Modelling the rejection probability of a quality test consisting of multiple measurements

Abstract Quality control is an essential part of manufacturing, and the different properties of the products can be tested with standardized methods. If the decision of qualification is based on only one test specimen representing a batch of products, the testing procedure is quite straightforward. However, when the measured property has a high variability within the product, as usual, several test specimens are needed for the quality verification. When a quality property is predicted, the response value of the model that most effectively finds the critical observations should naturally be selected. In this thesis, it has been shown that LIB-transformation (Larger Is Better) is a suitable method for multiple test samples, because it effectively recognizes especially the situations where one of the measurements is very low. The main contribution of this thesis is to show how to model quality of phenomena that consist of several measurement samples for each observation. The process contains several steps, beginning from the selection of the model type. Prediction of the exceedance probability provides more information for the decision making than that of the mean. Especially with the selected application, where the quality property has no optimal value, but the interest is in adequately high value, this approach is more natural. With industrial applications, the assumption of constant variance should be analysed critically. In this thesis, it is shown that exceedance probability modelling can benefit from the use of an additional variance model together with a mean model in prediction. The distribution shape modelling improves the model further, when the response variable may not be Gaussian. As the proposed methods are fundamentally different, the model selection criteria have to be chosen with caution. Different methods for model selection were considered and commented, and EPS (Exceedance Probability Score) was chosen, because it is most suitable for probability predictors. This thesis demonstrates that especially a process with high diversity in its production and more challenging distribution shape gains from the deviation modelling, and the results can be improved further with the distribution shape modelling.Tiivistelmä Laadunvalvonnalla on keskeinen rooli teollisessa tuotannossa. Valmistettavan tuotteen erilaisia ominaisuuksia mitataan standardin mukaisilla testausmenetelmillä. Testi on yksinkertainen, jos tuotteen laatu varmistetaan vain yhdellä testikappaleella. Kun testattava ominaisuus voi saada hyvin vaihtelevia tuloksia samastakin tuotteesta, tarvitaan useita testikappaleita laadun varmistamiseen. Tuotteen laatuominaisuuksia ennustettaessa valitaan malliin vastemuuttuja, joka tehokkaimmin tunnistaa laadun kannalta kriittiset havainnot. Tässä väitöskirjassa osoitetaan, että LIB-transformaatio (Large Is Better) tunnistaa tehokkaasti erityisesti tilanteet, joissa yksi mittauksista on hyvin matala. Tämän väitöskirja vastaa kysymykseen, kuinka mallintaa laatua, kun tutkittavasta tuotteesta tarvitaan useita testinäytteitä. Mallinnusprosessi koostuu useista vaiheista alkaen mallityypin valinnasta. Alitusriskin mallinnuksen avulla saadaan enemmän informaatiota päätöksenteon tueksi perinteisen odotusarvomallinnuksen sijaan, etenkin jos laatutekijältä vaaditaan vain riittävän hyvää tasoa optimiarvon sijaan. Teollisissa sovelluksissa ei voida useinkaan olettaa, että vasteen hajonta olisi vakio läpi prosessin. Tässä väitöskirjassa osoitetaan että alitusriskin ennustamistarkkuus paranee, kun odotusarvon lisäksi mallinnetaan myös hajontaa. Jakaumamuodon mallilla voidaan parantaa ennustetarkkuutta silloin, kun vastemuuttuja ei noudata Gaussin jakaumaa. Koska ehdotetut mallit ovat perustaltaan erilaisia, täytyy myös mallin valintakriteeri valita huolella. Työssä osoitetaan, että EPS (Exceedance Probability Score) toimii parhaiten käytetyillä todennäköisyyttä ennustavilla malleilla. Tässä väitöskirjassa osoitetaan, että erityisesti silloin kun tuotantoprosessi on monimuotoinen ja laatumuuttujan jakaumamuoto on haastava, mallinnuttaminen hyötyy hajontamallin käytöstä, ja tuloksia voidaan parantaa jakaumamuodon mallilla

Online quality monitoring and smart manufacturing

Abstract The purpose of this study was to develop an innovative supervisor system to assist the operators in an industrial manufacturing process to help discover new alternative solutions for improving both the products and the manufacturing process. This paper presents the solution for integrating different types of statistical modelling methods for a usable industrial application in quality monitoring. The usability of the tool was tested both offline and online in two case studies

Trustworthy distributed intelligence for smart cities

Abstract The future of smart cities has been significantly impacted by Internet of Things (IoT) and distributed intelligence, where a large scale of data are collected from massive amounts of heterogeneous devices and distributed intelligence brings storage, computing, and Artificial Intelligence (AI) functionality close to the end devices where data are generated for providing novel services and applications. However, AI empowered systems face many challenges due to the inscrutability of complex AI models which weakens the trust of users. This paper provides a general understanding of the underlying concepts and challenges in trustworthy distributed intelligence. A use case of district heating network is illustrated to explore the proposed concepts, technologies, and challenges for enabling trustworthy distributed intelligence for smart cities

Intelligent methods for root cause analysis behind the center line deviation of the steel strip

Abstract This article presents a statistical prediction model-based intelligent decision support tool for center line deviation monitoring. Data mining methods enable the data driven manufacturing. They also help to understand the manufacturing process and to test different hypotheses. In this study, the original assumption was that the shape of the strip during the hot rolling has a strong effect on the behaviour of the steel strip in Rolling, Annealing and Pickling line (RAP). Our goal is to provide information that enables to react well in advance to strips with challenging shape. In this article, we show that the most critical shape errors arising in hot rolling process will be transferred to critical errors in RAP-line process as well. In addition, our results reveal that the most critical feature characterizes the deviation better than the currently used criterion for rework. The developed model enables the user to understand better the quality of the products, how the process works, and how the quality model predicts and performs

Deep learning-based multimodal data fusion:case study in food intake episodes detection using wearable sensors

Abstract Background: Multimodal wearable technologies have brought forward wide possibilities in human activity recognition, and more specifically personalized monitoring of eating habits. The emerging challenge now is the selection of most discriminative information from high-dimensional data collected from multiple sources. The available fusion algorithms with their complex structure are poorly adopted to the computationally constrained environment which requires integrating information directly at the source. As a result, more simple low-level fusion methods are needed. Objective: In the absence of a data combining process, the cost of directly applying high-dimensional raw data to a deep classifier would be computationally expensive with regard to the response time, energy consumption, and memory requirement. Taking this into account, we aimed to develop a data fusion technique in a computationally efficient way to achieve a more comprehensive insight of human activity dynamics in a lower dimension. The major objective was considering statistical dependency of multisensory data and exploring intermodality correlation patterns for different activities. Methods: In this technique, the information in time (regardless of the number of sources) is transformed into a 2D space that facilitates classification of eating episodes from others. This is based on a hypothesis that data captured by various sensors are statistically associated with each other and the covariance matrix of all these signals has a unique distribution correlated with each activity which can be encoded on a contour representation. These representations are then used as input of a deep model to learn specific patterns associated with specific activity. Results: In order to show the generalizability of the proposed fusion algorithm, 2 different scenarios were taken into account. These scenarios were different in terms of temporal segment size, type of activity, wearable device, subjects, and deep learning architecture. The first scenario used a data set in which a single participant performed a limited number of activities while wearing the Empatica E4 wristband. In the second scenario, a data set related to the activities of daily living was used where 10 different participants wore inertial measurement units while performing a more complex set of activities. The precision metric obtained from leave-one-subject-out cross-validation for the second scenario reached 0.803. The impact of missing data on performance degradation was also evaluated. Conclusions: To conclude, the proposed fusion technique provides the possibility of embedding joint variability information over different modalities in just a single 2D representation which results in obtaining a more global view of different aspects of daily human activities at hand, and yet preserving the desired performance level in activity recognition

How physical exercise level affects sleep quality?:analyzing big data collected from wearables

Abstract Physical exercise and sleep have independent, yet synergistic, impacts on the health. However, the effects of acute exercise level on sleep quality have not been well investigated. We utilize statistical methods to investigate the differences of exercise level between the good and bad sleep nights. Our results present a complex interrelation between physical exercise and sleep quality with analyzing large personal data sets collected from wearables. As far as we know, this is the first study to investigate insights of interrelation of physical exercise and sleep quality based on a big volume of data collected from wearable devices of real users

Evidence-based and explainable smart decision support for quality improvement in stainless steel manufacturing

Abstract This article demonstrates the use of data mining methods for evidence-based smart decision support in quality control. The data were collected in a measurement campaign which provided a new and potential quality measurement approach for manufacturing process planning and control. In this study, the machine learning prediction models and Explainable AI methods (XAI) serve as a base for the decision support system for smart manufacturing. The discovered information about the root causes behind the predicted failure can be used to improve the quality, and it also enables the definition of suitable security boundaries for better settings of the production parameters. The user’s need defines the given type of information. The developed method is applied to the monitoring of the surface roughness of the stainless steel strip, but the framework is not application dependent. The modeling analysis reveals that the parameters of the annealing and pickling line (RAP) have the best potential for real-time roughness improvement

Addressing students’ needs:development of a learning analytics tool for academic path level regulation

Abstract Development of learning analytics (LA) tools requires theory-based approach, careful implementation, and user-centred evaluation. In this paper we report on a two-stage user-centred and theory-based development and evaluation of a LA student tool. Results show that LA tool’s support for different phases of self-regulation needs to be clearly differentiated and tested with students

Prediction of sleep efficiency from big physical exercise data

Abstract Physical exercise can improve sleep quality. However, how to perform physical exercise to achieve the best possible improvements is not clear. In this article, we build predictive models based on volume real data collected from wearable devices to predict the sleep efficiency related to users’ daily exercise information. As far as we know, this is the first study to investigate insights of prediction of sleep efficiency from volume physical exercise data collected from real world

Predicting the heart rate response to outdoor running exercise

Abstract Heart rate is a good measure for physical exercise as it accurately reflects exercise intensity and is easy to measure. If the heart rate response to a complete exercise session is predicted beforehand, information related to the exercise can be inferred, such as exercise intensity and calorie consumption. While most current heart rate prediction models are developed and tested for the scenarios of indoor running exercise or low running speed exercise, we adopt a nonlinear Ordinary Differential Equation (ODE) model for complete outdoor running exercise sessions to predict the heart rate response and identify the parameters of the model with machine learning algorithms. The proposed model enables us to predict a complete outdoor running exercise session instead of predicting the heart rate for a short duration. Model validation is carried out both on the training and testing sets. Our results show that the proposed model captures very stable prediction performance