In the context of Industry 4.0, the prominence of robotics has grown significantly, leading to a pressing need for advanced monitoring techniques. This thesis explores the potential role of Mixed Reality (MR) in robot condition monitoring through an exhaustive literature review of 138 selected studies. The investigation showed prevalent methods in robot condition monitoring, such as Fault Detection and Diagnosis, Machine Learning Techniques, Signal-based Monitoring, Model-based Monitoring, and Real-time Monitoring. MR, while not yet abundant in this context, is emerging as a promising tool, especially for real-time data visualization, remote maintenance, and integration with other technologies. By visually representing data and predictions directly on the robot, MR can speed up the diagnostic process, improve safety, and promote remote collaboration. However, challenges such as integration with legacy systems, effective data management, and hardware limitations were identified. The research also observed trends, benefits, and challenges in the broader application of MR in industrial settings. While MR offers significant advantages, including enhanced visualization, improved efficiency, and cost savings, its full integration into the world of robot condition monitoring necessitates further research and iterative refinement. In essence, this thesis presents a balanced overview of the potential and challenges of MR in robot condition monitoring, setting the stage for future exploration in this burgeoning domain.

Press felts are highly critical components of the paper machine. A degraded press felt could lead to paper web breaks, which requires the paper machine to be restarted. Moreover, a degraded felt influences the quality of the paper, leading to paper disposal. Condition monitoring aims at minimising the risk of paper web breaks, unsatisfactory paper quality and other types of production loss while maximising the useful life of the press felts. However, installing a new condition monitoring system is expensive and the installation can be difficult to fit into the scheduled maintenance stops. This thesis investigates the possibility of using existing monitoring systems instead of installing a new one. Four possible approaches of monitoring the degradation of the press felts have been explored. The identified approaches of press felt monitoring were tested by using data acquired through existing monitoring systems of the paper machine PM2 at BillerudKo-rsnäs in Karlsborg, located in the north of Sweden. The first approach is based upon process parameters. This approach could, however, not be properly investigated due to a malfunctioning sensor. The second approach revolves around the natural frequencies of the felt and the frequency changes as the felt degrades. The remaining two approaches originates from the hypothesis that felt degradation could lead to impacts as the possibly uneven felt passes the rollers. One approach is to detect these possible impacts by using the time domain feature kurtosis. The other approach is to monitor the harmonics these impacts could lead to. Neither the natural frequency nor the kurtosis approach was deemed promising, partly based on the results of the analysed data but also due to intrinsic deficiencies of these approaches. The approach based on felt harmonics did, however, exhibit indications that it might be a feasible monitoring technique. The felt harmonics approach should be further investigated. Furthermore, a python program that can synchronise data from different sources was developed. This program enables degradation features to be extracted using machine learning algorithms. However, due to the lack of vibration data and labels of the current felt condition, machine learning was not applied. / Pressfiltar är ytterst kritiska komponenter i pappersmaskinen. En nedsliten pressfilt kan orsaka pappersbanbrott vilket innebär att pappersmaskinen måste startas om. En nedsliten filt kan också påverka papperskvaliteten vilket resulterar i att papper måste kasseras. Tillståndsövervakning är ett steg närmare att kunna optimera användandet av pressfiltarna, det vill säga maximera livstiden samtidigt som risken för oplanerade stopp minimeras. Att installera ett nytt tillståndsövervakningssystem kan dock vara dyrt och installationen kan vara svår att rymmas i de planerade underhållsstoppen. Detta masterarbete utreder möjligheten att använda existerande övervakningssytem istället för att installera ett nytt. Fyra möjliga angreppssätt för tillståndsövervakning av pressfilten utforskades. De identifierade övervakningsteknikerna testades genom att använda data från existerande övervakningssystem på pappersmaskinen PM2 hos BillerudKorsnäs i Karlsborg utanför Kalix. Det första angreppssättet baseras på processparametrar. Detta angreppssätt kunde dock ej utvärderas på grund av en defekt sensor. Det andra angreppssättet kretsar kring filtens egenfrekvenser och dessas förändring när filten slits ut. Återstående två angreppssätt har sitt ursprung i hypotesen att en försämrad filt kan ge upphov till slag när den eventuellt ojämna filten passerar valsarna. Ett angreppssätt är att detektera dessa eventuella vibrationer genom tidsdomänfunktionen kurtosis. Det andra angreppssättet som använts är att övervaka de övertoner som slagen kan leda till. Varken angreppssättet baserat på egenfrekvens eller det baserat på kurtosis bedömdes lovande. Detta delvis baserat på resultaten från analyserad data men också på grund av de inneboende bristerna för dessa två angreppssätt. Det angreppssätt som baseras på filtens övertoner visade däremot indikationer på att det kan utgöra en möjlig övervakningsteknik och detta angreppssätt bör därför utforskas vidare. Vidare utvecklades ett pythonprogram som kan synkronisera data från olika källor. Programmet möjliggör applicering av maskininlärningsalgoritmer. På grund av brist på vibrationsdata och klassificering av nuvarande filttillstånd applicerades dock inte maskininlärning. / NonStopp

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<p>Condition monitoring supported with artificial intelligence, cloud computing, and industrial internet of things (IIoT) technologies increases the feasibility of predictive maintenance (PdM). However, the cost of traditional sensors, data acquisition systems, and the information technology expert knowledge required to inform and implement PdM challenge the industry. This thesis proposes a hybrid condition monitoring system (CMS) architecture consisting of a distributed, low-cost IIoT-sensor solution. The CMS uses micro-electro-mechanical system (MEMS) microphones for data acquisition, edge computing for signal preprocessing, and cloud computing, including artificial neural networks (ANN) for higher-level information processing. The higher-level information processing includes condition detection and time-based prediction capabilities to inform PdM strategies. The system’s feasibility is validated using a testbed for reciprocating linear-motion axes.</p>

Axlebox bearings are one of the most critical components of a rail vehicle with regard to safety. An axlebox bearing that breaks during operation can be dangerous for the passengers and expensive for the operator. In-service failure of axlebox bearings has been the cause of many catastrophic accidents. Thus, it is of utmost importance to predict bearing failures as early as possible. This will increase reliability and safety of the vehicle as well as reduce the vehicle maintenance cost. Monitoring of roller bearings is an active research eld, and many methods have been proposed by other researchers. Many of these methods employ complex algorithms to make the most use of the given measurements. The algorithms often lack interpretability and have high computational costs, making them dicult to employ in an on-board system. This thesis proposes an interpretable and transparent algorithm that predicts bearing damages with high accuracy. Meanwhile, it tries to retain interpretability as much as possible. The algorithm is based on Empirical Mode Decomposition (EMD) and Singular Value Decomposition (SVD). These two techniques extract essential and meaningful information from the axlebox accelerations. The algorithm is benchmarked on two benchmark datasets, and the results are compared to the respective literature. Then the algorithm is employed on the railway axlebox acceleration measurements that were taken on an axlebox test bench available at SWJTU. The proposed algorithm can be extended to incorporate additional measurements of dierent types, e.g. sound or temperature measurements. The incorporation of other types of measurements will improve the performance of the algorithm even further. / Axelbox lager är en av de viktigaste komponenterna i ett järnvägsfordon när det berör säkerheten. Ett axelbox lager som havererar under drift kan vara farligt for passagerarna och även dyrt för operatören. Driftfel av lagren har varit orsaken till många katastrofala olyckor. Därför är det av yttersta vikt att förutsäga lagerfel så tidigt som möjligt. Detta ökar fordonets tillförlitlighet och säkerhet samt minskar underhållskostnaderna. Mycket forskning har utförts inom övervakning av rullager. Många metoder använder komplexa algoritmer för att maximalt utnyttja matningarna. Algoritmerna saknar ofta tolkbarhet och har höga beräkningskostnader, vilket gör dem svåra att använda i ett integrerat system. Denna avhandling kombinerar era metoder för databehandling och maskininlärning till en algoritm som kan förutsäga lagerskador med hög precision, samtidigt som tolkningsförmågan bibehalls. Bland andra välkända metoder sa använder algoritmen Empirical Mode Decomposition (EMD) och Singular Value Decomposition (SVD) för att extrahera väsentlig information for vibrationsmätningarna. Algoritmen testas sedan med tre olika vibrationsdatamängder, varav en mättes specikt med tanke på simulering av axelbox lager. Ett annat mål med algoritmen är att göra den tillämpad för ytterligare mätningar. Det bör vara möjligt att inkludera mätningar av olika slag, dvs ljud- eller temperaturmätningar, och därigenom förbättra resultaten. Detta skulle minska implementeringskostnaden avsevärt eftersom befintliga sensorer används för detta ändamål. I händelsen av att de föreslagna metoderna inte fungerar med nya mätningar är det även möjligt att integrera ytterligare funktioner i algoritmen.

The lack of research on the impact of component degradation on the surface quality of machine tool spindles is limited and the primary motivation for this research. It is common in the manufacturing industry to replace components even if they still have some Remaining Useful Life (RUL), resulting in an ineffective maintenance strategy. The primary objective of this thesis is to design and construct an Exchangeable Spindle Unit (ESU) test stand that aims at capturing the influence of the failure transition of components during machining and its effects on the quality of the surface. Current machine tools cannot be tested with extreme component degradation, especially the spindle, since the degrading elements can lead to permanent damage, and machine tools are expensive to repair. The ESU substitutes and decouples the machine tool spindle to investigate the influence of deteriorated components on the response so that the machine tool spindle does not take the degrading effects. Data-driven quality control is another essential factor which many industries try to implement in their production line. In a traditional manufacturing scenario, quality inspections are performed to check if the parameters measured are within the nominal standards at the end of a production line or between processes. A significant flaw in the traditional approach is its inability to map the degradation of components to quality. Condition monitoring techniques can resolve this problem and help identify defects early in production. This research focuses on two objectives. The first one aims at capturing the component degradation by artificially inducing imbalance into the ESU shaft and capturing the excitation behavior during machining with an end mill tool. Imbalance effects are quantified by adding mass onto the ESU spindle shaft. The varying effects of the mass are captured and characterized using vibration signals. The second objective is to establish a correlation between the surface quality of the machined part with the characterized vibrations signals by Bagged Ensemble Tree (BET) machine learning models. The results show a good correlation between the surface roughness and the accelerometer signals. A comparison study between a balanced and imbalanced spindle along with its resultant surface quality is presented in this research. / Bristen på forskning om inverkan av komponentnedbrytning på ytkvaliteten hos verktygsmaskiner är begränsad och den primära motivationen för denna forskning. Det är vanligt inom tillverkningsindustrin att byta ut komponenter även om de fortfarande har en viss återstående livslängd, vilket resulterar i en ineffektiv underhållsstrategi. Det primära syftet med denna avhandling är att designa och konstruera en utbytbar spindelenhetstestsats som syftar till att fånga inverkan av komponentbrottsövergång under bearbetning och dess effekter på ytkvaliteten. Nuvarande verktygsmaskiner kan inte testas med extrem komponentnedbrytning, speciellt spindeln, eftersom de nedbrytande elementen kan leda till permanenta skador och verktygsmaskiner är dyra att reparera. Den utbytbara spindelenheten ersätter och kopplar bort verktygsmaskinens spindel för att undersöka effekten av försämrade komponenter på responsen så att verktygsmaskinens spindel inte absorberar de nedbrytande effekterna. Datadriven kvalitetskontroll är en annan viktig faktor som många industrier försöker implementera i sin produktionslinje. I ett traditionellt tillverkningsscenario utförs kvalitetsinspektioner för att kontrollera om de uppmätta parametrarna ligger inom de nominella normerna i slutet av en produktionslinje eller mellan processer. En betydande brist med det traditionella tillvägagångssättet är dess oförmåga att kartlägga komponenternas försämring till kvalitet. Tillståndsövervakningstekniker kan lösa detta problem och hjälpa till att identifiera defekter tidigt i produktionsprocessen. Denna forskning fokuserar på två mål. Den första syftar till att fånga komponentnedbrytning genom att artificiellt inducera obalans i axeln på den utbytbara spindelenheten och fånga excitationsbeteendet under bearbetning med ett fräsverktyg. Obalanseffekter kvantifieras genom att tillföra massa till spindelaxeln på den utbytbara spindelenheten. Massans varierande effekter fångas upp och karakteriseras med hjälp av vibrationssignaler. Det andra målet är att etablera en korrelation mellan ytkvaliteten hos den bearbetade delen med de karakteriserade vibrationssignalerna från Bagged Ensemble Tree maskininlärningsmodeller. Resultaten visar en god korrelation mellan ytjämnheten och accelerometerns signaler. En jämförande studie mellan en balanserad och obalanserad spindel tillsammans med dess resulterande ytkvalitet presenteras i denna forskning.

A non-invasive, on-line method for detection of mechanical (rotor, bearings eccentricity) and stator winding faults in a 3-phase induction motors from observation of motor line current supply input. The main aim is to avoid the consequence of unexpected failure of critical equipment which results in extended process shutdown, costly machinery repair, and health and safety problems. This thesis looks into the possibility of utilizing machine learning techniques in the field of condition monitoring of electromechanical systems. Induction motors are chosen as an example for such application. Electrical motors play a vital role in our everyday life. Induction motors are kept in operation through monitoring its condition in a continuous manner in order to minimise their off times. The author proposes a model free sensor-less monitoring system, where the only monitored signal is the input to the induction motor. The thesis considers different methods available in literature for condition monitoring of induction motors and adopts a simple solution that is based on monitoring of the motor current. The method proposed use the feature extraction and Support Vector Machines (SVM) to set the limits for healthy and faulty data based on the statistical methods. After an extensive overview of the related literature and studies, the motor which is the virtual sensor in the drive system is analysed by considering its construction and principle of operation. The mathematical model of the motor is used for analysing the system. This is followed by laboratory testing of healthy motors and comparing their output signals with those of the same motors after being intentionally failed, concluding with the development of a full monitoring system. Finally, a monitoring system is proposed that can detect the presence of a fault in the monitored machine and diagnose the fault type and severity / Ministry of Higher Education, Libya; Switchgear & Instruments Ltd.

Detecting abnormal operating conditions, which will lead to faults developing later, has important economic implications for industries trying to meet their performance and production goals. It is unacceptable to wait for failures that have potential safety, environmental and financial consequences. Moving from a “reactive” strategy to a “proactive” strategy can improve critical equipment reliability and availability while constraining maintenance costs, reducing production deferrals, decreasing the need for spare parts. Once the fault initiates, predicting its progression and deterioration can enable timely interventions without risk to personnel safety or to equipment integrity. This work presents an online-integrated condition monitoring and prognostics framework that addresses the above issues holistically. The proposed framework aligns fully with ISO 17359:2011 and derives from the I-P and P-F curve. Depending upon the running state of machine with respect to its I-P and P-F curve an algorithm will do one of the following: (1) Predict the ideal behaviour and any departure from the normal operating envelope using a combination of Evolving Clustering Method (ECM), a normalised fuzzy weighted distance and tracking signal method. (2) Identify the cause of the departure through an automated diagnostics system using a modified version of ECM for classification. (3) Predict the short-term progression of fault using a modified version of the Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS), called here MDENFIS and a tracking signal method. (4) Predict the long term progression of fault (Prognostics) using a combination of Autoregressive Integrated Moving Average (ARIMA)- Empirical Mode Decomposition (EMD) for predicting the future input values and MDENFIS for predicting the long term progression of fault (output). The proposed model was tested and compared against other models in the literature using benchmarks and field data. This work demonstrates four noticeable improvements over previous methods: (1) Enhanced testing prediction accuracy, (2) comparable processing time if not better, (3) the ability to detect sudden changes in the process and finally (4) the ability to identify and isolate the problem source with high accuracy.

Many automotive components are made of Al-Si coated ultra-high strength boron steel (UHSS) and are produced by hot stamping process. In this process, the workpiece is heated to an austenitizing temperature and is then formed and quenched simultaneously between the tools to achieve the desired shape and high strength. During hot stamping process, friction and wear occur which affect formability and maintenance intervals for tool replacement and repair. To repair worn tools, metal is deposited by fusion welding technique. The tribological behaviour of repair welded tool steel sliding against Al-Si coated UHSS has not been studied in detail and there is a need to investigate if the modified tool surface will affect friction and wear. Hot stamping, similar to many manufacturing processes, is affected by the global mega trend of digitalization and Industry 4.0. To monitor the process and optimize the control and operation are the main aims. In view of this, tribological condition monitoring is a promising approach that can allow measurement of physical properties such as vibrations, temperatures, and acoustic emission to be coupled to the tribological response of the system. The aim is to monitor the hot stamping process and enable early detection of changes in friction and wear which can be used for e.g. optimized maintenance and minimized scrap. The aim of this M.Sc. thesis was to improve the robustness of hot forming processes by studying the tribological behaviour of repair welded tool steel sliding against Al-Si coated UHSS under conditions relevant for hot stamping. Another aim was to obtain more predictable tool maintenance by the implementation of acoustic emission measurement system on a hot-strip tribometer and correlating condition monitoring signals to friction and wear phenomena. The tribological tests were carried out using a hot-strip tribometer in conditions representative of a hot stamping process of automotive components. Acoustic emission during sliding between hot work tool steel and different automotive component material surfaces was measured at room temperature in the same strip drawing tribometer and correlated to friction and wear of the surfaces to get more predictable maintenance intervals. Tool steel specimens were welded with the same material as the base material QRO90. Before conducting the tribological test, the repair welded tool steel pin cross-section was polished, etched, and observed under optical microscope and SEM to analyze the effect of Tungsten Inert Gas (TIG) welding process on the microstructure. The analysis was completed with EDS to study the elements in the microstructure. Microhardness was measured to obtain the microhardness profile from the repair welded tool steel pin surface to the bulk in order to study the effect of different microstructures on the mechanical properties. The weight and surface roughness of the pins were measured before the tribological test. After the test was finished, the weight of the pins was measured to calculate the weight difference. The sliding surface of the pins and the strips were photographed. The sliding surface of the pins was also observed and analyzed using SEM and EDS after the test to study wear characteristic of the repair welded tool steel at high temperatures. Acoustic emission signal from the sliding was studied using Toolox44 pins with surface roughness 300-400 nm and with lay direction parallel and perpendicular to sliding direction. Toolox44 pins were sliding against uncoated UHSS, as-delivered Al-Si coated UHSS, and heat-treated Al-Si coated UHSS strips. Acoustic emission was measured during the sliding at the same time as COF measurement. Weight of the pins was measured before and after the test and the wear damage on both surfaces was photographed. COF, AE signals in the time and frequency domain, and wear damage were compared and analyzed. It is found that repair welded tool steel has similar COF compared to the original hot work tool steel with the largest weight gain from the test at 700 ⁰C due to compaction galling mechanism with slower lump formation and the presence of wear particles, transfer layer, and formation of lumps. The weight gain is smaller from the test at 750 ⁰C due to faster lump formation. The weight loss from the test at 600 ⁰C is due to abrasive wear mechanism. SEM micrographs revealed that the repair welded tool steel surface and transfer layers can be found beneath a transfer layer. Wear particles adhered on the repair welded tool steel surface come from broken transfer layer or directly from Al-Si coated UHSS. A change in wear mechanism is indicated by acoustic emission burst signals or gradual amplitude change in the time domain. Frequency analysis of AE signals revealed a change in wear mechanism due to the formation of transferred material in the form of a lump causes AE signals with peaks at higher frequencies above 0.3 MHz to shorten.

In order to meet EU legislation on emissions, significant effort is being invested into the development of cost-effective renewable power generation technologies. The two leading technologies are solar and wind power because of their potential for the lowest levelised cost of energy and for showing a growth in installed capacity and technological development. Various research findings have suggested that significant cost savings in the capital expenditure of renewable energy projects can be made through the optimisation of their support foundations, the understanding of which has formed the main goal of the research.