Process Monitoring

Wireless sensor networks are appealing to researchers due to their wide range of application potential in areas such as target detection and tracking, environmental monitoring, industrial process monitoring, and tactical systems. However, low sensing ranges result in dense networks, which bring the necessity to achieve an efficient medium access protocol subject to power constraints. Various MAC protocols with different objectives were proposed for wireless sensor networks. In this paper, we first outline the sensor network properties that are crucial for the design of MAC layer protocols. Then, we describe several MAC protocols proposed for sensor networks emphasizing their strengths and weaknesses. Finally, we point out open research issues on MAC layer design.

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.

In this paper, a new nonlinear process monitoring technique based on kernel principal component analysis (KPCA) is developed. KPCA has emerged in recent years as a promising method for tackling nonlinear systems. KPCA can e ciently compute principal components in high-dimensional feature spaces by means of integral operators and nonlinear kernel functions. The basic idea of KPCA is to ÿrst map the input space into a feature space via nonlinear mapping and then to compute the principal components in that feature space. In comparison to other nonlinear principal component analysis (PCA) techniques, KPCA requires only the solution of an eigenvalue problem and does not entail any nonlinear optimization. In addition, the number of principal components need not be speciÿed prior to modeling. In this paper, a simple approach to calculating the squared prediction error (SPE) in the feature space is also suggested. Based on T 2 and SPE charts in the feature space, KPCA was applied to fault detection in two example systems: a simple multivariate process and the simulation benchmark of the biological wastewater treatment process. The proposed approach e ectively captured the nonlinear relationship in the process variables and showed superior process monitoring performance compared to linear PCA. ?

A set of electrostatically actuated microelectromechanical test structures is presented that meets the emerging need for microelectromechanical systems (MEMS) process monitoring and material property measurement at the wafer level during both process development and manufacturing.

Chemometrics, the application of mathematical and statistical methods to the analysis of chemical data, is finding ever widening applications in the chemical process environment. This article reviews the chemometrics approach to chemical process monitoring and fault detection. These approaches rely on the formation of a mathematical/statistical model that is based on historical process data. New process data can then be compared with models of normal operation in order to detect a change in the system. Typical modelling approaches rely on principal components analysis, partial least squares and a variety of other chemometric methods. Applications where the ordered nature of the data is taken into account explicitly are also beginning to see use. This article reviews the state-of-the-art of process chemometrics and current trends in research and applications.

In continuously stirred tank reactor experiments, with manure as substrate at thermophilic temperatures, the use of volatile fatty acids (VFA) as process indicators was investigated. Changes in ¥FA level were shown to be a good parameter for indicating process instability. The VFA were evaluated according to their relative changes caused by changes in hydraulic loading, organic loading or temperature. Butyrate and isobutyrate together were found to be particularly good indicators. Butyrate and isobutyrate concentrations increased significantly 1 or 2 days after the imposed perturbation, which makes these acids suitable for process monitoring and important for process control of the anaerobic biological system. In addition it was shown in a batch experiment that VFA at concentrations up to 50 mM did not reduce the overall methane production rate. This showed that ¥FA accumulation in anaerobic reactors was the result of process imbalance, not the cause of inhibition, thus justifying the use of VFA as process indicators.

In this paper we propose a new statistical method for process monitoring that uses independent component analysis (ICA). ICA is a recently developed method in which the goal is to decompose observed data into linear combinations of statistically independent components . Such a representation has been shown to capture the essential structure of the data in many applications, including signal separation and feature extraction. The basic idea of our approach is to use ICA to extract the essential independent components that drive a process and to combine them with process monitoring techniques. I 2 , I 2 e and SPE charts are proposed as on-line monitoring charts and contribution plots of these statistical quantities are also considered for fault identification. The proposed monitoring method was applied to fault detection and identification in both a simple multivariate process and the simulation benchmark of the biological wastewater treatment process, which is characterized by a variety of fault sources with non-Gaussian characteristics. The simulation results clearly show the power and advantages of ICA monitoring in comparison to PCA monitoring.

While principal component analysis (PCA) has found wide application in process monitoring, slow and normal process changes often occur in real processes, which lead to false alarms for a ®xed-model monitoring approach. In this paper, we propose two recursive PCA algorithms for adaptive process monitoring. The paper starts with an ecient approach to updating the correlation matrix recursively. The algorithms, using rank-one modi®cation and Lanczos tridiagonalization, are then proposed and their computational complexity is compared. The number of principal components and the con®dence limits for process monitoring are also determined recursively. A complete adaptive monitoring algorithm that addresses the issues of missing values and outlines is presented. Finally, the proposed algorithms are applied to a rapid thermal annealing process in semiconductor processing for adaptive monitoring. #

This paper discusses contribution plots for both the D-statistic and the Q-statistic in multivariate statistical process control of batch processes. Contributions of process variables to the D-statistic are generalized to any type of latent variable model with or without orthogonality constraints. The calculation of contributions to the Q-statistic is discussed. Control limits for both types of contributions are introduced to show the relative importance of a contribution compared to the contributions of the corresponding process variables in the batches obtained under normal operating conditions. The contributions are introduced for off-line monitoring of batch processes, but can easily be extended to on-line monitoring and to continuous processes, as is shown in this paper.

The issue of how to improve product quality and product yield in a brief period of time becomes more critical in many industries. Even though industrial processes are totally different in appearance, the problems to solve are highly similar: how to build a reliable model from a limited data, how to analyze the model and relate it to first principles, how to optimize operating condition, and how to realize an on-line monitoring and control system and maintain it. In this paper, statistical process monitoring and control methodologies are briefly surveyed, and our application results in steel facilities are presented. The achievements of the present work are as follows: 1) the development of a new method that can cope with qualitative quality information and relate operating conditions to product quality or product yield, 2) the simultaneous analysis of multiple processing units including a converter, a continuous caster, a blooming process, and rolling processes, and 3) the successful application results in the steel industry.

Micro-stereolithography mSL is a novel micro-manufacturing process which builds the truly 3D microstructures by solidifying the liquid monomer in a layer by layer fashion. In this work, an advanced mSL apparatus is designed and developed which includes an Ar q laser, the beam delivery system, computer-controlled precision x-y-z stages and CAD design tool, and in situ process monitoring systems. The 1.2 mm resolution of mSL fabrication has been achieved with this apparatus. The microtubes with high aspect ratio of 16 and real 3D microchannels and microcones are fabricated on silicon substrate. For the first time, mSL of ceramic microgears has been successfully demonstrated.

Groundwater is one of the most valuable natural resources, which supports human health, economic development and ecological diversity. Overexploitation and unabated pollution of this vital resource is threatening our ecosystems and even the life of future generations. With the advent of powerful personal computers and the advances in space technology, efficient techniques for land and water management have evolved of which RS (remote sensing) and GIS (geographic information system) are of great significance. These techniques have fundamentally changed our thoughts and ways to manage natural resources in general and water resources in particular. The main intent of the present paper is to highlight RS and GIS technologies and to present a comprehensive review on their applications to groundwater hydrology. A detailed survey of literature revealed six major areas of RS and GIS applications in groundwater hydrology: (i) exploration and assessment of groundwater resources, (ii) selection of artificial recharge sites, (iii) GIS-based subsurface flow and pollution modeling, (iv) groundwater-pollution hazard assessment and protection planning, (v) estimation of natural recharge distribution, and (vi) hydrogeologic data analysis and process monitoring. Although the use of these techniques in groundwater studies has rapidly increased since early nineties, the success rate is very limited and most applications are still in their infancy. Based on this review, salient areas in need of further research and development are discussed, together with the constraints for RS and GIS applications in developing nations. More and more RSand GIS-based groundwater studies are recommended to be carried out in conjunction with field investigations to effectively exploit the expanding potential of RS and GIS technologies, which will perfect and standardize current applications as well as evolve new approaches and applications. It is concluded that both the RS and GIS technologies have great potential to

Westerhuis et al. (J. Chemometrics 1998; 12: 301–321) show that the scores of consensus PCA and multiblock PLS (Westerhuis and Coenegracht, J. Chemometrics 1997; 11: 379–392) can be calculated directly from the regular PCA and PLS scores respectively. In this paper we show that both the loadings and scores of consensus PCA can be calculated directly from those of regular PCA, and the multiblock PLS loadings, weights and scores can be calculated directly from those of regular PLS. The orthogonal properties of four multiblock PCA and PLS algorithms are explored. The use of multiblock PCA and PLS for decentralized monitoring and diagnosis is derived in terms of regular PCA and PLS scores and residuals. While the multiblock analysis algorithms are basically equivalent to regular PCA and PLS, blocking of process variables in a large-scale plant based on process knowledge helps to localize the root cause of the fault in a decentralized manner. New definitions of block and variable contributions to SPE and T 2 are proposed for decentralized monitoring. This decentralized monitoring method based on proper variable blocking is successfully applied to an industrial polyester film process. Copyright © 2001 John Wiley & Sons, Ltd.

This paper presents a new method to perform fault diagnosis for data-correlation based process monitoring. As alternative to the traditional contribution plot method, reconstruction-based contribution of fault detection indices is proposed. The monitored indices are SP E, T 2 and a combined index ϕ. The lack of diagnosability of traditional contributions is analyzed for the case of single sensor faults with large fault magnitudes, whereas for the same case the proposed reconstruction-based contributions guarantee correct diagnosis. Monte Carlo simulation results are provided for the case of modest fault magnitudes by randomly assigning fault sensors and fault magnitudes.

A method for sintering nanoparticles by applying voltage is presented. This electrical sintering method is demonstrated using silver nanoparticle structures ink-jet-printed onto temperature-sensitive photopaper. The conductivity of the printed nanoparticle layer increases by more than five orders of magnitude during the sintering process, with the final conductivity reaching 3.7 × 10 7 S m −1 at best. Due to a strong positive feedback induced by the voltage boundary condition, the process is very rapid-the major transition occurs within 2 μs. The best obtained conductivity is two orders of magnitude better than for the equivalent structures oven-sintered at the maximum tolerable temperature of the substrate. Additional key advantages of the method include the feasibility for patterning, systematic control of the final conductivity and in situ process monitoring. The method offers a generic tool for electrical functionalization of nanoparticle structures.

Cutting tool wear degrades the product quality in manufacturing processes. Monitoring tool wear value online is therefore needed to prevent degradation in machining quality. Unfortunately there is no direct way of measuring the tool wear online. Therefore one has to adopt an indirect method wherein the tool wear is estimated from several sensors measuring related process variables. In this work, a neural network-based sensor fusion model has been developed for tool condition monitoring (TCM). Features extracted from a number of machining zone signals, namely cutting forces, spindle vibration, spindle current, and sound pressure level have been fused to estimate the average flank wear of the main cutting edge. Novel strategies such as, signal level segmentation for temporal registration, feature space filtering, outlier removal, and estimation space filtering have been proposed. The proposed approach has been validated by both laboratory and industrial implementations. r

Chemical process plant safety, production specifications, environmental regulations, operational constraints, and plant economics are some of the main reasons driving an upward interest in research and development of more robust methods for process monitoring and control. Principal component analysis (PCA) has long been used in fault detection by extracting relevant information from multivariate chemical data. The recent success of wavelets and multi-scale methods in chemical process monitoring and control has catalyzed an interest in the investigation of wavelets based methods for fault detection. In the present work, multi-scale principal component analysis (MSPCA) is used for fault detection and diagnosis. MSPCA simultaneously extracts both, cross correlation across the sensors (PCA approach) and auto-correlation within a sensor (wavelet approach). Using wavelets, the individual sensor signals are decomposed into approximations and details at different scales. Contributions from each scale are collected in separate matrices, and a PCA model is then constructed to extract correlation at each scale. The multi-scale nature of MSPCA formulation makes it suitable to work with process data that are typically non-stationary and represent the cumulative effect of many underlying process phenomena, each operating at a different scale. The proposed MSPCA approach is able to outperform the conventional PCA based approach in detecting and identifying real process faults in an industrial process, and yields minimum false alarms. Additionally, the advantage of MSPCA, over the traditional PCA approach for sensor validation, is also demonstrated on an industrial boiler data set.

Most multivariate statistical monitoring methods based on principal component analysis (PCA) assume implicitly that the observations at one time are statistically independent of observations at past time and the latent variables follow a Gaussian distribution. However, in real chemical and biological processes, these assumptions are invalid because of their dynamic and nonlinear characteristics. Therefore, monitoring charts based on conventional PCA tend to show many false alarms and bad detectability. In this paper, a new statistical process monitoring method using dynamic independent component analysis (DICA) is proposed to overcome these disadvantages. ICA is a recently developed technique for revealing hidden factors that underlies sets of measurements followed on a non-Gaussian distribution. Its goal is to decompose a set of multivariate data into a base of statistically independent components without a loss of information. The proposed DICA monitoring method is applying ICA to the augmenting matrix with time-lagged variables. DICA can show more powerful monitoring performance in the case of a dynamic process since it can extract source signals which are independent of the auto-and cross-correlation of variables. It is applied to fault detection in both a simple multivariate dynamic process and the Tennessee Eastman process. The simulation results clearly show that the method e ectively detects faults in a multivariate dynamic process. ?

In this research, we develop a new fault identification method for kernel principal component analysis (kernel PCA). Although it has been proved that kernel PCA is superior to linear PCA for fault detection, the fault identification method theoretically derived from the kernel PCA has not been found anywhere. Using the gradient of kernel function, we define two new statistics which represent the contribution of each variable to the monitoring statistics, Hotelling's T 2 and squared prediction error (SPE) of kernel PCA, respectively. The proposed statistics which have similar concept to contributions in linear PCA are directly derived from the mathematical formulation of kernel PCA and thus they are straightforward to understand. The main contribution of this work is that we firstly suggest a fault identification method especially applicable to process monitoring using kernel PCA. To demonstrate the performance, the proposed method is applied to two simulated processes, one is a simple nonlinear process and the other is a non-isothermal CSTR process. The simulation results show that the proposed method effectively identifies the source of various types of faults.

Chemical Imaging (CI) is an emerging platform technology that integrates conventional imaging and spectroscopy to attain both spatial and spectral information from an object. Vibrational spectroscopic methods, such as Near Infra Red (NIR) and

Magmatic production on Earth is dominated by asthenospheric melts of basaltic composition erupted at mid-ocean ridges, at hotspots, or being mostly differentiated at the base off or within arc crust. The time scale for segregation and transport of these melts is critically dependent on the permeability of the partially molten asthenospheric mantle. We employed a centrifuging piston cylinder at accelerations of 400-700 g, P = 1 GPa, and T = 1270-1300 oC, to measure the rate of basalt melt flow in olivine aggregates with porosities of 5-12 %. After annealing and textural equilibration at ambient acceleration but identical P-T conditions, centrifuging of samples with an initially homogeneous melt distribution for 21-24 h yields melt gradients along the sample axis (parallel to the gravity acceleration) of up to 12 vol% melt/mm. From this gradient, we determine melt velocities at the barycenter of the capsule which in turn lead to permeabilities of our samples. With the experimentally known porosities we have then determined the constant C in the permeability-porosity relationship κ = d2 φ3 / C to 3-27 with a preferred value of 10. The resulting permeabilities are consistent with a microscopic model in which melt is completely connected and are 1-2 orders of magnitude larger than currently predicted employing a C of 200-300. As a direct consequence melt extraction speeds become proportionally faster. Taken at face value, extrapolation of the measurements to conditions characteristic of sub mid-ocean ridge (MOR) asthenosphere yield extraction speeds of 2-130 m/yr for uniform flow assuming plausible upper asthenospheric parameters (d˜10-2 m, μ˜10 Pa s, ρ=500 kg/m3) and porosities on the order of 10-2 as indicated by sub-MOR geophysical observations. Application of these results in a model for porous media channelling instabilities yields melt transport times of ~ 1-2.5 kyr across the entire asthenosphere, sufficient to preserve the observed 230Th and 231Pa excesses of MOR basalts and the mantle signature of even shorter-lived isotopes such as 226Ra.

Batch processes are very important in most industries and are used to produce high-quality materials, which causes their monitoring and control to emerge as essential techniques. Several multivariate statistical analyses, including multiway principal component analysis (MPCA), have been developed for the monitoring and fault detection of batch process. In this paper, a new batch monitoring method using multiway kernel principal component analysis (MKPCA) is proposed. Three-way batch data of normal batch process are unfolded batch-wise, and then KPCA is used to capture the nonlinear characteristics within normal batch processes. The proposed monitoring method was applied to fault detection in the simulation benchmark of fed-batch penicillin production. In both off-line analysis and on-line batch monitoring, the proposed approach can effectively capture the nonlinear relationships among process variables. In on-line monitoring, MKPCA can detect significant deviation which may cause a lower quality of final products. MPCA, however, has a limit to detect faults.

Continuous quality assurance (QA) in health care has necessitated the adoption of statistical methods developed as industrial process monitoring techniques. One such statistical technique is the cumulative summation (Cusum) methodology, which can monitor continuously a production process and detect subtle deviations from a preset defined level of achievement. The method is practical, simple to apply, easy to introduce and has proved popular with trainees in some specialities. This article introduces the concepts of a sequential analysis, deals with the practical steps of setting up a data collection and monitoring performance for procedures in health care.

In this paper, we propose a new approach of fault detection and diagnosis combining a Neural Nonlinear Principal Component Analysis (NNLPCA) and Partial Least Square (PLS). We have made a comparative study between the Linear Principal Component Analysis (LPCA) and Nonlinear Principal Component Analysis (NLPCA) to monitor a manufacturing process. This study has shown the capability of NLPCA in explaining nonlinear correlations in the process data. The traditional LPCA is limited to complex nonlinear systems; therefore, an adaptive NLPCA based on an improved training auto-associative neural network is presented. The proposed approach is applied to fault detection of a manufacturing process. The performance of the proposed approach is then illustrated and compared to those of classic LPCA.

In this contribution three aspects of miniaturized total analysis systems (mTAS) are described and discussed in detail. First, an overview of microfabricated components for fluid handling is given. A description of the importance of sampling-and fluid-handling techniques is followed by details of microvalves, micropumps and micro flowchannels. Secondly, the problems associated with system integration are discussed. As a solution for the realization of microfluidic-and micro analysis systems, the concept of a planar mixed circuit board (MCB) as a platform for the integration of different components is described. In addition, the design, modeling and simulation, and realization of several components in the form of standard modules for integration on a MCB is described. As an illustration of the potential of this approach, the realization of a mTAS demonstrator for the optical detection of the pH change of a pH indicator, is presented. Finally, a number of different applications of mTAS are described, such as on-line process monitoring, environmental monitoring, biomedical and space applications and DNA-analysis.

Multivariate statistical process control (MSPC) has been successfully applied to chemical processes. In order to improve the performance of fault detection, two kinds of advanced methods, known as moving principal component analysis (MPCA) and DISSIM, have been proposed. In MPCA and DISSIM, an abnormal operation can be detected by monitoring the directions of principal components (PCs) and the degree of dissimilarity between data sets, respectively. Another important extension of MSPC was made by using multiscale PCA (MS-PCA). In the present work, the characteristics of several monitoring methods are investigated. The monitoring performances are compared with using simulated data obtained from the Tennessee Eastman process. The results show that the advanced methods can outperform the conventional method. Furthermore, the advantage of MPCA and DISSIM over conventional MSPC (cMSPC) and that of the multiscale method are combined, and the new methods known as MS-MPCA and MS-DISSIM are proposed.

The Self-Organizing Map (SOM) is a powerful neural network method for analysis and visualization of high-dimensional data. It maps nonlinear statistical dependencies between high-dimensional measurement data into simple geometric relationships on a usually twodimensional grid. The mapping roughly preserves the most important topological and metric relationships of the original data elements and, thus, inherently clusters the data. The need for visualization and clustering occur, for instance, in the analysis of various engineering problems. In this paper, the SOM has been applied in monitoring and modeling of complex industrial processes. Case studies, including pulp process, steel production, and paper industry are described.

Acoustic measurement techniques are being developed to monitor the state of equipment and the physicochemical changes within chemical engineering processes. The advantage of acoustics is that unlike other techniques, direct contact with the process under investigation is not required so intrusion can be kept to a minimum. Most research concentrates on the active use of low-powered ultrasound, i.e. the generation of an acoustic wave and the measurement of any changes in the wave. This review highlights the use of passive measurement of acoustic emissions created by a process as a potentially non-invasive, real-time monitoring technique to be used in process control. Research into the acoustic emission sources found in several chemical engineering processes such as gas}liquid mixing, solid systems and chemical reactions and the process information that can be obtained from them is discussed.

In many ways electrophoretic techniques appear ideal for quality monitoring of proteins and are thus well suited for the analysis of recombinant glycoproteins. The requirements of high throughput, comparative analysis and resolution of many variants are met by several electrophoretic techniques. A wide variety of such techniques are available to biotechnologists in the rapidly developing area of recombinant glycoproteins. It is the aim of this review to specifically cover recent work which has been applied to the analysis of DNA-derived glycoproteins, both from a process control standpoint and final product validation. All major areas of electrophoresis including sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), isoelectric focusing and techniques utilizing capillary electrophoresis are covered, with emphasis on analysis of glycoforms and oligosaccharide profiles of recombinant glycoproteins. As illustration, actual examples rather than standard glycoproteins are given to indicate the potential and limitations which may be encountered. It is anticipated that this review will prove a useful and practical guide to the latest developments by indicating the relevant merits of different methods.

Market demand places great emphasis in industry on product quality. Consequently, process monitoring and control have become important aspects of systems engineering. In this article we detail the results of a 2-year study focusing on the development of a condition monitoring system for a fed-batch fermentation system operated by Biochemie Gmbh in Austria. We also demonstrate the suitability and limitations of current state of the art technologies in this field and suggest novel modifications and configurations to improve their suitability for application to a fed-batch fermentation system.

While remarkable progress has been made in developing sensors for metal ions such as Ca(II) and Zn(II), designing and synthesizing sensitive and selective metal ion sensors remains a significant challenge. Perhaps the biggest challenge is the design and synthesis of a sensor capable of specific and strong metal binding. Since our knowledge about the construction of metal-binding sites in general is limited, searching for sensors in a combinatorial way is of significant value. Therefore, we have been able to use a combinatorial method called in vitro selection to obtain catalytic DNA that can bind a metal ion of choice strongly and specifically. The metal ion selectivity of the catalytic DNA was further improved using a 'negative selection' strategy where catalytic DNA that are selective for competing metal ions are discarded in the in vitro selection processes. By labeling the resulting catalytic DNA with a fluorophore/quencher pair, we have made a new class of metal ion fluorescent sensors that are the first examples of catalytic DNA biosensors for metal ions. The sensors combine the high selectivity of catalytic DNA with the high sensitivity of fluorescent detection, and can be applied to the quantitative detection of metal ions over a wide concentration range and with high selectivity. The use of DNA sensors in detection and quantification of lead ions in environmental samples such as water from Lake Michigan has been demonstrated. DNA is stable, cost-effective, environmentally benign, and easily adaptable to optical fiber and microarray technology for device manufacture. Thus, the DNA sensors explained here hold great promise for on-site and real-time monitoring of metal ions in the fields of environmental monitoring, developmental biology, clinical toxicology, wastewater treatment, and industrial process monitoring. #

Abstract}In this work extensions to principal component analysis (PCA) for wastewater treatment (WWT) process monitoring are discussed. Conventional PCA has some limitations when used for WWT monitoring. Firstly, PCA assumes that data are stationary, which is normally not the case in WWT monitoring. Secondly, PCA is most suitable for monitoring data that display events in one time-scale. However, in WWT operation, disturbances and events occur in different time-scales. These two limitations make conventional PCA unsuitable for WWT monitoring. The first limitation can be overcome by use of adaptive PCA. In adaptive PCA, the PCA model is continuously updated using an exponential memory function. Variable mean, variance and co-variance are thus adapted to the changing conditions. The second problem can be solved by time-scale decomposition of data prior to analysis. The time-scale decomposition methodology involves wavelets and multiresolution analysis (MRA) in combination with PCA. MRA provides a tool for investigation and monitoring of process measurement at different timescales by decomposing measurement data into separate frequency bands. Time-scale decomposition increases the sensitivity of the monitoring, which makes it possible to detect small but significant events in data displaying large variations. Moreover, time-scale information is sometimes important in the interpretation of a disturbance to determine its physical cause. Also, by decomposing data, the problem of changing process conditions is partly solved. All the presented methods are illustrated with examples using real WWT process data. #

This paper describes an in-depth study on the development of a system for monitoring tool wear in hard turning. Hard turning is used in the manufacturing industry as an economic alternative to grinding, but the reliability of hard turning processes is often unpredictable. One of the main factors affecting the reliability of hard turning is tool wear. Conventional wear-monitoring systems for turning operations cannot be used for monitoring tools used in hard turning because a conglomeration of phenomena, such as chip formation, tool wear and surface finish during hard turning, exhibits unique behavior not found in regular turning operations. In this study, various aspects associated with hard turning were investigated with the aim of designing an accurate tool wearmonitoring system for hard turning. The findings of the investigation showed that the best method to monitor tool wear during hard turning would be by means of force-based monitoring with an Artificial Intelligence (AI) model. The novel formulation of the proposed AI model enables it to provide an accurate solution for monitoring crater and flank wear during hard turning. The suggested wear-monitoring system is simple and flexible enough for online implementation, which will allow more reliable hard turning in industry.

This paper presents the results of our study of the permanent-magnet synchronous motor (PMSM) running under demagnetization. We examined the effect of demagnetization on the current spectrum of PMSMs with the aim of developing an effective condition-monitoring scheme. Harmonics of the stator currents induced by the fault conditions are examined. Simulation by means of a two-dimensional finite-element analysis (FEA) software package and experimental results are presented to substantiate the successful application of the proposed method over a wide range of motor operation conditions. Methods based on continuous wavelet transform (CWT) and discrete wavelet transform (DWT) have been successfully applied to detect and to discriminate demagnetization faults in PMSM motors under nonstationary conditions. Additionally, a reduced set of easy-to-compute discriminating features for both CWT and DWT methods has been defined. We have shown the effectiveness of the proposed method by means of experimental results.

This paper provides a review of active materials in the context of applications to manufacturing machining processes. The important concepts and background of active materials are briefly introduced. After which, the applications of these materials are discussed as applied to relevant themes in machining processes. A brief overview of research work on experimental and theoretical studies on various process monitoring

Two drying methods of cranberries (microwave-vacuum and microwave-convective) are reviewed, and their advantages and disadvantages regarding the quality of dried product and the process performance are presented. Mechanically and osmotically pretreated berries were subjected to drying and quality evaluation. Quality parameters are color (in Hunter L à a à b à coordinates), textural characteristics (toughness and Young's modulus), and organoleptic *Correspondence: G.

Univariate and multivariate statistical process control (USPC and MSPC) methods have been widely used in process industries for fault detection. However, their practicability and achievable performance are limited due to the assumptions that a process is operated in a steady state and that variables are normally distributed. In the present work, external analysis is proposed to distinguish from faults from normal changes in operating conditions. To further improve the monitoring performance, a new MSPC method based on independent component analysis (ICA) is proposed. The simulation results of a CSTR process have clearly shown the superiority of the proposed ICA-based SPC over USPC and PCA-based SPC, and also the usefulness of external analysis.

The present study aimed to delineate the extent to which unitary executive functions might be shared across the separate domains of episodic and working memory. A mixed blocked/event-related functional magnetic resonance imaging (fMRI) design was employed to assess sustained (tonic control) and transient (phasic control) brain responses arising from incrementing executive demand (source versus item episodic memoryvis-à-vistwo-back versus one-back working memory) using load-dependent activation overlaps as indices of common components. Although an extensive portion of the regional load effects constituted differential control modulations in both sustained and transient responses, commonalities were also found implicating a subset of executive core mechanisms consistent with unitary or domain general control. 'Unitary' control modulations were temporally dissociated into (1) shared tonic components involving medial and lateral prefrontal cortex, striatum, cerebellum and superior parietal cortex, assumed to govern enhanced top-down context processing, monitoring and sustained attention throughout task periods and (2) stimulus-synchronous phasic components encompassing posterior intraparietal sulcus, hypothesized to support dynamic shifting of the 'focus of attention' among internal representations. Taken together, these results converge with theoretical models advocating both unity and diversity among executive control processes.

The MonALISA (Monitoring Agents in A Large Integrated Services Architecture) system provides a distributed service architecture which is used to collect and process monitoring information. While its initial target field of application is networks and Grid systems supporting data processing and analysis for global high energy and nuclear physics collaborations, MonALISA is broadly applicable to many fields of "data intensive" science, and to the monitoring and management of major research and education networks. MonALISA is based on a scalable Dynamic Distributed Services Architecture), and is implemented in Java using JINI and WSDL technologies. The scalability of the system derives from the use of a multi threaded engine to host a variety of loosely coupled self-describing dynamic services, the ability of each service to register itself and then to be discovered and used by any other services, or clients that require such information. The framework integrates many existing monitoring tools and procedures to collect parameters describing computational nodes, applications and network performance. Specialized mobile agents are used in the MonALISA framework to perform global optimization tasks or help and improve the operation of large distributed system by performing supervising tasks for different applications or real time parameters. MonALISA is currently running around the clock monitoring several Grids and distributed applications on around 160 sites.

Fifteen micro-fermentation trials were conducted during the 2008 vintage harvest in the Valtellina (Northern Italy) viticultural area. During fermentation, the spectra were achieved in the near and mid-infrared region by a FT-NIR spectrometer and a FT-IR spectrometer, respectively. Samples were also analysed by using chemical methods to evaluate sugars (glucose and fructose), alcohols (ethanol and glycerol) and phenolic compounds (total phenolics, total anthocyanins and total flavonoids). The pretreated spectral data were processed using principal component analysis. After feature selection by the algorithm SELECT, linear discriminant analysis (LDA) was applied to spectral data as a classification technique, to predict the fermentation stage from initial to final phase. Moreover, partial least square regression was used to predict sugar content, ethanol, glycerol and phenolic compounds simultaneously. LDA results, characterised by a high percentage of correct classification (87% and 100% as average value in prediction for NIR and MIR spectroscopy, respectively), showed that samples belonging to a particular fermentation step could be correctly classified. Good calibration models for the prediction of the main compositional changes during alcoholic fermentation were obtained with both FT-NIR and FT-IR, suggesting that either instruments could be used to evaluate online and simultaneously these compounds in red wine.

The process monitoring and mechanics of fixed abrasive diamond wire saw machining are investigated in this study. New techniques to affix diamond particles to a steel wire core have advanced to make this process feasible for the machining of ceramics, wood, and foam materials. Developments in fixed abrasive diamond wire machining are first reviewed. Advantages of using fixed abrasive diamond