Kernel-based fault diagnosis of inertial sensors using analytical redundancy

Kernel methods are able to exploit high-dimensional spaces for representational advantage, while only operating implicitly in such spaces, thus incurring none of the computational cost of doing so. They appear to have the potential to advance the state of the art in control and signal processing applications and are increasingly seeing adoption across these domains. Applications of kernel methods to fault detection and isolation (FDI) have been reported, but few in aerospace research, though they offer a promising way to perform or enhance fault detection. It is mostly in process monitoring, in the chemical processing industry for example, that these techniques have found broader application. This research work explores the use of kernel-based solutions in model-based fault diagnosis for aerospace systems. Specifically, it investigates the application of these techniques to the detection and isolation of IMU/INS sensor faults – a canonical open problem in the aerospace field. Kernel PCA, a kernelised non-linear extension of the well-known principal component analysis (PCA) algorithm, is implemented to tackle IMU fault monitoring. An isolation scheme is extrapolated based on the strong duality known to exist between probably the most widely practiced method of FDI in the aerospace domain – the parity space technique – and linear principal component analysis. The algorithm, termed partial kernel PCA, benefits from the isolation properties of the parity space method as well as the non-linear approximation ability of kernel PCA. Further, a number of unscented non-linear filters for FDI are implemented, equipped with data-driven transition models based on Gaussian processes - a non-parametric Bayesian kernel method. A distributed estimation architecture is proposed, which besides fault diagnosis can contemporaneously perform sensor fusion. It also allows for decoupling faulty sensors from the navigation solution

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Application of Computational Intelligence Techniques to Process Industry Problems

In the last two decades there has been a large progress in the computational intelligence research field. The fruits of the effort spent on the research in the discussed field are powerful techniques for pattern recognition, data mining, data modelling, etc. These techniques achieve high performance on traditional data sets like the UCI machine learning database. Unfortunately, this kind of data sources usually represent clean data without any problems like data outliers, missing values, feature co-linearity, etc. common to real-life industrial data. The presence of faulty data samples can have very harmful effects on the models, for example if presented during the training of the models, it can either cause sub-optimal performance of the trained model or in the worst case destroy the so far learnt knowledge of the model. For these reasons the application of present modelling techniques to industrial problems has developed into a research field on its own. Based on the discussion of the properties and issues of the data and the state-of-the-art modelling techniques in the process industry, in this paper a novel unified approach to the development of predictive models in the process industry is presented

Near infrared and Raman spectroscopy for the in-process monitoring of pharmaceutical production processes

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Advanced sensors technology survey

This project assesses the state-of-the-art in advanced or 'smart' sensors technology for NASA Life Sciences research applications with an emphasis on those sensors with potential applications on the space station freedom (SSF). The objectives are: (1) to conduct literature reviews on relevant advanced sensor technology; (2) to interview various scientists and engineers in industry, academia, and government who are knowledgeable on this topic; (3) to provide viewpoints and opinions regarding the potential applications of this technology on the SSF; and (4) to provide summary charts of relevant technologies and centers where these technologies are being developed

Legal Challenges and Market Rewards to the Use and Acceptance of Remote Sensing and Digital Information as Evidence

Bakgrund I den nutida forskningen är det essentiellt att företag tar hänsyn till medarbetarnas motivation så att de gynnas av det arbetssätt som tillämpas. En arbetsmetod som blivit allt vanligare är konceptet Lean som ursprungligen kommer från den japanska bilindustrin. Lean har idag utvecklats till ett allmängiltigt koncept som tillämpas i flertalet branscher världen över. Trots att konceptet innebär flertalet positiva aspekter har det fått utstå stark kritik när det kommer till de mänskliga aspekterna och forskare har ställt sig frågan om Lean är "Mean". Kritiken härleds främst till medarbetares arbetsmiljö i form av stress och brist på variation, självbestämmande, hälsa och välmående. Få empiriska studier har däremot genomförts som undersöker konsekvenserna som Lean får på medarbetares upplevda motivation. Syfte Vårt syfte är att undersöka och öka förståelsen för medarbetares upplevelser av motivationen i företag som tillämpar Lean. Vidare har studien för avsikt att utreda om det föreligger en paradox mellan Lean och vad som motiverar medarbetare på en arbetsplats. Metod Studien har utgått från en kvalitativ metod via intervjuer. För att göra en djupare undersökning och analysera hur vårt fenomen, motivation, upplevs i en kontext med Lean tillämpade vi Små-N-studier. Vi har även haft en iterativ forskningsansats som förenat den deduktiva och induktiva ansatsen där studien pendlat mellan teorier och empiriska observationer fram tills det slutgiltiga resultatet. Slutsatser Utefter medarbetarnas upplevelser har vi identifierat att det inte föreligger någon paradox mellan Lean och motivation eftersom övervägande antal medarbetare upplevde att de är motiverade även om företaget tillämpar Lean. Dock har studien kunnat urskilja både stödjande och motverkande faktorer när det kommer till medarbetarnas upplevda arbetsförhållanden som i sin tur inverkar på motivationen. De motverkande faktorerna menar vi främst beror på att arbetsförhållandena i somliga fall innehåller höga prestationskrav, målstyrning samt standardiseringar. Vidare upplevs motivationen överlag som mer positiv när företagen använder en mjukare form av Lean där samtliga medlemmars intressen beaktas.Background In modern research, it is essential that companies consider employees’ motivation so that they benefit from the applied practices. A working method that has become increasingly common is the concept Lean, which has its origin in the Japanese automotive industry. Today, Lean has evolved into a universal concept that is applied in many industries worldwide. Although the concept involves numerous positive aspects it has endured strong criticism when it comes to the human aspects and researchers have raised the question if Lean is "Mean". Criticism is derived primarily to employees’ working conditions in terms of stress and lack, variation, autonomy, health and wellbeing. However, few empirical studies have been carried out that examines the impact that Lean has on employees’ experienced motivation. Aim The aim is to increase the understanding of employees’ experienced motivation in companies that practice Lean. Further on the study has the intention to investigate if there is a paradox between Lean and what motivates employees on work. Methodology The study has been conducted through a qualitative method by interviews and to be able to do a deeper examination and analyze how our phenomenon, motivation, is experienced in a Lean context we applied small-N-studies. Our strategy has been iterative, combining both a deductive and inductive approach, where the study has varied between theories and empirical observations until the final result. Conclusions We have identified that there is no paradox between Lean and motivation since the majority of employees’ experienced that they are motivated even though the company practice Lean. Nevertheless the study shows that there are both supportive and counteractive factors when it comes to the employees’ experienced working conditions. The counteractive factors consists foremost of high performance standards, goal steering and standardizations, and have in some cases a negative influence on the working conditions. Furthermore the experienced motivation is more positive overall when the companies use a softer form of Lean where all the members’ interests are taken into account

Design and implementation of an ultrasonic sensor for rapid monitoring of industrial malolactic fermentation of wines

Ultrasound is an emerging technology that can be applied to monitor food processes. However, ultrasonic techniques are usually limited to research activities within a laboratory environment and they are not extensively used in industrial processes. The aim of this paper is to describe a novel ultrasonic sensor designed to monitor physical–chemical changes that occur in wines stored in industrial tanks. Essentially, the sensor consists of an ultrasonic transducer in contact with a buffer rod, mounted inside a stainless steel tube section. This structure allows the ultrasonic sensor to be directly installed in stainless steel tanks of an industrial plant. The operating principle of this design is based on the measurement of ultrasonic velocity of propagation. To test its proper operation, the sensor has been used to measure changes of concentration in aqueous samples and to monitor the progress of a malolactic fermentation of red wines in various commercial wineries. Results show the feasibility of using this sensor for monitoring malolactic fermentations in red wines placed in industrial tanks.Postprint (author's final draft

Process monitoring and visualization solutions for hot-melt extrusion : a review

Objectives: Hot-melt extrusion (HME) is applied as a continuous pharmaceutical manufacturing process for the production of a variety of dosage forms and formulations. To ensure the continuity of this process, the quality of the extrudates must be assessed continuously during manufacturing. The objective of this review is to provide an overview and evaluation of the available process analytical techniques which can be applied in hot-melt extrusion. Key Findings: Pharmaceutical extruders are equipped with traditional (univariate) process monitoring tools, observing barrel and die temperatures, throughput, screw speed, torque, drive amperage, melt pressure and melt temperature. The relevance of several spectroscopic process analytical techniques for monitoring and control of pharmaceutical HME has been explored recently. Nevertheless, many other sensors visualizing HME and measuring diverse critical product and process parameters with potential use in pharmaceutical extrusion are available, and were thoroughly studied in polymer extrusion. The implementation of process analytical tools in HME serves two purposes: (1) improving process understanding by monitoring and visualizing the material behaviour and (2) monitoring and analysing critical product and process parameters for process control, allowing to maintain a desired process state and guaranteeing the quality of the end product. Summary: This review is the first to provide an evaluation of the process analytical tools applied for pharmaceutical HME monitoring and control, and discusses techniques that have been used in polymer extrusion having potential for monitoring and control of pharmaceutical HME