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Machine tools are essential components of modern manufacturing. They are com posed of various mechanical, hydraulic, and electrical systems such as the spindle, tool changer, cooling system, and the linear and rotary feed drives. Due to their com plexity, high cost, and importance to the manufacturing process it is recommended to implement some sort of condition monitoring and predictive maintenance to ensure that they remain reliable and high performing. One way of potentially implement ing predictive maintenance and condition monitoring is digital twins. Digital twins enable the real-time, accurate, and complex modeling and monitoring of mechanical systems. They utilize data collected from the system to constantly update their mod els which can be used for monitoring of the systems state and future predictions. This work presents a digital twin workbench of a machine tool feed drive. The workbench enables the collection and analysis of large, varied, high-frequency data which can be used to construct a digital twin of the feed drive. A digital twin can enable many other useful functionalities. Some of these functionalities include condition moni toring, modeling, control, visualization, and simulation. These functionalities can enable maximum asset performance and are key in implementing effective predictive maintenance. The main contributions of this work are the following: The design and iv construction of a machine tool feed drive which implements a novel external distur bance force method. A new method of fault detection in ball screws using interacting multiple models which was shown to provide accurate estimates of levels of preloads in a ball screw driven feed drive. A digital twin based modeling strategy and analysis of the data generated by the system including system modeling and observations on modeling difficulties. / Thesis / Master of Applied Science (MASc) / Digital twins enable the real-time, accurate, and complex modeling and monitoring of mechanical systems. Machine tools are essential components of modern manufac turing. They are composed of various mechanical, hydraulic, and electrical systems such as the spindle, tool changer, cooling system, and linear and rotary feed drives. This work presents the design of a workbench of a machine tool linear feed drive, a fault detection strategy, and a digital twin modeling solution. The workbench enables the collection and analysis of large, varied, high-frequency data which can be used to construct a digital twin of the feed drive. A digital twin can enable many other useful functionalities. Some of these functionalities include condition monitoring, modeling, control, visualization, and simulation. These functionalities can enable maximum asset performance and are key in implementing effective predictive maintenance.

The increasing demand for higher efficiency and lower environmental impact of transmissions, used in automotive and wind energy industries has created a need for more advanced technical solutions to fulfil those requirements. Condition monitoring plays an important role in the transmission life cycle, saving resources and time. Recently condition monitoring, using machine learning has shifted from reactive to proactive action, predicting minor faults before they become significant. This thesis intends to develop a methodology that can be used to predict faults like pitting initiation, before propagating in FZG test rig, available at KTH Machine Design department. Standard sensor measurements already available like temperature, rotation speed and torque are used in this project. Four kinds of gears were used, two made of wrought, and two – of powder metal steel, each with ground or superfinish surface. After a literature review about pitting fatigue, condition indicators for these failures and machine learning were done, a statistical analysis was done, to see how the transmission behaves during testing and to have comparison material, helpful when having machine learning results. Two machine learning models, Decision Tree and Support Vector Machine were selected and trained in two combinations, either with Root Mean Square only, or with Crest Factor, Standard Deviation and Kurtosis in addition. As a result, 64 models were trained, 32 for all tests and another 32 to investigate two particular tests due to a longer pitting propagation period. New condition indicators like Standard Deviation and Signal – to – noise ratio was calculated to get more nuanced trends than just using one measurement to monitor the gearbox behavior. After comparing with the results from statistical analysis and previously done tooth profile measurements, it was concluded that the new indicators could indicate the change in gearbox operation before the first pitting initiation is detected, using tooth profile measurement.

<p>Vibrating screens are industrial machines used to sort aggregates through their high rotational accelerations. Utilized in mining operations, they are able to screen dozens of tonnes of material per hour. To enhance maintenance and troubleshooting, this thesis introduces a vibration based condition monitoring system capable of observing machine operation. Using acceleration data collected from remote parts of the machine, software continuously detects for abnormal operation triggered by fault conditions. Users are to be notified in the event of a fault and be provided with relevant information.</p> <p>Acceleration data is acquired from a set of sensor devices that are mounted to specified points on the vibrating screen. Data is then wirelessly transmitted to a centralized unit for digital signal processing. Existing sensor devices developed for a previous project have been upgraded and integrated into the monitoring system. Alternative communication technologies and the utilized Wi-Fi network are examined and discussed.</p> <p>The condition monitoring system's hardware and software was designed following engineering principles. Development produced a functional prototype system, implementing the monitoring process. The monitoring technique utilizes signal filtering and processing to compute a set of variables that reveal the status of the machine. Decision making strategies are then employed as to determine when a fault has occurred.</p> <p>Testing performed on the developed monitoring system has also been documented. The performance of the prototype system is examined as different fault scenarios are induced and monitored. Results and descriptions of virtual simulations and live industrial experiments are presented. The relationships between machine faults and detected fault signatures are also discussed.</p> / Master of Applied Science (MASc)

This thesis introduces a multi-Bayesian framework for detection and classification of features in environments abundant with error-inducing noise. The approach takes advantage of Bayesian correction and classification in three distinct stages. The corrective scheme described here extracts useful but highly stochastic features from a data source, whether vision-based or otherwise, to aid in higher-level classification. Unlike many conventional methods, these features’ uncertainties are characterized so that test data can be correctively cast into the feature space with probability distribution functions that can be integrated over class decision boundaries created by a quadratic Bayesian classifier. The proposed approach is specifically formulated for road crack detection and characterization, which is one of the potential applications. For test images assessed with this technique, ground truth was estimated accurately and consistently with effective Bayesian correction, showing a 33% improvement in recall rate over standard classification. Application to road cracks demonstrated successful detection and classification in a practical domain. The proposed approach is extremely effective in characterizing highly probabilistic features in noisy environments when several correlated observations are available either from multiple sensors or from data sequentially obtained by a single sensor. / Master of Science / Humans have an outstanding ability to understand things about the world around them. We learn from our youngest years how to make sense of things and perceive our environment even when it is not easy. To do this, we inherently think in terms of probabilities, updating our belief as we gain new information. The methods introduced here allow an autonomous system to think similarly, by applying a fairly common probabilistic technique to the task of perception and classification. In particular, road cracks are observed and classified using these methods, in order to develop an autonomous road condition monitoring system. The results of this research are promising; cracks are identified and correctly categorized with 92% accuracy, and the additional “intelligence” of the system leads to a 33% improvement in road crack assessment. These methods could be applied in a variety of contexts as the leading edge of robotics research seeks to develop more robust and human-like ways of perceiving the world.

This dissertation presents an approach to reconstruct degenerate near-planar road surface in three-dimensional (3D) while automatically detect road defects. Three techniques are developed in this dissertation to establish the proposed approach. The first technique is proposed to reconstruct the degenerate near-planar road surface into 3D from one camera. Unlike the traditional Structure from Motion (SfM) technique which has the degeneracy issue for near-planar object 3D reconstruction, the uniqueness of the proposed technique lies in the use of near-planar characteristics of surfaces in the 3D reconstruction process, which solves the degenerate road surface reconstruction problem using only two images. Following the accuracy-enhanced 3D reconstructed road surface, the second technique automatically detects and estimates road surface defects. As the 3D surface is inversely solved from 2D road images, the detection is achieved by jointly identifying irregularities from the 3D road surfaces and the corresponding image information, while clustering road defects and obstacles using a mean-shift algorithm with flat kernel to estimate the depth, size, and location of the defects. To enhance the physics-driven automatic detection reliability, the third technique proposes and incorporates a self-supervised learning structure with data-driven Convolutional Neural Networks (CNN). Different from supervised learning approaches which need labeled training images, the road anomaly detection network is trained by road surface images that are automatically labeled based on the reconstructed 3D surface information. In order to collect clear road surface images on the public road, a road surface monitoring system is designed and integrated for the road surface image capturing and visualization. The proposed approach is evaluated in both simulated environment and through real-world experiments. The parametric study of the proposed approach shows the small error of the 3D road surface reconstruction influenced by different variables such as the image noise, camera orientation, and the vertical movement of the camera in a controlled simulation environment. The comparison with traditional SfM technique and the numerical results of the proposed reconstruction using real-world road surface images then indicate that the proposed approach effectively reconstructs high quality near-planar road surface while automatically detects road defects with high precision, accuracy, and recall rates without the degenerate issue. / Doctor of Philosophy / Road is one of the key infrastructures for ground transportation. A good road surface condition can benefit mainly on three aspects: 1. Avoiding the potential traffic accident caused by road surface defects, such as potholes. 2. Reducing the damage to the vehicle initiated by the bad road surface condition. 3. Improving the driving and riding comfort on a healthy road surface. With all the benefits mentioned above, it is important to examine and check the road surface quality frequently and efficiently to make sure that the road surface is in a healthy condition. In order to detect any road surface defects on public road in time, this dissertation proposes three techniques to tackle the road surface defects detection problem: First, a near-planar road surface three-dimensional (3D) reconstruction technique is proposed. Unlike traditional 3D reconstruction technique, the proposed technique solves the degenerate issue for road surface 3D reconstruction from two images. The degenerate issue appears when the object reconstructed has near-planar surfaces. Second, after getting the accuracy-enhanced 3D road surface reconstruction, this dissertation proposes an automatic defects detection technique using both the 3D reconstructed road surface and the road surface image information. Although physics-based detection using 3D reconstruction and 2D images are reliable and explainable, it needs more time to process these data. To speed up the road surface defects detection task, the third contribution is a technique that proposes a self-supervised learning structure with data-driven Convolutional Neural Networks (CNN). Different from traditional neural network-based detection techniques, the proposed combines the 3D road information with the CNN output to jointly determine the road surface defects region. All the proposed techniques are evaluated using both the simulation and real-world experiments. Results show the efficacy and efficiency of the proposed techniques in this dissertation.

Multifunktionale Strukturen sind heute in verschiedenen Fachbereichen von Wissenschaft und Technik von großer Bedeutung. Die Integration von Zusatzfunktionen in existierende Strukturen und Maschinenelemente ermöglicht die Entwicklung neuer innovativer Produkte, die nicht nur kostengünstig, sondern auch platzsparend hergestellt werden können oder vollkommen neue Funktion erfüllen. Das Kunststoffgleitlager mit inte-grierter Verschleißsensorik, das derzeit an der Professur Intelligente Maschinensysteme der Hochschule Mittweida erforscht wird, ist ein beispielhaftes Forschungsprojekt im Bereich multifunktionaler Strukturen. Das Kunststoffgleitlager bietet dem Anwender die Möglichkeit, dank der integrierten Verschleißsensorik aus elektrisch leitfähigem thermoplastischem Kunststoff, Betriebsdaten in Echtzeit zu ermitteln. Diese Innovation ermöglicht die Erfassung des Verschleißgrades des Lagers während des Betriebs, was wiederum eine effektivere Planung von Wartungsintervallen erlaubt. Durch die Vermeidung des vorbeugenden Austauschs noch funktionsfähiger Lager können erhebliche Ressourcen und Kosten eingespart werden. Zur Analyse des Betriebsverhaltens der Sensorelemente wurden umfangreiche numerische Untersuchungen zum mechanischen, thermischen und elektrischen Verhalten des Kunststoffgleitlagers durchgeführt. Ein gekoppeltes Modell wurde in Ansys entwickelt, und mittels einer Parameterstudie verschiedene Szenarien simuliert. Die erzielten Ergebnisse bieten einen detaillierten Einblick in das Betriebsverhalten und die Funktion des Lagers inkl. Sensorik. Mit diesen Erkenntnissen konnte ein Verschleißmodell erstellt werden, welches auf Basis der Sensorwerte kraft- richtungsunabhängig den Verschleißzustand des Lagers ermittelt. Weiterhin kann mit den ermittelten Daten ein passgenauer Messverstärker effizient entwickelt werden. / Multifunctional structures are of great importance in various fields of science and technology today. The integration of additional functions into existing structures and machine elements enables the development of new innovative products that can be manufactured not only cost-effectively but also in a space-saving manner or fulfill entirely new functions. The plastic plain bearing with integrated wear sensing, currently being researched at the Chair of Intelligent Machine Systems at Mittweida University of Applied Sciences, is an exemplary research project in the field of multifunctional structures. The plastic plain bearing provides the user with the ability to determine operating data in real-time thanks to the integrated wear sensing made of electrically conductive thermoplastic material. This innovation enables the monitoring of the degree of wear of the bearing during operation, which in turn allows for more effective planning of maintenance intervals. By avoiding the preventive replacement of still functional bearings, significant resources and costs can be saved. Extensive numerical investigations into the mechanical, thermal, and electrical behavior of the plastic plain bearing were conducted to analyze the operational behavior of the sensor elements. A coupled model was developed in Ansys, and various scenarios were simulated through a parameter study. The results obtained provide a detailed insight into the operational behavior and functionality of the bearing including the sensor system. With this knowledge, a wear model was created, which determines the wear condition of the bearing independently of the direction of force based on the sensor values. Furthermore, with the determined data, a precisely fitting signal amplifier can be efficiently developed.

This thesis aims to identify methods for detection of damper winding faults in synchronous machines (SMs) powered by variable frequency drives (VFDs). The problem of failing damper windings has received attention after reports of serious damage which have been discovered during maintenance checks. Since SMs often are used for critical applications, failures can be devastating if they cause total breakdowns. Also, VFDs are believed to cause additional stress in the damper windings of SMs and since the utilisation of VFDs is increasing, the problem is expected to become more common in the future. Currently, there is no method for detection of failures during normal operation of SMs, thus research in the area is required. Simulations based on the finite element method as well as laboratory experiments have been performed in order to examine the impact of VFDs and to find a way of detecting damper winding failures. The results confirm that utilization of VFDs produce higher currents in the damper winding compared to direct-online operation. The results also show that in case of a damper bar failure, the current distribution among the damper winding segments is affected. However, monitoring of all damper winding segments is unrealistic due to the number of sensors required. Another approach, which has been investigated, involves monitoring of the current through the pole interconnectors of one of the endrings. Potential fault indicators have been found by analysing the currents in the frequency domain. However, further studies are required in order to evaluate the method. Also the temperature of the damper winding was investigated as an indicator.

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