Process Monitoring System Research Articles

Establishment of "A-PPNS", A Navigation System for Regenerating the Software Development Business

Currently, knowledge within the field of software development is largely implicit and is not formally disseminated and shared. This means that there is little improvement and regeneration of processes, and knowledge gained from previous projects is not necessarily applied to new ones. In order to turn this situation around it is necessary to take an organized approach to sharing job-related information. For this study, the authors constructed "Amalab-Project Planning Navigation System, or A-PPNS", a system for organizing accumulated knowledge related to the field of software development. More specifically, A-PPNS is a business process monitoring system and consists of the following four elements: (i) Optimized estimate support subsystem, (ii) Schedule monitoring system, (iii) QCD optimization diagnostic system, and (iv) Strategic technology accumulation system. The effectiveness of this system has been demonstrated and verified at Company A, a system integration company.

Development of process monitoring system in drilling process using fuzzy rules

This study describes the development of process monitoring system in drilling process in high speed machining center. The system monitors the several state variables of the cutting process of a drill. However, the thrust force and torque signals have been chosen to detect and monitor the drilling process, the acoustic emission signal has been also analyzed. Experimental data have allowed defining statistical behavior of the variables for non-fault conditions, tool wear and breakage. The new approach in this project is to use the standard data acquisition software LabVIEW for the purpose of data collection, signal analysis, decision making and sending back the message to the machine.

Signal estimation and change detection in tank data for nuclear safeguards

Process monitoring (PM) is increasingly important in nuclear safeguards as a complement to mass-balance based nuclear materials accounting (NMA). Typically, PM involves more frequent but lower quality measurements than NMA. While NMA estimates special nuclear material (SNM) mass balances and uncertainties, PM often tracks SNM attributes qualitatively or in the case of solution monitoring (SM) tracks bulk mass and volume. Automatic event marking is used in several nuclear safeguards PM systems. The aims are to locate the start and stop times and signal changes associated with key events. This paper describes results using both real and simulated SM data to quantify the errors associated with imperfect marking of start and stop times of tank events such as receipts and shipments. In the context of safeguards, one can look both forward and backward in modest time intervals to recognize events. Event marking methods evaluated include differencing, multi-scale principal component analysis using wavelets, and piecewise linear regression (PLR). All methods are evaluated on both raw and smoothed data, and several smoothing options are compared, including standard filters, hybrid filters, and local kernel smoothing. The main finding for real and simulated examples considered is that a two-step strategy is most effective. First, any reasonably effective initial smoother is used to provide a good initial guess at change point locations. Second, PLR is applied, looking for one change point at a time. In contrast, PLR that allows for multiple change points simultaneously has worse performance.

Performance parameter prediction for sewage sludge digesters using reflectance FT-NIR spectroscopy

This study investigates the use of Fourier transform near infrared (FT-NIR) spectroscopy combined with principle components analysis (PCA) and partial least squares regression (PLS-R) as part of a possible process monitoring system for sewage sludge anaerobic digesters. The ability of FT-NIR with PCA to distinguish between different stages of the AD process was investigated, it was found that waste activated sludge (WAS), primary, feed (Primary:WAS 70:30) and digested sludge were distinguishable from each other using this technique. PLS-R was used successfully to track differing proportions of primary:WAS in feedstocks of 5% total solids (Coefficient of Efficiency (CE) = 0.93). The study also looked at the ability of reflectance mode NIR spectroscopy to track process parameters important for stability. Temperature and organic loading rate variations were employed to stress the digesters. Predictive models were produced for volatile fatty acids (VFA), bicarbonate alkalinity (BA) and total and volatile solids (TS and VS) and independently validated for each digester. The models were able to track the relevant process parameters: TS (CE = 0.75), VS (CE = 0.75), BA (CE = 0.71), and VFA (CE = 0.69). This technique could be used to improve the performance of sewage sludge anaerobic digesters.

Virtual Factory Integrated Manufacturing System for Process Simulation and Monitoring

AbstractA virtual factory integrated manufacturing system combined with system simulation and virtual reality is introduced in this paper. We describe the system architecture and development methodology, provide the working principle of process simulation and monitoring in virtual factory, and then explain the detailed applications of virtual factory integrated manufacturing system. With the visualization and three-dimensional human-computer interaction about process and production data, this platform can provide effective supports on monitoring, control and operation of manufacturing system.

Voltammetry Coupled with Multiway Chemometrics for Monitoring and Diagnosis of Electrodeposition Process

AbstractA system for plating bath process monitoring and diagnosis was developed utilizing a novel approach that combines voltammetric methods with various statistical determinant analysis methods coupled with multiway data decomposition. The voltammetric methods employed were designed to provide a current response predominantly affected by the presence of specific contaminants while being practically insensitive to the varying concentrations of deliberately added bath constituents and other likely bath contaminants. The shape differences between deformed and training set voltammograms were quantified and visualized using two outlier detection chemometric techniques (Mahalanobis Distance (D) and D coupled with Q‐residuals (DQ)) coupled with three‐way decomposition PARAllel FACtor Analysis (PARAFAC) method. The specificity of the response to the presence of a likely contaminant combined with the multi‐parameter quantification of its presence enabled not only early‐fault detection but also its diagnosis, thereby automating the troubleshooting process.

Process Monitoring Evaluation and Implementation for the Wood Abrasive Machining Process

Wood processing industries have continuously developed and improved technologies and processes to transform wood to obtain better final product quality and thus increase profits. Abrasive machining is one of the most important of these processes and therefore merits special attention and study. The objective of this work was to evaluate and demonstrate a process monitoring system for use in the abrasive machining of wood and wood based products. The system developed increases the life of the belt by detecting (using process monitoring sensors) and removing (by cleaning) the abrasive loading during the machining process. This study focused on abrasive belt machining processes and included substantial background work, which provided a solid base for understanding the behavior of the abrasive, and the different ways that the abrasive machining process can be monitored. In addition, the background research showed that abrasive belts can effectively be cleaned by the appropriate cleaning technique. The process monitoring system developed included acoustic emission sensors which tended to be sensitive to belt wear, as well as platen vibration, but not loading, and optical sensors which were sensitive to abrasive loading.

사출성형기 실린더와 금형 캐비티의 실시간 모니터링을 이용한 사출성형공정 비교 분석

Recently, on-line process monitoring systems using sensors are being extensively used to produce highquality products. However, the difficulty in installing the sensors within the mold in the cases of micro-molds, optical molds, and molds with complex structures is a serious disadvantage of such process monitoring systems. In this study, the quantitative index of a process monitoring system was evaluated with the mold cavity pressure and the nozzle pressure for the injection molding machine. In order to evaluate the effect of the nozzle pressure, we performed correlation analysis for the weight of the molded product. We also examined the control characteristics of the injection molding machine by analyzing the effect of multistage injection speed, holding pressure, and injection pressure limit on the process monitoring data.

Design and simulation of roadway development operations to improve productivity

A typical Australian 3–5 Mt/year longwall requires 9 to 15 km of roadway to be developed every year (G. Lewis and G. Gibson: Proc. 13th Austr. Tunnelling Conf., Melbourne, Australia, May 2008, ATS, 317–326.). One of the major challenges faced by the Australian longwall mining industry is that roadway development has been unable to keep pace with improvements in longwall extraction rates. This has affected the profitability, viability and future of underground coal mines (M. Kelly: 'Coordination of roadway development strategy', ACARP report no. C5013, ACARP, Brisbane, Australia, 1997.). Gibson (G. Gibson: 'Australian roadway development– current practices', ACARP report no. C15005, ACARP, Brisbane, Australia, 2005.) proposed the development of an integrated suite of management tools and procedures to study how roadway development rates can be improved. A computer based process mapping, management and monitoring system would help the adoption of a systems/process control approach. The objective of this study was the development of a simulation model to better understand, analyse and improve the planning and execution of current roadway development. The models developed were able to demonstrate the utilisation of the self-drilling rock bolt technology could improve advance rate of roadway developments.

Two-Photon Polymerization of a Polymer-Ceramic Hybrid Material with a Low-Cost Nd:YAG Laser: Preliminary Resolution Study and 3D Fabrication

This paper reports the microfabrication of a polymer-ceramic hybrid material (Ormocomp®) with a low cost Nd:YAG picosecond laser system. Both the process resolution and the fabrication of three-dimensional (3D) microobjects were studied. Microstructures were fabricated on microscope slides with a moving xyz platform and a real-time process monitoring system by tracking the contours of the structure with the laser focus, followed by rinsing away the unpolymerized resin. The evaluation of the process resolution was based on the scanning electron microscope (SEM) images of socalled voxels and polymer lines. The dimensions of the produced structures varied greatly depending on the laser focus position and the process parameters used. The minimum achievable feature size was found to be approximately 1 μm. Microcylinders, cones and cone arrays were fabricated and studied with SEM imaging. The biocompatibility of the Ormocomp ®-photoinitiator combination was also demonstrated with human embryonic stem cell-derived neuronal cells. The fabricated structures could in the future be utilized in cell culture and drug delivery applications.

Application of a recent FBRM-probe model to quantify preferential crystallization of dl-threonine

The process of preferential crystallization is controlled by crystallization kinetics. Thus a key to run the process satisfactorily is using a process monitoring system eventually combined with a process model. In this context information of the liquid phase composition as well as the time resolved particle size distributions are usually desired. In contrast to measuring the liquid phase composition the inline measurement of particle size distributions is often difficult. The Focused Beam Reflectance Measurement probe (Lasentec, Mettler-Toledo GmbH) is an inline monitoring tool that is capable of measuring so-called chord-length distributions (CLDs). However these CLDs are different to the desired particle size distributions (PSDs). In the presented study this problem is overcome using a recent rigorous mathematical model of the measurement technology to transform PSDs into CLDs. The study shows how the probe model can be adopted to the investigated system using a series of initial validation experiments. Chord-splitting seems to occur when using the Focused Beam Reflectance Measurement probe. A simple modification of the particle geometry is incorporated into the probe model in order to simulate the chord-splitting. The simulated CLDs are then compared to the measured CLDs of different preferential crystallization runs performed for the system d,l-threonine/water. More specifically, measured moments of the chord length distributions along with optical rotation trajectories are compared to calculated moments of a population balance model in order to estimate the kinetic model parameters. This way the presented case study gives a systematic approach on how to apply the used probe model to monitor a preferential crystallization process and parameterize a process model.

Simultaneous process mean and variance monitoring using artificial neural networks

Control chart patterns (CCPs) can be employed to determine the behavior of a process. Hence, CCP recognition is an important issue for an effective process-monitoring system. Artificial neural networks (ANNs) have been applied to CCP recognition tasks and promising results have been obtained. It is well known that mean and variance control charts are usually implemented together and that these two charts are not independent of each other, especially for the individual measurements and moving range ( X– R m ) charts. CCPs on the mean and variance charts can be associated independently with different assignable causes when corresponding process knowledge is available. However, ANN-based CCP recognition models for process mean and variance have mostly been developed separately in the literature with the other parameter assumed to be under control. Little attention has been given to the use of ANNs for monitoring the process mean and variance simultaneously. This study presents a real-time ANN-based model for the simultaneous recognition of both mean and variance CCPs. Three most common CCP types, namely shift, trend, and cycle, for both mean and variance are addressed in this work. Both direct data and selected statistical features extracted from the process are employed as the inputs of ANNs. The numerical results obtained using extensive simulation indicate that the proposed model can effectively recognize not only single mean or variance CCPs but also mixed CCPs in which mean and variance CCPs exist concurrently. Empirical comparisons show that the proposed model performs better than existing approaches in detecting mean and variance shifts, while also providing the capability of CCP recognition that is very useful for bringing the process back to the in-control condition. A demonstrative example is provided.

Statistical Monitoring of Complex Chemical Processes Using Agent-Based Systems

It is highly desirable to have a statistical process monitoring (SPM) system that detects the abnormalities in process operations quickly with as few missed and false alarms as possible while the process operates under various operating conditions. An agent-based combined monitoring and fault detection framework is proposed in this study. In this framework, different SPM techniques compete with and complement each other to enhance detection speed and accuracy. SPM techniques from literature such as principal component analysis (PCA), multiblock PCA (MBPCA), and dynamic PCA (DPCA) techniques are implemented in this agent-based process supervision system. An agent performance assessment and agent management layer provides dynamic adaptation of the supervision system and improves the performance of SPM. The statistical information coming from each of the statistical techniques is summarized through a consensus mechanism. The performance of the agent-based consensus mechanism using different consensus criteri...

An object-oriented architecture for sensorless cutting force feedback for CNC milling process monitoring and control

Intelligent Manufacturing Systems require fault-tolerant components capable of preventing and detecting failures, and optimizing their own performance. Although there are a number of process monitoring and control systems (PMCS) available for these tasks, most of them require special equipment that increases cost and setup time. This work proposes a standard software architecture and methodology for implementing a sensorless PMCS which separates data representation from its algorithmic processing. Two different case studies are introduced showing the feasibility and applicability of the proposed design, one for cycle time optimization and another one for online tool condition detection. A good performance was obtained in both case studies based on the proposed PMCS architecture, which can be implemented using different distributed software technologies.

Process Monitoring of Tubular Microreactors using Particle Filter

Microreactors consist of tens to hundreds of micrometer-scale channels. The residence time of a fluid can be set exactly and backmixing can be minimized. Particularly, very short residence time can be achieved. In addition, it is possible to precisely control the reaction temperature due to large surface to volume ratio of channels. These features of microreactors make it possible to realize the production of specialty chemicals, which cannot be handled in conventional reactors. The most recognized problem in microreactors is channel blockage. The catalyst deterioration is also an inevitable problem for catalyst reactions in microreactors. To realize stable long-term operation of microreactors, it is necessary to detect such problems as early as possible. Since miniaturized sensors are expensive and their direct installation inside channels may disturb the flow, it is indispensable to develop a process monitoring system using a few indirect measurements. In this research, a state and parameter estimation system for tubular microreactors (TMRs) is developed to detect process faults. In the developed system, a process model is derived from the first-principle model of TMRs. Particle Filter (PF) or Extended Kalman Filter (EKF) or Ensemble Kalman Filter (EnKF) is designed to obtain the unknown parameters such as catalyst efficiency from a single wall temperature sensor. To achieve high estimation performance, the optimal sensor location is determined on the basis of the observability. The numerical examples illustrate that the blockage and the catalyst deterioration of TMRs can be detected more rapidly and accurately by using PF, as compared with EnKF and EKF.

Multivariate Process Monitoring Using Classical Multidimensional Scaling and Procrustes Analysis

This paper presents a new process monitoring framework using multidimensional scaling. The traditional method of multivariate process monitoring is generally base on principal component analysis (PCA) and is carried out by monitoring the fault detection parameters Hotelling's T2 and squared prediction errors (SPE). Both indexes are derived directly from multivariate scores in the observation sample configurations. This conventional system was found inappropriately used especially in monitoring highly nonlinear multivariate processes leading to a great number of principal components being selected. Alternatively, classical multidimensional scaling (CMDS) is another technique which can be used in compressing multivariate data by using dissimilarity measures for process monitoring. The proposed process monitoring system is developed based on variable relationships and the dissimilarity measures in terms of variable profiles are used in projecting the multivariate scores. A new monitoring index, which is the resultant vector length different between the new and the normal variable profiles, is introduced. Procrustes analysis (PA) is implemented for on-line process monitoring through a moving-window mechanism. The proposed monitoring method is demonstrated on a simulated continuous stirred tank reactor (CSTR) with recycle system. The results show that the proposed system was efficient as well as effective in detecting various abrupt and incipient faults compared to the linear PCA-based scheme.

An adaptive control system with self-organizing fuzzy sliding mode control strategy for micro wire-EDM machines

This paper aims to develop an adaptive control system for process monitoring, identification, and control in micro wire electrical discharge machining (wire-EDM). The proportion of short circuits in the total sparks defined as short circuit ratio is chosen as a control parameter for the adaptive control system. Pulse interval of each spark is adjusted in real-time according to the discrimination of gap status to improve the stability of machining operation. A self-organizing fuzzy sliding mode controller is then proposed for servo feed control. This intelligent control strategy has on-line learning capability and disturbance rejection robustness for responding to the nonlinear, stochastic, and time-varying characteristics of the micro wire-EDM process. Another fuzzy logic controller is designed to control the short circuit ratio at a pre-determined level through an on-line regulation of servo voltage for adaptive process control. Experimental results show that the developed adaptive control system results in faster machining and better machining stability than does a conventional control scheme.

ICAS-PAT: A software for design, analysis and validation of PAT systems

In chemicals based product manufacturing, as in pharmaceutical, food and agrochemical industries, efficient and consistent process monitoring and analysis systems (PAT systems) have a very important role. These PAT systems ensure that the chemicals based product is manufactured with the specified end product qualities. In an earlier article, Singh et al. [Singh, R., Gernaey, K. V., Gani, R. (2009). Model-based computer-aided framework for design of process monitoring and analysis systems. Computers & Chemical Engineering, 33, 22–42] proposed the use of a systematic model and data based methodology to design appropriate PAT systems. This methodology has now been implemented into a systematic computer-aided framework to develop a software (ICAS-PAT) for design, validation and analysis of PAT systems. Two supporting tools needed by ICAS-PAT have also been developed: a knowledge base (consisting of process knowledge as well as knowledge on measurement methods and tools) and a generic model library (consisting of process operational models). Through a tablet manufacturing process example, the application of ICAS-PAT is illustrated, highlighting as well, the main features of the software.

Research on Real-Time Process Monitoring Technology and System for High-Speed Turbopump

Turbopump is a high-fault-rate component in Liquid Rocket Engine (LRE). The research on real-time process monitoring technology and system for turbopump is vital to increase the reliability and safety of LRE. In this paper, three real-time process monitoring algorithms for turbopump were studied firstly, on the basis of monitoring parameters selection and multiple domain feature extraction. Then, the real-time monitoring system realized by means of PXI (PCI eXtensions for Instrumentation) controller was introduced. And, aiming at the shortage of the previous system, the real-time process monitoring system based on DSP (Digital Signal Processor) for turbopump was studied to realize higher computing speed, which could conduce to extract multiple domain features and run manifold real-time monitoring algorithms. The new system could also satisfy the small-sized requirement in engine flying state.

A microcontroller-based approach to monitoring pneumatic actuators

This paper presents the development of a microcontroller-based process monitoring system that is capable of being embedded within high speed automated assembly systems. A generic architecture is presented to fully utilise the communication, data acquisition and signal processing attributes of a current generation microcontroller. The efficacy of this approach is illustrated by the development of an autonomous method in which the system acquires and analyses transient data related to the operation of a linear actuator. The performance of this system clearly identifies the potential of microcontroller based solutions as very powerful yet affordable real-time quality management tools. The low costs associated with the implementation offer the prospect of affordable integrated quality management to a wide range of industries.