The early identification of a defect that is developing in a bearing is crucial for avoiding failures in rotating machinery. Frequency domain analysis of the vibration signals has been shown to contribute to a better understanding of the nature of a developing defect. Early signs of degradation might be more noticeable in certain frequency bands. The advantages in identifying and monitoring these bandwidths are several: prevention of serious machinery damages, reduction of the loss of investments, and improvement of the accuracy in failure predicting models. This paper presents and compares two approaches for the diagnosis of bearing faults. The first approach was knowledge-based. It relied on principles of mechanics to interpret the measured vibration signals and utilized prior knowledge of the bearing characteristics and testing parameters. The second approach was data-driven whereby data were acquired exclusively from the vibration signal. Both approaches were successfully applied for fault diagnosis by identifying the frequencies of the vibration spectra characteristic for the bearing under study. From this, bandwidths of interest for early fault detection could be determined. The diagnostic abilities of both approaches were studied to analyze and compare their individual strengths regarding the aspects of implementation time, domain knowledge, data processing associated knowledge, data requirements, diagnostic performance, and practical applicability. The advantages, apparent limitations as well as avenues for further improvement of both approaches are discussed.

Advanced technologies for the calibration of machine tools are presented. Kinematic errors independently of their causes are classified into errors within one-axis as intra-axis errors, errors between axes as inter-axis errors, and as volumetric errors. As the major technological elements of machine tool calibration, the measurement methods, modeling theories, and compensation strategies of the machine tool errors are addressed. The criteria for selecting a combination of the technological elements for machine tool calibration from the point of view of accuracy, complexity, and cost are provided. Recent applications of artificial intelligence and machine learning in machine tool calibration are introduced. Remarks are also made on future trends in machine tool calibration.

Application of laser-based measurement systems such as LiDARs are becoming more common in a wide range of application areas. Many of these applications are sensitive on the performance of the positioning measurements LiDARs. The main objective of this paper is to describe a methodology for performance evaluation of LiDAR-based position measurement system. The proposed approach, based on carried out experimentation, can generally be applicable for LiDAR evaluation for large-scale position measurements. The methodology was developed for the characterization of volumetric error of positioning measurements in a three-dimensional Cartesian coordinate system with the utilization of an external laser tracker. The methodology requires a measurement artifact with a spherical geometry which is inspected from various distances with the LiDAR. The laser tracker measurements are used for validation and verification purposes. This paper presents a case study with the implementation of the proposed methodology for a selected commercially available LiDAR system. Positioning error identification is essential for performance evaluation, the measurement shown in the paper outlines the performance of the selected LiDAR system.

Condition-based maintenance (CBM) is a maintenance strategy used to gain updated information about equipment condition and is today considered a natural part of the engineering field. The replacement of the traditional scheduled maintenance strategy in favor of CBM has the potential to significantly improve the safety of the system operating in harsh environments of the operation and increase in productivity by prolonging the life of an asset and preventing costly breakdowns. For many years CBM remained the subject of vigorous research and discussions. Increasing the automation level and the number of sensors in industries allowed obtaining and collecting data in large amounts. The current level of computational power allows us to process and analyse this massive amount of data, which has given a new leap in the development of industrial analytics. Rather than in the case of classical knowledge-based modelling tools, data-driven methods propose modelling and forecasting frameworks based on data analysis. Consequently, the transition to data-driven modelling gave a leap in CBM research and has recently drawn increasing attention, providing new case studies, algorithms, and results. However, technical challenges remain. Despite great flexibility and good forecasting performances, there are several limitations of data-driven algorithms. This paper provides an overview of the data-driven failure algorithms for rolling element bearings monitoring. Bearings have played a pivotal role in industrial machinery to operate with high efficiency and safety. They are considered to be one of the most common machine elements of precision rotating machinery. A benchmarking of various predictive and descriptive algorithms was performed. The analysis was carried out on a dataset from the run-to-failure experiments on bearings from NASA's Data Repository. This paper also summarizes the current trends and highlights the limitations with respect to traditional knowledge-based modelling. Special attention is paid to identifying research gaps and promising research directions.

The research work in this paper was carried out to reach advanced positioning capabilities of unmanned aerial vehicles (UAVs) for indoor applications. The paper includes the design of a quadcopter and the implementation of a control system with the capability to position the quadcopter indoor using onboard visual pose estimation system, without the help of GPS. The project also covered the design and implementation of quadcopter hardware and the control software. The developed hardware enables the quadcopter to raise at least 0.5kg additional payload. The system was developed on a Raspberry single-board computer in combination with a PixHawk flight controller. OpenCV library was used to implement the necessary computer vision. The Open-source software-based solution was developed in the Robotic Operating System (ROS) environment, which performs sensor reading and communication with the flight controller while recording data about its operation and transmits those to the user interface. For the vision-based position estimation, pre-positioned printed markers were used. The markers were generated by ArUco coding, which exactly defines the current position and orientation of the quadcopter, with the help of computer vision. The resulting data was processed in the ROS environment. LiDAR with Hector SLAM algorithm was used to map the objects around the quadcopter. The project also deals with the necessary camera calibration. The fusion of signals from the camera and from the IMU (Inertial Measurement Unit) was achieved by using Extended Kalman Filter (EKF). The evaluation of the completed positioning system was performed with an OptiTrack optical-based external multi-camera measurement system. The introduced evaluation method has enough precision to be used to investigate the enhancement of positioning performance of quadcopters, as well as fine-Tuning the parameters of the used controller and filtering approach. The payload capacity allows autonomous material handling indoors. Based on the experiments, the system has an accurate positioning system to be suitable for industrial application.

Stiffness is an important characteristic of production machinery, as it contributes to its ability to precisely maintain the pose between a tool centre point with respect to a workpiece under loads. For machine tools, it directly affects the geometric dimensions and surface properties of the parts, i.e. how closely the parts match their design drawings. This work presents an efficient measurement procedure to measure and identify the full translational stiffness matrices of machine tools. The measurement procedure consists of inducing static loads, which vary in magnitude and direction, at the tool centre point of the machine tool using the Loaded Double Ball Bar and measures the displacement with three Linear Variable Differential Transformers. The main components of the uncertainty budget related to the measurement of the cross compliance are also summarized. The measurement procedure is implemented in a case study on a 5-axis machining centre. Finally, the manuscript concludes with a discussion on the utility value of the translational stiffness matrix for the design and operation of machine tools as well as the possibility to expand the measurement procedure to capture the quasi-static and dynamic compliance.

The included angle of a V-groove on a large ceramic workpiece with a length of 500 mm is measured on an ultra-precision surface grinding machine. A pair of electronic dial gauges is mounted on the grinding wheel head to detect the ground flank surfaces of the groove. A calibrated artefact with identical material and nominal geometry but a shorter length of 100 mm is aligned side-by-side with the workpiece. The influences of machine scanning motion errors and thermal errors on the angle measurement are compensated through measuring the angular deviations of the V-grooves on the workpiece and on the artefact.

Neither production machinery, nor production systems will ever become completely describable or predictable. This results in the continuous need for monitoring and diagnostics of such systems in order to manage related uncertainties. In advanced production systems uncertainty has to be the subject to a systematic management process to maintain machine health and improve performance. Automation of diagnostics can fundamentally improve this management process by providing an affordable and scalable information source. In this thesis, the important aspects of uncertainty management in production systems are established and serve as a basis for the composition of an uncertainty-based machine diagnostics framework. The proposed framework requires flexible, fast, integrated and automated diagnostics methods. An inertial measurement-based test method is presented in order to satisfy these requirements and enable automated measurements for diagnostics of production machinery. The gained insights and knowledge about production machine health and capability improve transparency, predictability and dependability of production machinery and production systems. These improvements lead to increased overall equipment effectiveness and higher level of sustainability in operation.

Varken produktionsmaskiner eller produktionssystem kommer någonsin att bli fullständigt beskrivbara eller förutsägbara. Detta resulterar i ett kontinuerligt behov av övervakning och diagnostik av anordningar och system för att kunna hantera relaterade osäkerheter. I avancerade produktionssystem måste osäkerhet vara föremål för en systematisk hanteringsprocess för att upprätthålla maskinshälsa och förbättra prestanda. Automatisering av diagnostik kan fundamentalt förbättra denna hanteringsprocess genom att tillhandahålla en prisvärd och skalbar informationsk

älla.

I den här avhandlingen fastställs de viktiga aspekterna av osäkerhetshantering i produktionssystem och detta utgör grunden för konstruktionen av ett osäkerhetsbaserat ramverk för maskindiagnostik. Det föreslagna ramverket kräver flexibla, snabba, integrerade och automatiserade diagnostiska metoder. En tröghetsmätningsbaserad testmetod presenteras för att uppfylla dessa krav och möjliggöra automatiserade mätningar för diagnostik av produktionsmaskiner. De erhållna insikterna och kunskaperna relaterade till produktionsmaskinens hälsa och kapacitet förbättrar transparens, förutsägbarhet och pålitlighet för produktionsmaskiner och produktionssystem. Dessa förbättringar leder till ökad övergripande utrustningseektivitet och högre resurseektivitet.

Nyckelord: Osäkerhetshantering, Automatiserad Diagnostik,

Tröghetsmätningsenhet

The accuracy of multi-axis machine tools is affected to a large extent by the behavior of the system's axes and their error sources. In this paper, a novel methodology using circular inertial measurements quantifies changes in squareness between two axes of linear motion. Conclusions are reached through direct utilization of measured accelerations without the need for double integration of sensor signals. Results revealed that the new methodology is able to identify squareness values verified with traditional measurement methods. The work supports the integration of sensors into machine tools in order to reach higher levels of measurement automation. behalf of CIRP.

Manufacturers need online methods that give up-to-date information of system capabilities to know and predict the performance of their machine tools. Use of an inertial measurement unit (IMU) is attractive for on-machine condition monitoring, so methods based on spatial filters were developed to determine rail wear conditions of linear guideways of a carriage from its IMU-based error motion. Rail wear-induced changes in translational and angular error motions as small as 1.5 mu m and 3.0 microradians, respectively, could be resolved. A corresponding two-part root-cause analysis procedure was developed to determine the rail locations of error motion degradation as well as the most probable physical location of damage that causes the detected error motion changes. Another analysis method determined the root cause of non-localized damage along each rail. These approaches support the development of smart machine tools that provide actionable intelligence to manufacturers for early warnings of system degradation.