Process Monitoring System Research Articles

Process Monitoring Technology Based on Virtual Machining

This paper presents a process monitoring system which is integrated with virtual machining for a more accurate diagnosis of machining operation without the need for test machining. Virtual machining simulates the material removal process along the NC tool path and predicts the static and dynamic characteristics of the operation. The status data of a machine tool from the CNC and sensor signals are integrated with pre-estimated virtual machining data. The proposed monitoring system analyzes the integrated data to diagnose the machining operation in (near) real-time. The application of the monitoring system to 3 axis machining is demonstrated with experimental results.

Integrated Monitoring System of Production Processes

Abstract Integrated monitoring system for discrete manufacturing processes is presented in the paper. The multilayer hardware and software reference model was developed. Original research are an answer for industry needs of the integration of information flow in production process. Reference model corresponds with proposed data model based on multilayer data tree allowing to describe orders, products, processes and save monitoring data. Elaborated models were implemented in the integrated monitoring system demonstrator developed in the project. It was built on the base of multiagent technology to assure high flexibility and openness on applying intelligent algorithms for data processing. Currently on the base of achieved experience an application integrated monitoring system for real production system is developed. In the article the main problems of monitoring integration are presented, including specificity of discrete production, data processing and future application of Cyber-Physical-Systems. Development of manufacturing systems is based more and more on taking an advantage of applying intelligent solutions into machine and production process control and monitoring. Connection of technical systems, machine tools and manufacturing processes monitoring with advanced information processing seems to be one of the most important areas of near future development. It will play important role in efficient operation and competitiveness of the whole production system. It is also important area of applying in the future Cyber-Physical-Systems that can radically improve functionally of monitoring systems and reduce the cost of its implementation.

Monitoring the Diagnostic Process on an Inpatient Neurology Service.

The Institute of Medicine report Improving Diagnosis in Health Care called for tools to monitor physicians' diagnostic process. We addressed this need by developing a tool for clinicians to record and analyze their diagnostic process. The tool was a secure web application in which clinicians used a structured grading system to assess the relative impact of clinical, laboratory, and neuroimaging data for every new diagnosis. Four neurohospitalists used the tool for 6.5 months on a general neurology ward service at a single tertiary-level teaching hospital. Process measures of tool use included number of diagnoses entered, time spent on each data entry, and concordance of diagnoses compared to the medical record. We also aggregated the data across clinicians to examine the average process scores across common inpatient disorders. The 4 clinicians entered 254 new diagnoses that took approximately 3 minutes per patient. In 50 randomly chosen cases, the neurohospitalists' diagnoses entered into the tool agreed with 92% of diagnoses in the medical record, which was better than the agreement between billing code and medical record diagnoses (74%). The diagnostic process varied across disease categories, showing a spectrum of clinical-dominant (eg, headache), laboratory-dominant (eg, encephalitis), and neuroimaging-dominant (eg, stroke) disorders. This study demonstrated the feasibility of a clinician-driven diagnostic process monitoring system, along with preliminary characterization of the process for common disorders. The tracking of diagnostic process has the potential to promote reflection on clinical practice, deconstruct neurologists' clinical decision-making, and improve health-care safety.

안벽공정 모니터링 시스템의 개념 설계에 관한 연구

안벽공정 모니터링 시스템의 개념 설계에 관한 연구

On-Line Monitoring of Drill Wear in Machining of AISI 1040 Steel Using Virtual Instrumentation

In this research work, an attempt is made to develop the on-line wear state monitoring system during drilling process on Vertical Machining Centre (VMC). The main objectives of the present work is to predict the drill wear states of High Speed Steel (HSS) drill bit during the drilling of a AISI 1040 Steel work piece using standard data acquisition software Lab VIEW by the application of Virtual Instrumentation (VI). The drill bit wear states were analyzed using spindle motor cutting current signals. The effective drill wear model have been developed using spindle motor cutting current signals and the various cutting parameters through Lab VIEW, to predict wear states of the drill-bit. The developed on-line drill wear process monitoring system has been used for the continuous monitoring of the drill-bit status, and to exhibit the drill wear states. It was found that the developed model show a good agreement with the experimental data where the deviation in drill-bit wear is less than 7% for varying cutting conditions.

Process monitoring of friction stir welding via the frequency of the spindle motor current

Friction stir welding is a solid-state process that is gaining preference for the joining of metals with low melting points. Despite the clear advantages of friction stir welding over traditional fusion welding, voids within the weld seam arise when improper conditions are present. The work presented in this article examines the development of an automated process monitoring system for friction stir welding. The system indirectly monitors the welding torque through the supplied current to the spindle motor. To measure the current, a clamp-on current meter was used. Our results have shown that using a simple and inexpensive clamp-on current meter provides good insight into the welding torque. Examination focused on the frequency spectrum of the current. A Fourier transform decomposed the signal into various frequencies present. The results consistently showed that when no void was present, there was a component of the current’s frequency at 14 Hz. However, when the tool encountered a void, the frequency spectrum changed. The component at 14 Hz went away while content in the range of 1–4 Hz increased.

Performance of adaptive np-chart with estimated parameter

Purpose – The purpose of this paper is to compare the performances of np-VP control chart with estimated parameter to the np-VP control chart with known parameter using average time-to-signal (ATS), standard deviation of the time-to-signal (SDTS), and average number of observations to signal (ANOS) as performance measures. Design/methodology/approach – The approach used in this study is probabilistic in which the expected values of performance measures are calculated using probabilities of different estimators used to estimate process parameter. Findings – Numerical results indicate different performances for the np-VP control chart in known and estimated parameter cases. It is obvious that when process parameter is not known and is estimated using Phase I data, the chart does not perform as user expects. To tackle this issue, optimal Phase I estimation scenarios are recommended to obtain the best performance from the chart in the parameter estimation case in terms of performance measures. Practical implications – This research adds to the body of knowledge in quality control of process monitoring systems. This paper may be of particular interest to practitioners of quality systems in factories where products are monitored to reduce the number of defectives and np chart parameter needs to be estimated. Originality/value – The originality of this paper lies within the context in which an adaptive np control chart is studied and the process parameter unlike previous studies is assumed unknown. Although other types of control charts have been studied when process parameter is unknown but this is the first time that adaptive np chart performance with estimated process parameter is studied.

Application of Clustering Methods for Online Tool Condition Monitoring and Fault Diagnosis in High-Speed Milling Processes

Tool condition monitoring (TCM) is a necessary action in a high-speed milling (HSM) process. As a worn milling tool might irreversibly damage a workpiece, there is a vital demand for a TCM system to evaluate the tool wear progress, or equivalently the resultant surface roughness, nonintrusively. To build up a condition monitoring system for HSM processes, sensor signals are to be utilized to form a reference model that reflects the performance of the system. Therefore, a desired reference model has to apply more efficient feature extraction and artificial intelligence techniques to be more repeatable and generalizable. This paper illustrates the performance of clustering techniques on high-speed end milling experimental data. Studied clustering methods are applied to the wavelet features of force and vibration signals to illustrate the repeatability of their results. It is shown that clustering methods can coarsely capture the status of the process and can be applied for fault diagnosis and TCM purposes. It is also discussed how the application of clustering methods may improve the performance of existing reference models toward the more efficient utilization of available experimental data and to develop easily generalizable reference models. Finally, a possible application of clustering results is discussed comparing with state-of-the-art papers.

ORGANIZATIONAL AND METHODOLOGICAL CONDITIONS OF PORTFOLIO IMPLEMENTATION INTO PRIMARY SCHOOL EDUCATIONAL PROCESS

Goal. The article outlines the priorities of the portfolio as an objective and timely assessment of the main results (personal, subject and meta-subject ones) in primary school in conditions of transition to a qualitatively new level of education content in accordance with the Federal State Educational Standard of Primary General Education (FSES of PGE). Organizational and methodical peculiarities of the portfolio implementation process into educational process of primary school are specified. Method or Work Methodology . The basic research methods are as follows: generaliza-tion, analogy, classification, modeling of the portfolio introducing process as a means of primary school student’s achievement evaluation. Results. The results of the work comprise systematization of a student’s portfolio features as an effective means of assessing student achievement, as well as specification of the portfolio structure, distribution of roles in its accumulation, interpretation and use. Range of Result Application . The study results can be applied in primary school educational process with the aim of increasing the educational process monitoring and evaluation system productivity.

Model-based Predictive Force Control in Milling – System Identification

Manufacturing complex products, process monitoring and process control systems can increase productivity. Today, improvements in simulation technique enable computer based process set up and optimization reducing expensive trials. The performance of offline optimization strongly depends on the selected model and does not include uncertainties such as to tool wear or deviating material properties. Sensors help to reduce uncertainties and thus increase stability.Milling is one of the most flexible metal cutting processes. The process force is a very important evaluating process performance. Closed loop control strategies for force control in milling help increasing the process performance and protecting the tool, the workpiece and the machine. The varying transfer behavior of the controlled system is challenging. Adaptive control systems only react to changing transfer behavior and adjust the controller parameter. However, they cannot prevent high overshoots of the force.In this paper a new method to identify the controlled system is presented. The controlled system's transfer behavior is split into one part resulting from the machine tool and a second part describing the manufacturing process.

Production Monitoring Based on Sensing Clamping Elements

Clamping errors in workpiece positioning decrease the production outcome of machine tools by causing rejects. An automated monitoring of these failures does not take place in practice, due to limited installation space for the sensor integration, especially in series production. Within the Collaborative Research Centre 653, the IFW develops and investigates a condition and process monitoring system based on sensing clamping elements in close cooperation with two industrial partners, the companies Roemheld GmbH (clamping technology manufacturer) and ReiKam GmbH (fixture construction service provider). It consists of hydraulic clamping elements with integrated sensors, decentral electronics for signal preprocessing, bus communication and a central processing unit. Measureable quantities are hydraulic pressure, the clamping stroke and the process forces. This article describes the prototypical realization and shows its usability in condition and process monitoring. Experimental results from measurements during milling operations and the comparison with a dynamometer demonstrate the performance of the sensory clamping system.

Synergy between data reconciliation and principal component analysis in energy monitoring

Monitoring of energy consumption is central importance for the energy-efficient operation of chemical processes. Fault detection and process monitoring systems can reduce the environmental impact and enhance safety and energy efficiency of chemical processes. These solutions are based on the analysis of process data. Data reconciliation is a model-based technique that checks the consistence of measurements and balance equations. Principal component analysis is a similar multivariate model based technique, but it utilises a data-driven statistical model. We investigate how information can be transferred between these models to get a more sensitive tool for energy monitoring. To illustrate the capability of the proposed method in energy monitoring, we provide a case study for heat balance analysis in the wellknown Tennessee Eastman benchmark problem. The results demonstrate how balance equations can improve energy management of complex process technologies.

PCA Based Data Reconciliation in Soft Sensor Development - Application for Melt Flow Index Estimation

Melt flow index (MFI) is a very important property of thermoplastic polymers. Laboratory measurements follow standard methods (ASTM D1238 or ISO 1133) to determine MFI and give accurate values, but these measurements are available only in 2-4 hour sampling intervals. Using soft sensors real time estimation of MFI is available for process control and monitoring. When detailed knowledge about the process is not available, data-driven soft sensors can be applied. In this case historical process data are used to build statistical models to determine the relationship between inputs and outputs. Since these methods are based on measurements, the performance of soft sensor depends on the quality of data. Measurements are always affected by errors so pre-processing of data should be necessary. The measurement noise and process variable can be correlated with each other so one opportunity to improve measurement accuracy is using multivariate statistical methods (PCA, PLS). Statistical methods can be improved when phenomenological knowledge is taken into account (e.g. balance equations). The aim of the presented research is to propose a methodology to support the data-driven development of process monitoring systems. We developed a method which improves the effectiveness of data based MFI soft sensor. This method includes advantages of a priori knowledge based models and data-driven multivariate statistical process monitoring tools. As a case study we developed a soft-sensor to estimate MFI of the products of industrial polypropylene reactor at TVK Plc. The proposed method is able to detect undesirable operation states and it can be used for fault detection.

Cyber physical process monitoring systems

Manufacturing process monitoring systems is evolving from centralised bespoke applications to decentralised reconfigurable collectives. The resulting cyber-physical systems are made possible through the integration of high power computation, collaborative communication, and advanced analytics. This digital age of manufacturing is aimed at yielding the next generation of innovative intelligent machines. The focus of this research is to present the design and development of a cyber-physical process monitoring system; the components of which consist of an advanced signal processing chain for the semi-autonomous process characterisation of a CNC turning machine tool. The novelty of this decentralised system is its modularity, reconfigurability, openness, scalability, and unique functionality. The function of the decentralised system is to produce performance criteria via spindle vibration monitoring, which is correlated to the occurrence of sequential process events via motor current monitoring. Performance criteria enables the establishment of normal operating response of machining operations, and more importantly the identification of abnormalities or trends in the sensor data that can provide insight into the quality of the process ongoing. The function of each component in the signal processing chain is reviewed and investigated in an industrial case study.

On the use of multi-agent systems for the monitoring of industrial systems

The objective of the current paper is to present an intelligent system for complex process monitoring, based on artificial intelligence technologies. This system aims to realize with success all the complex process monitoring tasks that are: detection, diagnosis, identification and reconfiguration. For this purpose, the development of a multi-agent system that combines multiple intelligences such as: multivariate control charts, neural networks, Bayesian networks and expert systems has became a necessity. The proposed system is evaluated in the monitoring of the complex process Tennessee Eastman process.

Canonical correlation analysis-based fault detection methods with application to alumina evaporation process

In this paper, canonical correlation analysis (CCA)-based fault detection methods are proposed for both static and dynamic processes. Different from the well-established process monitoring and fault diagnosis systems based on multivariate analysis techniques like principal component analysis and partial least squares, the core of the proposed methods is to build residual signals by means of the CCA technique for the fault detection purpose. The proposed methods are applied to an alumina evaporation process, and the achieved results show that both methods are applicable for fault detection, while the dynamic one delivers better detection performance.

Ubiquitous Monitoring for Industrial Cyber-Physical Systems Over Relay- Assisted Wireless Sensor Networks

Ubiquitous monitoring over wireless sensor networks (WSNs) is of increasing interest in industrial cyber-physical systems (CPSs). Question of how to understand a situation of physical system by estimating process parameters is largely unexplored. This paper is concerned with the distributed estimation problem for industrial automation over relay-assisted WSNs. Different from most existing works on WSN with homogeneous sensor nodes, the network considered in this paper consists of two types of nodes, i.e., sensing nodes (SNs), which is capable of sensing and computing, and relay nodes (RNs), which is only capable of simple data aggregation. We first adopt a Kalman filtering (KF) approach to estimate the unknown physical parameters. In order to facilitate the decentralized implementation of the KF algorithm in relay-assisted WSNs, a tree-based broadcasting strategy is provided for distributed sensor fusion. With the fused information, the consensus-based estimation algorithms are proposed for SNs and RNs, respectively. The proposed method is applied to estimate the slab temperature distribution in a hot rolling process monitoring system, which is a typical industrial CPS. It is demonstrated that the introduction of RNs improves temperature estimation efficiency and accuracy compared with the homogeneous WSN with SNs only.

RTRO–Coal: Real-Time Resource-Reconciliation and Optimization for Exploitation of Coal Deposits

This contribution presents an innovative and integrated framework for real-time-process reconciliation and optimization (RTRO) in large continuous open pit coal mines. RTRO-Coal is currently developed, validated, tested and implemented as part of a multi-national multi-partner European Union funded R&D project. The key concept is to promote a shift in paradigm from intermittent discontinuous to a continuous process monitoring and quality management system in large scale coal mining operations. The framework is based on a real-time feedback control loop linking online data acquired during extraction rapidly with a sequentially up-datable resource model. The up-to-date model is integrated with a real-time optimization of short-term sequencing and production control decisions. Improved decisions are expected to lead to increased resource-and process efficiency and support a sustainable extraction of natural resources. This contribution introduces to the framework, discusses main building blocks and illustrates the value added by the means of selected examples.

Designing and Implementation of Industrial Process Control and Monitoring System Using Fuzzy PID and WSN

The paper addresses the nonlinear control design problem for a two tank system. Three crucial industrial parameters i.e. temperature, level and flow are used in this work. Mathematical modeling of the proposed two tank system is presented in this work and the proposed system is also implemented using Lab View. Industrial parameters are controlled by Fuzzy PID controller which greatly improves the performance of the system. This system can be adopted by industries to improve their processes and it can also be used by academia for teaching purposes. This system provides real time simulation and hardware implementation which provide aid in learning for the graduate and undergraduate control students.

Linear friction weld process monitoring of fixture cassette deformations using empirical mode decomposition

Due to its inherent advantages, linear friction welding is a solid-state joining process of increasing importance to the aerospace, automotive, medical and power generation equipment industries. Tangential oscillations and forge stroke during the burn-off phase of the joining process introduce essential dynamic forces, which can also be detrimental to the welding process. Since burn-off is a critical phase in the manufacturing stage, process monitoring is fundamental for quality and stability control purposes. This study aims to improve workholding stability through the analysis of fixture cassette deformations. Methods and procedures for process monitoring are developed and implemented in a fail-or-pass assessment system for fixture cassette deformations during the burn-off phase. Additionally, the de-noised signals are compared to results from previous production runs. The observed deformations as a consequence of the forces acting on the fixture cassette are measured directly during the welding process. Data on the linear friction-welding machine are acquired and de-noised using empirical mode decomposition, before the burn-off phase is extracted. This approach enables a direct, objective comparison of the signal features with trends from previous successful welds. The capacity of the whole process monitoring system is validated and demonstrated through the analysis of a large number of signals obtained from welding experiments.