Process Monitoring

Communications Systems (PACS) as the final step towards a completely digital clinical environment completing the HIS/EMR and 1,400 web/terminals for patient information access. Financial benefits throughout the hospital were assessed upfront and measured periodically. Key indicators (radiology exam turnaround time, number of radiology procedures performed, inpatients length of stay before and after the PACS implementation, etc.) were analyzed and values were statistically tested to assess workflow and productivity improvements. The hospital went Bfilmless^in 28 weeks. Between the half of 2004 and the respective period in 2003, overall Radiology Department productivity increased by 12%, TAT improved by more than 60%. Timelier patient care resulted in decreased lengths of stay. Neurology alone experienced a 12% improvement in average patient stay. To quantify the impact of PACS on the average hospital stays and the expected productivity benefits to inpatient productivity were used a Bhigh level^and a Bdetailed^business model. Annual financial upsides have exceeded $1.9 millions/year. A well-planned PACS deployment simplifies imaging workflow and improves patient care throughout the hospital while delivering substantial financial benefits. Staff buyin was the key in this process and on-going training and process monitoring are a must.

To achieve desired accuracy, precision and surface roughness during laser-material removal process, monitoring and control of the process parameters related to laser, optics, workpiece material and its motion are required. Focus position, defined as a gap between the focusing lens and the surface of the sample workpiece, is one of the most critical process parameters, which determines the projection of the intensity of the laser beam on the surface to be ablated and therefore directly affects volume and geometry of the material removed and there by machining quality. In this paper, acoustic emission (AE) generated by laser-material interactions was statistically analyzed with respect to the variations in the focus position. The study involved on-line measurements of the AE signal from the laser-material interaction zone as a function of the focus position and the width of the machined trenches. Several basic statistical parameters, e.g. average amplitude, variance and power spectrum density were analyzed to select distinct informational parameters. Pattern recognition analysis of three informational parameters based on variances within frequency diapasons of 20-180, 180-300, and 300-500 kHz was used for reliable classification of the focus position and width of the machined trenches. The results provide important information for future development of on-line monitoring and control systems for laser-material removal process. Crown

Many multivariate techniques have been applied to diagnose faults such as Principal Component Analysis (PCA), Fisher's Discriminant Analysis (FDA), and Discriminant Partial Least Squares (DPLS). However, it has been shown that FDA and DPLS are more proficient than PCA for diagnosing faults. And recently applying kernel on FDA which is called KFDA (Kernel FDA) has showed outperformance than linear FDA based method. We propose in this research work an advanced KFDA for faults classification with Building knowledge base for faults structure using FSN. A case study is done on a chemical G-Plant process, constructed and experimental runs are done in Okayama University, Japan. The results are showing improving performance of fault detection rate for the new model over FDA.

Biodiesel, an alternative diesel fuel made from renewable sources, is produced by the transesterification of oil or fat with alcohol. In order to monitor the progress of this reaction, in situ viscosity measurements were taken using an acoustic wave solid state viscometer. This novel concept is reported from the proof-of-concept stage to a pilot plant installation. The viscometer was able to monitor the reaction until the end-point was reached, and could therefore be adapted in the future for process control in a batch transesterification reactor for biodiesel production.

Monitoring business activities using Business Intel- ligence (BI) tools is a well-established concept. How- ever, online process monitoring is an emerging area which helps organizations not only plan for future im- provements but also change and alter their current ongoing processes before problems happen. In this paper, we explore how monitoring process perfor- mance can help evolve process goals and require- ments. We elaborate an approach that uses the User Requirements Notation (URN) to model the goals and processes of the organization, and to monitor and align processes against their goals. A BI tool exploit- ing an underlying data warehouse provides the Key Performance Indicators (KPI) used to measure the satisfaction of goals and process requirements. Feed- ing this information into the URN modeling tool, we can analyze the consequences of current business ac- tivities on desired business goals, which can be used for process and business activity alignment thereafter. We illu...

A partially embedded long fibre-Bragg grating (FBG) into an epoxy cylindrical specimen is used to monitor the strain distribution evolution along its axis when subjected to subsequent cycles of thermal ageing at 70°and 110°C. Assuming a total volume reduction of 2.5% at full cure, the epoxy conversion at the end of the thermal cycles was found $85%. Using strain data from the FBG, the coefficient of thermal expansion was evaluated as $11.5 Â 10 À5 C À1. Based on the strain data from the FBG, numerical simulations of an equivalent thermo-elastic matrix were performed to obtain the residual stresses in the specimen at the end of the thermal cycles. The results demonstrate the capability of FBG sensors in providing important information on the degree of cure and evolution of fabrication induced strains inside the epoxy.

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 b s t r a c t Some industrial processes frequently change due to various factors, such as alterations of feedstocks and compositions, different manufacturing strategies, fluctuations in the external environment and various product specifications. Most multivariate statistical techniques are under the assumption that the process has one nominal operation region. The performance of it is not good when they are used to monitor the process with multiple operation regions. In this paper, we developed an effective approach for monitoring multi-mode continuous processes with the following improvements. 1). Offline mode identification algorithm is proposed to identify (i) stable modes, (ii) transitional modes between two stable modes, and (iii) noise. 2). According to the data distribution, proper multivariate statistical algorithm is selected automatically to realize fault detection for each mode. 3). When online monitoring, the right model is chosen based on Mode Transformation Probability (MTP), which makes full use of the empirical knowledge hidden in offline data. This method can enhance realtime performance of online mode identification for continuous process and timely monitoring can be further realized. The proposed method is illustrated by application in furnace temperature system of continuous annealing line. The effectiveness of mode identification and fault detection is demonstrated in the results.

Nylon 6/clay nanocomposites were studied by dielectric relaxation spectroscopy (DRS) to correlate morphology and microstructure with relaxation behavior of the polymer matrix at the molecular level. Partially exfoliated clay microstructure was achieved by extruding nylon 6 with surfactant-treated montmorillonite clays. A new on-line dielectric slit die sensor was used to examine the melt state properties during extrusion compounding. Solid state properties were probed by off-line DRS over a temperature range from K50 to 180 8C in a frequency range from 10 K3 to 10 6 Hz. Using non-linear regression methods in conjunction with the temperature-frequency positions of relaxations observed in the dielectric loss data, the experimental data were fit with the Havriliak-Negami and Cole-Cole dielectric relaxation functions corrected for electrode polarization and DC conductivity. Characteristic frequency, relaxation strength, and DC conductivities were extracted from curves with overlapping relaxation modes. Two dielectric relaxations were observed in the composite melt: the a relaxation associated with molecular segmental motion, and a Maxwell-Wagner relaxation (MW) resulting from interfacial polarization at the resin/clay interface. Analysis of the solid-state data yielded a comprehensive master plot of dielectric relaxations attributed to segmental and local molecular dynamics and other relaxations resulting from water and Maxwell-Wagner interfacial polarization. The impact of clay fillers is seen in nearly all relaxation processes changing both characteristic frequency and strength of the relaxation. Published by Elsevier Ltd.

Research into surface acoustic wave (SAW) devices began in the early 1970s and led to the development of high performance, small size, and high reproducibility devices. Much research has now been done on the application of such devices to consumer electronics, process monitoring, and communication systems. The use of novel materials, such as gallium phosphate (GaPO4), extends the operating temperature of the elements. SAW devices based on this material operating at 434 MHz and up 800 C, can be used for passive wireless sensor applications. Interdigital transducer (IDT) devices with platinum/zirconium metallization and 1.4 m finger-gap ratio of 1:1 have been fabricated using direct write e-beam lithography and a lift-off process. The performance and long-term stability of these devices has been studied, and the results are reported in this paper.

Oscillations in process control loops present a common problem. This paper proposes a new algorithm to detect valve stiction for diagnosis of oscillation. Formalism for the qualitative description of input-output characteristics of a valve is also proposed for implementation of the detection algorithm. The method was evaluated on several industrial data sets. It showed excellent performance for detection of stiction. r

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

This paper examines the issue of instrumentation for civil structures and in particular discusses the role of sensing systems in the evolution of the smart building concept. The sheer size of most structures of interest presents its own technological challenge in designing a suitable sensing architecture which will both conveniently address the area to be covered and will also provide sufJicient sensing points to adequately characterize the structure under test. In addition, the measurement problem itselJ; which comprises both process monitoring and in use assessment must be dejined. The former is very much process speci$c and m y range from assessing whether concrete is dry to ensuring that protective materials are properly installed. The latter is a l m s t entirely concerned with providing an alarm for either the onset of unacceptable corrosion within the structure or the appearance of physical damage for example due to foundation failure. This paper presents an analysis of the principal issues which the civil engineering smart structure must address and derives generic system specijications. It continues to argue thatjber optic solutions are the most appropriate sensing technologies and examines a number of distributed jiber optic technologies which enable both physical and chemical parameters to be addressed throughout a large structure.

This study analyzes a low-cost reliable real-time optimal monitoring platform for fused filament fabrication-based open source 3-D printing. An algorithm for reconstructing 3-D images from overlapping 2-D intensity measurements with relaxed camera positioning requirements is compared with a single camera solution for single side 3-D printing monitoring. The algorithms are tested for different 3-D object geometry and filament colors. The results showed that both of the algorithms with a single and double camera system were effective at detecting a clogged nozzle, incomplete project, or loss of filament for a wide range of 3-D object geometries and filament colors. The combined approach was the most effective and achieves 100 percent detection rate for failures. The combined method analyzed here has a better detection rate and a lower cost compared to previous methods. In addition, this method is generalizable to a wide range of 3-D printer geometries, which enables further deployment of desktop 3-D printing as wasted print time and filament are reduced, thereby improving the economic advantages of distributed manufacturing.

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.

A major problem confronting American manufacturers todayishowtoeconomically achievehigh qualityin the goodswhich they manufacture. Equipment reliability and maintenance drastically affect the three key elements of competitiveness: quality, cost and product lead time. Well-maintained machines hold tolerances better, help reduce scrap and rework, and raise consistency and quality of the part. They increase uptime and yields of good parts, thereby cutting total production costs, and also can shorten lead timesby reducing downtime and the need for retooling. The recent rush to embrace computer-integrated manufacturing (elM) has further increased the use of relatively unknown and untested technology. In a computcr-integrated manufacturing system,a fault can cause machine and production downtime and create serious productivity losses. Today, many factories are still performing maintenance on equipment in a reactive, or breakdown, mode, since traditional process monitoring systems can detect machine or process faults only when they occur. Reactive maintenance is expensive because of extensive unplanned downtime and damage to machinery. In high-performance systems one often cannot tolerate significant degradation in performance during normal system operation. This had lcd many US manufacturers to look to suppliers for smarter equipment that will ease the need for strong technical support. The proactive maintenance approach is to use integrated, investigative and corrective practices to significantly extend machinery life. The goal of proactive maintenance is to eliminatc failures of equipment forever. If the behaviour of a machine can be monitored adaptively then an early warning of possible faults can be generated. By doing this, maintenance personnel can perform early diagnostics and part replacement during regular daily maintenance hours. Therefore, the mean-timebetween-failure can be extended to an unlimitedlength. The author believes that the development orin-process monitoring of machine degradation and faults is one of the most important research tasks for increasing machine uptime and improving production quality. This paper examines various methods in machine monitoring, fault detection, and fault diagnostics. A perspective on proactive maintenancc and research needs is also presented.

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.

The focus of this paper is on improving the monitoring and control of end-use quality variables in batch nylon 6,6 autoclaves. Relatively few on-line measurements and frequent disturbances often result in a significant amount of variability in the product quality variables for this system. To lessen the sensitivity to these disturbances, a fundamental model of the nylon autoclave process is developed from first principles and is subsequently used to develop and test practical reactor control configurations. Various PID implementations are evaluated according to their robustness to typical process disturbances. The model is also used to formulate strategies designed to detect process disturbances and monitor the development of the quality variables on-line. The process monitoring results are then used in conjunction with inferential quality control strategies to improve robustness to typical process disturbances.

Cognitive fit theory, along with the proximity compatibility principle, is investigated as a basis to evaluate the effectiveness of information visualizations to support a decision-making task. The task used in this study manipulates varying levels of task complexity for quality control decisions in a high-volume discrete manufacturing environment. The volume of process monitoring and quality control data produced in this type of environment can be daunting. Today's managers need effective decision support tools to sort through the morass of data in a timely fashion to make critical decisions on product and process quality.

Recent advances in the development of sensors based on infrared diode and quantum cascade lasers for the detection of trace gas species is reported. Several examples of applications in environmental and industrial process monitoring as well as in medical diagnostics using quartz enhanced photoacoustic spectroscopy and laser absorption spectroscopy will be described. Keywords: Trace gas detection, near infrared diode lasers, mid infrared quantum and interband cascade lasers, quartz enhanced photoacoustic spectroscopy, laser absorption spectroscopy.

Inappropriate machining conditions such as cutting forces cause tool failures, poor surface quality and worst of all machine breakdowns. This may be avoided by using optimal machining parameters, e.g. feed-rate, and continuing to monitor it throughout the machining process. To optimize feed-rate, we propose a system that consists of an optimisation module, a process control module and a knowledge based evaluation module. STEP-NC is the underlying data model for optimisation. Given the nominal powers, the cutting force can be estimated based on the higher-level production information such as workpiece properties, tool materials and geometries, and machine capabilities. The main function of the Process Control module is process monitoring and control. The output is the desired actual feed-rate. Finally, the actual feed-rate is recorded and evaluated in the Knowledge Based Evaluation module.

The qualitative analysis of complex process systems is an important task at the design of control and process monitoring algorithms. Qualitative models require interpretable description of the operating regimes of the process. This work shows a novel approach to discover and isolate operating regimes of process systems based on process models, time series analysis, and decision tree induction technique. The novelty of this approach is the application of time series segmentation algorithms to detect the homogeneous periods of the operation. Advanced sequence alignment algorithm used in bioinformatics is applied for the calculation of the similarity of the process trends described by qualitative variables. Decision tree induction is applied for the transformation of this hidden knowledge into easily interpretable rule base to represent the operation regions of the process. The whole methodology is applied to detect operating regimes of an industrial fixed bed tube reactor.

This work focuses on developing an application based on the information contained in the numerical control (NC) kernel for surface roughness monitoring of the part in process. A human-machine interface (HMI) was developed in order to facilitate the interaction between the operator and the NC kernel with a graphical user interface working in the computer numerically controlled (CNC) screen. Experimentation was carried out in order to obtain the data to be modeled with artificial neural networks for surface roughness average parameter (Ra) predictions. Finally, a compact solution was implemented through global user data (GUD). Data from the HMI and from the kernel are collected in the GUD and analyzed with the artificial neural network. The application provides the surface roughness average parameter of the part in process and gives optimized parameters to the operator. Verification tests were carried out, showing accurate results. The use of the application developed in this research ensures the surface roughness Ra requirement, improves cutting parameters, reduces manual finishing operations and unacceptable parts at the end of the manufacturing process, and provides a solution implemented in the machine tool CNC screen without the need of any other external sensors.

Slope movements (e.g. landslides) are dynamic systems that are complex in time and space and closely linked to both inherited and current preparatory and triggering controls. It is not yet possible to assess in all cases conditions for failure, reactivation and rapid surges and successfully simulate their transient and multi-dimensional behaviour and development, although considerable progress has been made in isolating many of the key variables and elementary mechanisms and to include them in physically-based models for landslide hazard assessments. Therefore, the objective of this paper is to review the state-of-the-art in the understanding of landslide processes and to identify some pressing challenges for the development of our modelling capabilities in the forthcoming years for hazard assessment. This paper focuses on the special nature of slope movements and the difficulties related to simulating their complex time-dependent behaviour in mathematical, physically-based models. It analyses successively the research frontiers in the recognition of first-time failures (pre-failure and failure stages), reactivation and the catastrophic transition to rapid gravitational processes (post-failure stage). Subsequently, the paper discusses avenues to transfer local knowledge on landslide activity to landslide hazard forecasts on regional scales and ends with an outline how geomorphological investigations and supporting monitoring techniques could be applied to improve the theoretical concepts and the modelling performance of physically-based landslide models at different spatial and temporal scales.

Over the last years Wireless Sensor Networks (WSN) have been becoming increasingly applicable for real world scenarios and now production ready solutions are available. In the same period the upcoming combination of Service-oriented Architectures and Web Service technology demonstrated a way to realize open standardized, flexible, service component based, loosely coupled and interoperable cross domain enterprise software solutions. But those solutions have been too resource-intensive and complex to be applicable for limited devices like wireless sensor nodes or small-sized embedded systems. Thus, more and more research investigations have been launched to bring the aspect of cross domain interoperability to the field of embedded battery powered devices. The proposed laboratory assistance solution in this paper demonstrates the benefits of Web Service enabled WSNs for process monitoring and disaster management by extending an existing system in the Life Science Automation domain. Especially, the capability to provide location based services in industrial automation environment represents a beneficial feature of the presented integration approach and results in high-quality information delivery bundled with specific data about the locational origin of the capturing sensor.

A mixed-integer linear programming formulation for the design of sensor networks for simultaneous process monitoring and fault detection/resolution is presented. The objective is to find a cost-optimal sensor set for a chemical process that provides a good estimate of the state of the system and detects as well as locate a preestablished set of faults. A continuously stirred tank reactor example is provided and extensions are discussed. ©

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

On-line analyzers can provide accurate and timely information for process control and monitoring. Statistical Process Control (SPC) techniques can be effectively utilized to support the development and maintenance of these tools. The D6299- 10 ASTM standard details how on-line analyzers should be validated. The applicability of this standard is demonstrated through the analysis of industrial data collected from an on-line gas chromatograph. The results confirm that automatized SPC can effectively improve the reliability of advanced process control systems.

Peptide mapping is a key analytical method for studying the primary structure of proteins. The sensitivity of the peptide map to even the smallest change in the covalent structure of the protein makes it a valuable 'finger-print' for identity testing and process monitoring. We recently conducted a full method validation study of an optimised reverse-phase high-performance liquid chromatography (RP -HPLC) tryptic map of a therapeutic anti-CD4 IgG1 monoclonal antibody. We have used this method routinely for over 1 year to support bioprocess development and test production lots for clinical trials. Herein we summarize the precision and ruggedness of the testing procedure and the main findings with respect to 'coverage of amino acid sequence' and limits-of-detection for various hypothetical www.elsevier.com/locate/jpba Abbre6iations: AAA, amino acid analysis; DTT, dithithreitol; Glu-C, endoproteinase Glu-C; IgG1, immunoglobulin G1; LC/ESI-MS, liquid chromatography electrospray-ionization mass spectrometry; LOD, limit-of-detection; Lys-C, endoproteinase Lys-C; MALDI-TOF MS, matrix-assisted laser-desorption mass spectrometry; Met(O), methionine sulfoxide; PCA, principal component analysis; pGlu, pyroglutamic acid; PTH, phenylthiohydantoin; RP -HPLC, reverse-phase high-performance liquid chromatography; S/E, substrate-to-enzyme ratio (weight-to-weight); TFA, trifluoroacetic acid; TPCK, Tosyl-L-phenylalanine chloromethyl ketone. : S 0 7 3 1 -7 0 8 5 ( 9 9 ) 0 0 1 8 1 -8 structural variants. We also describe, in more detail, two unanticipated insights into the method gained from the validation study. The first of these is a potentially troublesome side-product arising during the reduction/alkylation step. Once the cause of this side-product was identified, it was easily prevented. We also report on subtle changes to the peptide map upon extended storage of the digest in the autosampler. These findings helped us to develop a 'robust' method for implementation in a quality control laboratory.

This paper presents a method to detect metallurgical short circuits suitable for Optibar intercell bars in copper electrowinning and electrorefining processes. One of the primary achievements of this bar is to limit short-circuit currents to a maximum of 1.5 p.u. of the actual process current. However, low-current short circuits are more difficult to detect. Thus, conventional short-circuit detection instruments like gaussmeters and infrared cameras become ineffective. To overcome this problem, the proposed method is based on detecting the voltage drop across anode-cathode pairs. The method does not affect the operation of the process and does not require modifications of the industrial plant. In order to verify the performance of this proposal, experimental measurements done over a period of four months at a copper refinery are presented. A 100% success rate was obtained.

A novel antioxidative assay based on direct current polarography has been developed. Quantification of antioxidative (AO) activity has been based on a decrease of hydrogen peroxide anodic current in the presence of antioxidants. An efficient experimental procedure, without any special pretreatment of analyzed samples, has been applied. Antioxidative activity of different kinds of commercial beers (dark, blond, and alcohol-free), some small-scale made special beers with medicinal herbs and mushroom extracts, extracts themselves, as well as individual phenolic components present in beer has been measured. In addition, changes of AO activity during the full-scale industrial process of beer production have been monitored. A strong correlation between results obtained and total phenolics content has been observed. The assay can be recommended for application in brewing industry, either to survey a process with the aim to optimize relevant technological factors or to analyze quality of final product.

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.