniques and signal features for the diagnosis of centrifugal equipment faults, was introduced to determine the best fault identification technique and signal feature. The results suggested that the proposed automated feature selection and fault identification algorithm is effective and competitive as it yielded a fault identification performance of 100% in 3.5 seconds only in comparison to 57.2 seconds for NN. The sensitivity analysis showed that the algorithm is robust as its fault identification performance was affected by neither the time length nor the position of signals. The characterisation study demonstrated the effectiveness of the AE spectral feature technique over the fault identification techniques and signal features tested in the course of diagnosing centrifugal equipment faults. Moreover, the algorithm performed well in the identification of machine degradation. In summary, the results of this study indicate that the proposed two-pronged approach has the potential to yield a highly reliable LNG liquefaction system with significantly improved availability and profitability profiles.

The growth of global competition has led to remarkable changes in the way manufacturing companies operate. These changes have affected maintenance and made its role even more crucial for business success. To remain competitive, manufacturing companies must continuously increase the effectiveness and efficiency of their production processes. Furthermore, the introduction of lean manufacturing has increased concerns regarding equipment availability and, therefore, the demand for effective maintenance. That maintenance is becoming more important for the manufacturing industry is evident in current discussions on national industrialization agendas. Digitalization, the industrial internet of things (IoT) and their connections to sustainable production are identified as key enablers for increasing the number of jobs in industry. Agendas such as “Industry 4.0” in Germany and “Smart Industry” in Sweden are promoting the connection of physical items such as sensors, devices and enterprise assets, both to each other and to the internet. Machines, systems, manufactured parts and humans will be closely interlinked to collaborative actions. Every physical object will formulate a cyber-physical system (CPS), and it will constantly be linked to its digital fingerprint and to intensive connection with the surrounding CPSs of its on-going processes. That said, despite the increasing demand for reliable production equipment, few manufacturing companies pursue the development of strategic maintenance. Moreover, traditional maintenance strategies, such as corrective maintenance, are no longer sufficient to satisfy industrial needs, such as reducing failures and degradations of manufacturing systems to the greatest possible extent. The concept of maintenance has evolved over the last few decades from a corrective approach (maintenance actions after a failure) to a preventive approach (maintenance actions to prevent the failure). Strategies and concepts such as condition based maintenance (CBM) have thus evolved to support this ideal outcome. CBM is a set of maintenance actions based on the real-time or near real-time assessment of equipment conditions, which is obtained from embedded sensors and/or external tests and measurements, taken by portable equipment and/or subjective condition monitoring. CBM is increasingly recognized as the most efficient strategy for performing maintenance in a wide variety of industries. However, the practical implementation of advanced maintenance technologies, such as CBM, is relatively limited in the manufacturing industry. Based on the discussion above, the objective of this research is to provide frameworks and guidelines to support the development and implementation of condition based maintenance in manufacturing companies.  This thesis will begin with an overall analysis of maintenance management to identify factors needed to strategically manage production maintenance. It will continue with a focus on CBM to illustrate how CBM could be valued in manufacturing companies and what the influencing factors to implement CBM are. The data were collected through case studies, mainly at one major automotive manufacturing site in Sweden. The bulk of the data was collected during a pilot CBM implementation project. Following the findings from these efforts, a formulated maintenance strategy is developed and presented, and factors to evaluate CBM cost effectiveness are assessed. These factors indicate the benefits of CBM, mostly with regard to reducing the probability of experiencing maximal damage to production equipment and reducing production losses, particularly at high production volumes. Furthermore, a process of CBM implementation is presented. Some of the main elements in the process are the selection of the components to be monitored, the techniques and technologies for condition monitoring and their installation and, finally, the analysis of the results of condition monitoring. Furthermore, CBM of machine tools is presented and discussed in this thesis, focusing on the use of vibration monitoring technique to monitor the condition of machine tool spindle units. / INNOFACTURE - innovative manufacturing development

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 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.