Modelling and Management of Uncertainty in Production Systems : from Measurement to Decision

The advanced handling of uncertainties arising from a wide range of sources is fundamental in quality control and dependability to reach advantageous decisions in different organizational levels of industry. Es-pecially in the competitive edge of production, uncertainty shall not be solely object of estimation but the result of a systematic management process. In this process, the composition and utilization of proper in-formation acquisition systems, capability models and propagation tools play an inevitable role. This thesis presents solutions from production system to operational level, following principles of the introduced con-cept of uncertainty-based thinking in production. The overall aim is to support transparency, predictability and reliability of production sys-tems, by taking advantage of expressed technical uncertainties. On a higher system level, the management of uncertainty in the quality con-trol of industrial processes is discussed. The target is the selection of the optimal level of uncertainty in production processes integrated with measuring systems. On an operational level, a model-based solution is introduced using homogeneous transformation matrices in combination with Monte Carlo method to represent uncertainty related to machin-ing system capability. Measurement information on machining systems can significantly support decision-making to draw conclusions on man-ufactured parts accuracy, by developing understanding of root-causes of quality loss and providing optimization aspects for process planning and maintenance.QC 20181015</p

Modelling and Management of Uncertainty in Production Systems : from Measurement to Decision

The advanced handling of uncertainties arising from a wide range of sources is fundamental in quality control and dependability to reach advantageous decisions in different organizational levels of industry. Es-pecially in the competitive edge of production, uncertainty shall not be solely object of estimation but the result of a systematic management process. In this process, the composition and utilization of proper in-formation acquisition systems, capability models and propagation tools play an inevitable role. This thesis presents solutions from production system to operational level, following principles of the introduced con-cept of uncertainty-based thinking in production. The overall aim is to support transparency, predictability and reliability of production sys-tems, by taking advantage of expressed technical uncertainties. On a higher system level, the management of uncertainty in the quality con-trol of industrial processes is discussed. The target is the selection of the optimal level of uncertainty in production processes integrated with measuring systems. On an operational level, a model-based solution is introduced using homogeneous transformation matrices in combination with Monte Carlo method to represent uncertainty related to machin-ing system capability. Measurement information on machining systems can significantly support decision-making to draw conclusions on man-ufactured parts accuracy, by developing understanding of root-causes of quality loss and providing optimization aspects for process planning and maintenance.QC 20181015</p

Prediction of the machine tool errors under quasi-static load : Developing methodology through the synthesis of bottom-up and top-down modeling approach

One of the biggest challenges in the manufacturing industry is to increase the understanding of the sources of the errors and their effects on machining systems accuracy. In this thesis a new robust empirical evaluation method is developed to predict the machine tool errors under quasi-static load including the effect of the variation of stiffness in the workspace, the geometric and the kinematic errors. These errors are described through combined computational models for a more accurate assessment of the machine tool’s capability. The purpose of this thesis is to establish such methodology through the synthesis of the bottom-up and the top-down modeling approach, which consists the combination of the direct (single axis measurements by laser interferometer) and indirect (multi-axis measurements by loaded double ball-bar) measurement technics. The bottom-up modeling method with the direct measurement was applied to predict the effects of the geometric and kinematic errors in the workspace of a machine tool. The top-down modeling method with the indirect measurement was employed to evaluate the variation of the static stiffness in the workspace of a machine tool. The thesis presents a case study demonstrating the applicability of the proposed approach. The evaluation technic extended for machine tools with various kinematic structures. The methodology was implemented on a three and a five axis machine tool and the results expose the potential of the approach

Modelling and Management of Uncertainty in Production Systems : from Measurement to Decision

The advanced handling of uncertainties arising from a wide range of sources is fundamental in quality control and dependability to reach advantageous decisions in different organizational levels of industry. Es-pecially in the competitive edge of production, uncertainty shall not be solely object of estimation but the result of a systematic management process. In this process, the composition and utilization of proper in-formation acquisition systems, capability models and propagation tools play an inevitable role. This thesis presents solutions from production system to operational level, following principles of the introduced con-cept of uncertainty-based thinking in production. The overall aim is to support transparency, predictability and reliability of production sys-tems, by taking advantage of expressed technical uncertainties. On a higher system level, the management of uncertainty in the quality con-trol of industrial processes is discussed. The target is the selection of the optimal level of uncertainty in production processes integrated with measuring systems. On an operational level, a model-based solution is introduced using homogeneous transformation matrices in combination with Monte Carlo method to represent uncertainty related to machin-ing system capability. Measurement information on machining systems can significantly support decision-making to draw conclusions on man-ufactured parts accuracy, by developing understanding of root-causes of quality loss and providing optimization aspects for process planning and maintenance.QC 20181015</p

Uncertainty Management for Automated Diagnostics of Production Machinery

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, Tröghetsmätningsenhe

Uncertainty Management for Automated Diagnostics of Production Machinery

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, Tröghetsmätningsenhe

Uncertainty Management for Automated Diagnostics of Production Machinery

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, Tröghetsmätningsenhe

Condition monitoring of rolling element bearings: benchmarking of data-driven methods

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.QC 20210521</p

Early fault diagnosis in rolling element bearings: comparative analysis of a knowledge-based and a data-driven approach

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.QC 20230918</p

Machine tool calibration: Measurement, modeling, and compensation of machine tool errors

International audienceAdvanced technologies for the calibration of machine tool are presented. Geometric, kinematic, thermally-induced and load-induced errors 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, mathematical models and compensation approaches 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