Distributed control system
A distributed control system is a computerised control system for a process or plant with a large number of control loops, in which autonomous controllers are distributed throughout the system, but there is central operator supervisory control. This is in contrast to systems; the DCS concept increases reliability and reduces installation costs by localising control functions near the process plant, with remote monitoring and supervision. Distributed control systems first emerged in large, high value, safety critical process industries, were attractive because the DCS manufacturer would supply both the local control level and central supervisory equipment as an integrated package, thus reducing design integration risk. Today the functionality of SCADA and DCS systems are similar, but DCS tends to be used on large continuous process plants where high reliability and security is important, the control room is not geographically remote; the key attribute of a DCS is its reliability due to the distribution of the control processing around nodes in the system.
This mitigates a single processor failure. If a processor fails, it will only affect one section of the plant process, as opposed to a failure of a central computer which would affect the whole process; this distribution of computing power local to the field Input/Output connection racks ensures fast controller processing times by removing possible network and central processing delays. The accompanying diagram is a general model which shows functional manufacturing levels using computerised control. Referring to the diagram. Level 2 contains the supervisory computers, which collect information from processor nodes on the system, provide the operator control screens. Level 3 is the production control level, which does not directly control the process, but is concerned with monitoring production and monitoring targets Level 4 is the production scheduling level. Levels 1 and 2 are the functional levels of a traditional DCS, in which all equipment are part of an integrated system from a single manufacturer.
Levels 3 and 4 are not process control in the traditional sense, but where production control and scheduling takes place. The processor nodes and operator graphical displays are connected over proprietary or industry standard networks, network reliability is increased by dual redundancy cabling over diverse routes; this distributed topology reduces the amount of field cabling by siting the I/O modules and their associated processors close to the process plant. The processors receive information from input modules, process the information and decide control actions to be signalled by the output modules; the field inputs and outputs can be analog signals e.g. 4–20 mA DC current loop or 2 state signals that switch either "on" or "off", such as relay contacts or a semiconductor switch. DCSs are connected to sensors and actuators and use setpoint control to control the flow of material through the plant. A typical application is a PID controller fed by a flow meter and using a control valve as the final control element.
The DCS sends the setpoint required by the process to the controller which instructs a valve to operate so that the process reaches and stays at the desired setpoint.. Large oil refineries and chemical plants have several thousand I/O points and employ large DCS. Processes are not limited to fluidic flow through pipes and can include things like paper machines and their associated quality controls, variable speed drives and motor control centers, cement kilns, mining operations, ore processing facilities, many others. DCSs in high reliability applications can have dual redundant processors with "hot" switch over on fault, to enhance the reliability of the control system. Although 4–20 mA has been the main field signalling standard, modern DCS systems can support fieldbus digital protocols, such as Foundation Fieldbus, profibus, HART, Modbus, PC Link etc. and other digital communication protocols such as modbus. Modern DCSs support neural networks and fuzzy logic applications. Recent research focuses on the synthesis of optimal distributed controllers, which optimizes a certain H-infinity or the H 2 control criterion.
Distributed control systems are dedicated systems used in manufacturing processes that are continuous or batch-oriented. Processes where a DCS might be used include: Chemical plants Petrochemical and refineries Pulp and Paper Mills Boiler controls and power plant systems Nuclear power plants Environmental control systems Water management systems Water treatment plants Sewage treatment plants Food and food processing Agro chemical and fertilizer Metal and mines Automobile manufacturing Metallurgical process plants Pharmaceutical manufacturing Sugar refining plants Agriculture Applications Process control of large industrial plants has evolved through many stages. Control would be from panels local to the process plant; however this required a large manpower resource to attend to these dispersed panels, there was no overall view of the process. The next logical development was the transmission of all plant measurements to a permanently-manned central control room; this was the centralisation of all the localised panels, with the advantages of lower manning levels and easier overview of the process.
The controllers were b
Taylor & Francis
Taylor & Francis Group is an international company originating in England that publishes books and academic journals. It is a division of a United Kingdom-based publisher and conference company; the company was founded in 1852 when William Francis joined Richard Taylor in his publishing business. Taylor founded his company in 1798, their subjects covered agriculture, education, geography, mathematics and social sciences. From 1917 to 1930 Francis' son, Richard Taunton Francis was sole partner in the firm. In 1965 Taylor & Francis began book publishing. In 1988 it acquired Hemisphere Publishing and the company was renamed Taylor & Francis Group to reflect the growing number of imprints. In 1990 Taylor & Francis exited from the printing business to concentrate on publishing. In 1998 Taylor & Francis Group went public on the London Stock Exchange and in the same year the group purchased its academic publishing rival Routledge for £90 million. Acquisitions of other publishers has remained a core part of the group's business strategy.
Taylor & Francis merged with Informa in 2004 to create a new company called T&F Informa, since renamed back to Informa. Following the merger, T&F closed the historic Routledge books office in New Fetter Lane and relocated to its current headquarters in Milton Park, Oxfordshire. Taylor & Francis Group is now the academic publishing arm of Informa and accounted for 30.2% of Group Revenue and 38.1% of Adjusted Profit in 2017. Taylor & Francis publishes more than 2,700 journals, 7,000 new books each year, with a backlist of over 140,000 titles available in print and digital formats, it uses the Routledge imprint for its publishing in humanities, social sciences, behavioural sciences and education and the CRC Press imprint for its publishing in science, technology and mathematics. In 2017, T&F sold assets from its Garland Science imprint to W. W. Norton & Company and ceased to use that brand. Although considered the smallest of the'Big Four' STEM publishers, its Routledge imprint is claimed to be the largest global academic publisher within humanities and social sciences.
The company's journals have been delivered through the Taylor & Francis Online website since June 2011. Prior to that they were provided through the Informaworld website. Taylor & Francis ebooks are now available via the TaylorFrancis website. Taylor & Francis operates a number of Web services for its digital content including Routledge Handbooks Online, the Routledge Performance Archive, Secret Intelligence Files and Routledge Encyclopedia of Modernism. Taylor & Francis offers Open Access publishing options in both its books and journals divisions and through its Cogent Open Access journals imprint. Taylor & Francis is a member of several professional publishing bodies including the Open Access Scholarly Publishers Association, the International Association of Scientific and Medical Publishers, the Association of Learned & Professional Society Publishers and The Publishers Association. In 2017, after collaborating for several years, T&F purchased specialist digital resources company Colwiz.
The group has 1,800 employees located in at least 18 offices worldwide. Its head office is based in Milton Park, Abingdon in the United Kingdom, with other offices in Stockholm, New York, Boca Raton, Kentucky, Kuala Lumpur, Hong Kong, Shanghai, Melbourne, Cape Town and New Delhi; the old Taylor and Francis logo depicts a hand pouring oil into a lit lamp, along with the Latin phrase "alere flammam" - to feed the flame. The modern logo is a stylised oil lamp in a circle. In 2013, the entire board of the Journal of Library Administration resigned in a dispute over author licensing agreements. In 2016 Critical Reviews in Toxicology was accused of being a "broker of junk science" by the Center for Public Integrity. Monsanto was found to have worked with an outside consulting firm to induce the journal to publish a biased review of the health effects of its product "Roundup". In 2017, Taylor & Francis was criticized for getting rid of the editor-in-chief of International Journal of Occupational and Environmental Health, who accepted articles critical of corporate interests.
The company replaced the editor with a corporate consultant without consulting the editorial board. The journal Cogent Social Sciences accepted a hoax article, "The conceptual penis as a social construct", rejected by another Taylor & Francis journal, NORMA: International Journal for Masculinity Studies; when the authors announced the hoax, the article was retracted. In December 2018, the journal Dynamical Systems accepted the paper Saturation of Generalized Partially Hyperbolic Attractors only to have it retracted after publication due to the Iranian nationality of the authors; the European Mathematical Society condemned the retraction and announced that Taylor & Francis had agreed to reverse the decision. Previous instances of Taylor & Francis journals discriminating against Iranian authors were reported in 2013. Taylor & Francis academic journals Munroe, Mary H.. "Taylor & Francis". The Academic Publishing Industry: A Story of Merger and Acquisition. Northern Illinois University Libraries. Archived from the original on 2012-05-04.
Retrieved 2008-06-20. Brock, W. H. & Meadows, A. J.. The Lamp Of Learning: Taylor & Francis And Two Centuries Of Publishing. Taylor & Francis. Official website Taylor & Francis online journals and reference works Taylor & Francis eBooks Informa Divisions - Academic Publishing
Computer-aided manufacturing is the use of software to control machine tools and related ones in the manufacturing of workpieces. This is not the only definition for CAM, its primary purpose is to create a faster production process and components and tooling with more precise dimensions and material consistency, which in some cases, uses only the required amount of raw material, while reducing energy consumption. CAM is now a system used in lower educational purposes. CAM is a subsequent computer-aided process after computer-aided design and sometimes computer-aided engineering, as the model generated in CAD and verified in CAE can be input into CAM software, which controls the machine tool. CAM is used in many schools alongside Computer-Aided Design to create objects. Traditionally, CAM has been considered as a numerical control programming tool, where in two-dimensional or three-dimensional models of components generated in CAD; as with other “Computer-Aided” technologies, CAM does not eliminate the need for skilled professionals such as manufacturing engineers, NC programmers, or machinists.
CAM, in fact, leverages both the value of the most skilled manufacturing professionals through advanced productivity tools, while building the skills of new professionals through visualization and optimization tools. Early commercial applications of CAM was in large companies in the automotive and aerospace industries, for example Pierre Béziers work developing the CAD/CAM application UNISURF in the 1960s for car body design and tooling at Renault. CAM software was seen to have several shortcomings that necessitated an overly high level of involvement by skilled CNC machinists. Fallows created the first CAD software but this had severe shortcomings and was promptly taken back into the developing stage. CAM software would output code for the least capable machine, as each machine tool control added on to the standard G-code set for increased flexibility. In some cases, such as improperly set up CAM software or specific tools, the CNC machine required manual editing before the program will run properly.
None of these issues were so insurmountable that a thoughtful engineer or skilled machine operator could not overcome for prototyping or small production runs. In high production or high precision shops, a different set of problems were encountered where an experienced CNC machinist must both hand-code programs and run CAM software. Integration of CAD with other components of CAD/CAM/CAE Product lifecycle management environment requires an effective CAD data exchange, it had been necessary to force the CAD operator to export the data in one of the common data formats, such as IGES or STL or Parasolid formats that are supported by a wide variety of software. The output from the CAM software is a simple text file of G-code/M-codes, sometimes many thousands of commands long, transferred to a machine tool using a direct numerical control program or in modern Controllers using a common USB Storage Device. CAM packages could not, still cannot, reason as a machinist can, they could not optimize toolpaths to the extent required of mass production.
Users would select the type of machining process and paths to be used. While an engineer may have a working knowledge of G-code programming, small optimization and wear issues compound over time. Mass-produced items that require machining are initially created through casting or some other non-machine method; this enables hand-written and optimized G-code that could not be produced in a CAM package. At least in the United States, there is a shortage of young, skilled machinists entering the workforce able to perform at the extremes of manufacturing; as CAM software and machines become more complicated, the skills required of a machinist or machine operator advance to approach that of a computer programmer and engineer rather than eliminating the CNC machinist from the workforce. Typical areas of concern: High Speed Machining, including streamlining of tool paths Multi-function Machining 5 Axis Machining Feature recognition and machining Automation of Machining processes Ease of Use Over time, the historical shortcomings of CAM are being attenuated, both by providers of niche solutions and by providers of high-end solutions.
This is occurring in three arenas: Ease of usage Manufacturing complexity Integration with PLM and the extended enterpriseEase in useFor the user, just getting started as a CAM user, out-of-the-box capabilities providing Process Wizards, libraries, machine tool kits, automated feature based machining and job function specific tailorable user interfaces build user confidence and speed the learning curve. User confidence is further built on 3D visualization through a closer integration with the 3D CAD environment, including error-avoiding simulations and optimizations. Manufacturing complexity The manufacturing environment is complex; the need for CAM and PLM tools by the manufacturing engineer, NC programmer or machinist is similar to the need for computer assistance by the pilot of modern aircraft systems. The modern machinery cannot be properly used without this assistance. Today's CAM systems support the full range of machine tools including: turning, 5 axis machining, laser / plasma cutting, wire EDM.
Today’s CAM user can generate streamlined tool paths, optimized tool axis tilt for higher feed rates, better tool life and surface finish, an
Overall equipment effectiveness
Overall equipment effectiveness is a term coined by Seiichi Nakajima in the 1960s to evaluate how a manufacturing operation is utilized. It is based on the Harrington Emerson way of thinking regarding labor efficiency; the results are stated in a generic form which allows comparison between manufacturing units in differing industries. It is not however an absolute measure and is best used to identify scope for process performance improvement, how to get the improvement. Continuous improvement in OEE is the goal of TPM; the goal of TPM as set out by Seiichi Nakajima is "The continuous improvement of OEE by engaging all those that impact on it in small group activities". To achieve this, the TPM toolbox sets out a Focused improvement tactic to reduce each of the six types of OEE loss. For example the Focused improvement tactic to systematically reduce breakdown risk sets out how to improve asset condition and standardise working methods to reduce human error and accelerated wear. OEE is a measure of.
Combining OEE with Focused improvement converts OEE from a lagging to a leading indicator. The first Focussed improvement stage of OEE improvement is to achieve a stable OEE. One which varies at around 5% from the mean for a representative production sample. Once an asset effectiveness is stable and not impacted by variability in equipment wear rates and working methods; the second stage of OEE improvement can be carried out to remove chronic losses. Combining OEE and TPM Focused improvement tactics creates a leading indicator that can be used to guide performance management priorities; as the TPM process delivers these gains through small cross functional improvement teams, the process of OEE improvement raises front line team engagement/problem ownership and skill levels. It is this combination of OEE as a KPI, TPM Focused improvement tactics and front line team engagement that locks in the gains and delivers the TPM goal of year on year improvement in OEE. A challenging but achieveable 3 to 5 year goal is a 50% improvement in OEE.
OEE measurement is commonly used as a key performance indicator in conjunction with lean manufacturing efforts to provide an indicator of success. OEE can be illustrated by a brief discussion of the six metrics; the hierarchy consists of four underlying measures. Overall equipment effectiveness and total effective equipment performance are two related metrics that report the overall utilization of facilities and material for manufacturing operations; these top view metrics directly indicate the gap between ideal performance. Overall equipment effectiveness quantifies how well a manufacturing unit performs relative to its designed capacity, during the periods when it is scheduled to run. Total effective equipment performance measures OEE against calendar hours, i.e.: 24 hours per day, 365 days per year. In addition to the above measures, there are four underlying metrics that provide understanding as to why and where the OEE and TEEP gaps exist; the measurements are described below Loading: The portion of the TEEP Metric that represents the percentage of total calendar time, scheduled for operation.
Availability: The portion of the OEE Metric that represents the percentage of scheduled time that the operation is available to operate. Referred to as Uptime. Performance: The portion of the OEE Metric that represents the speed at which the Work Center runs as a percentage of its designed speed. Quality: The portion of the OEE Metric that represents the Good Units produced as a percentage of the Total Units Started, it is referred to as the first pass yield. What follows is a detailed presentation of each of the six OEE / TEEP Metrics and examples of how to perform calculations; the calculations are not complicated, but care must be taken as to standards that are used as the basis. Additionally, these calculations are valid at the work center or part number level but become more complicated if rolling down to aggregate levels. OEE breaks the performance of a manufacturing unit into three separate but measurable components: Availability and Quality; each component points to an aspect of the process.
OEE rolled up to Department or Plant levels. This tool allows for drilling down for specific analysis, such as a particular Part Number, Shift, or any of several other parameters, it is unlikely that any manufacturing process can run at 100% OEE. Many manufacturers benchmark their industry to set a challenging target. OEE is calculated with the formula **Using the examples given below: **= Alternatively, easier, OEE is calculated by dividing the minimum time needed to produce the parts under optimal conditions by the actual time needed to produce the parts. For example: Total Time: 8 hour shift or 28,800 seconds, producing 14,400 parts, or one part every 2 seconds. Fastest possible cycle time is 1.5 seconds, hence only 21,600 seconds would have been needed to produce the 14,400 parts. The remaining 7,200 seconds or 2 hours were lost; the OEE is now the 21,600 seconds divided by 28,800 seconds, or 75%. Where OEE measures effectiveness based on scheduled hours, TEEP measures effectiveness against calendar hours, i.e.: 24 hours per day, 365 days per year.
TEEP, reports the'bottom line' utilization of assets. TEEP = Loading * OE
Quality management system
A quality management system is a collection of business processes focused on meeting customer requirements and enhancing their satisfaction. It is aligned with an organization's purpose and strategic direction, it is expressed as the organizational goals and aspirations, processes, documented information and resources needed to implement and maintain it. Early quality management systems emphasized predictable outcomes of an industrial product production line, using simple statistics and random sampling. By the 20th century, labor inputs were the most costly inputs in most industrialized societies, so focus shifted to team cooperation and dynamics the early signaling of problems via a continual improvement cycle. In the 21st century, QMS has tended to converge with sustainability and transparency initiatives, as both investor and customer satisfaction and perceived quality is tied to these factors. Of QMS regimes, the ISO 9000 family of standards is the most implemented worldwide – the ISO 19011 audit regime applies to both, deals with quality and sustainability and their integration.
Other QMS, e.g. Natural Step, focus on sustainability issues and assume that other quality problems will be reduced as result of the systematic thinking, transparency and diagnostic discipline; the term "Quality Management System" and the acronym "QMS" were invented in 1991 by Ken Croucher, a British management consultant working on designing and implementing a generic model of a QMS within the IT industry. Quality objectives Quality manual Organizational structure and responsibilities Data management Processes – including purchasing Product quality leading to customer satisfaction Continuous improvement including corrective and preventive action Quality instrument Document control The concept of a quality as we think of it now first emerged from the Industrial Revolution. Goods had been made from start to finish by the same person or team of people, with handcrafting and tweaking the product to meet'quality criteria'. Mass production brought huge teams of people together to work on specific stages of production where one person would not complete a product from start to finish.
In the late 19th century pioneers such as Frederick Winslow Taylor and Henry Ford recognized the limitations of the methods being used in mass production at the time and the subsequent varying quality of output. Birland established Quality Departments to oversee the quality of production and rectifying of errors, Ford emphasized standardization of design and component standards to ensure a standard product was produced. Management of quality was the responsibility of the Quality department and was implemented by Inspection of product output to'catch' defects. Application of statistical control came as a result of World War production methods, which were advanced by the work done of W. Edwards Deming, a statistician, after whom the Deming Prize for quality is named. Joseph M. Juran focused more on managing for quality; the first edition of Juran's Quality Control Handbook was published in 1951. He developed the "Juran's trilogy", an approach to cross-functional management, composed of three managerial processes: quality planning, quality control, quality improvement.
These functions all play a vital role. Quality, as a profession and the managerial process associated with the quality function, was introduced during the second half of the 20th century and has evolved since then. Over this period, few other disciplines have seen as many changes as the quality profession; the quality profession grew from simple control to systems engineering. Quality control activities were predominant in the 1940s, 1950s, 1960s; the 1970s were an era of quality engineering and the 1990s saw quality systems as an emerging field. Like medicine and engineering, quality has achieved status as a recognized professionAs Lee and Dale state, there are many organizations that are striving to assess the methods and ways in which their overall productivity, the quality of their products and services and the required operations to achieve them are done; the two primary, state of the art, guidelines for medical device manufacturer QMS and related services today are the ISO 13485 standards and the US FDA 21 CFR 820 regulations.
The two have a great deal of similarity, many manufacturers adopt QMS, compliant with both guidelines. ISO 13485 are harmonized with the European Union medical devices directive as well as the IVD and AIMD directives; the ISO standard is incorporated in regulations for other jurisdictions such as Japan and Canada. Quality System requirements for medical devices have been internationally recognized as a way to assure product safety and efficacy and customer satisfaction since at least 1983 and were instituted as requirements in a final rule published on October 7, 1996; the U. S. Food and Drug Administration had documented design defects in medical devices that contributed to recalls from 1983 to 1989 that would have been prevented if Quality Systems had been in place; the rule is promulgated at 21 CFR 820. According to current Good Manufacturing Practice, medical device manufacturers have the responsibility to use good judgment when developing their quality system and apply those sections of the FDA Quality System Regulation that are applicable to their specific products and operations, in Part 820 of the QS regulation.
As with GMP, operating within this flexibility, it is the responsibility of each manufacturer to establish requirements for each type or family of devices that will result in devices that are safe and effective, to establish method
An industrial robot is a robot system used for manufacturing. Industrial robots are automated and capable of movement on three or more axis. Typical applications of robots include welding, assembly and place for printed circuit boards and labeling, product inspection, testing, they can assist in material handling. In the year 2015, an estimated 1.64 million industrial robots were in operation worldwide according to International Federation of Robotics. The most used robot configurations are articulated robots, SCARA robots, delta robots and cartesian coordinate robots. In the context of general robotics, most types of robots would fall into the category of robotic arms. Robots exhibit varying degrees of autonomy: Some robots are programmed to faithfully carry out specific actions over and over again without variation and with a high degree of accuracy; these actions are determined by programmed routines that specify the direction, velocity and distance of a series of coordinated motions. Other robots are much more flexible as to the orientation of the object on which they are operating or the task that has to be performed on the object itself, which the robot may need to identify.
For example, for more precise guidance, robots contain machine vision sub-systems acting as their visual sensors, linked to powerful computers or controllers. Artificial intelligence, or what passes for it, is becoming an important factor in the modern industrial robot; the earliest known industrial robot, conforming to the ISO definition was completed by "Bill" Griffith P. Taylor in 1937 and published in Meccano Magazine, March 1938; the crane-like device was built entirely using Meccano parts, powered by a single electric motor. Five axes of movement were possible, including grab rotation. Automation was achieved using punched paper tape to energise solenoids, which would facilitate the movement of the crane's control levers; the robot could stack wooden blocks in pre-programmed patterns. The number of motor revolutions required for each desired movement was first plotted on graph paper; this information was transferred to the paper tape, driven by the robot's single motor. Chris Shute built a complete replica of the robot in 1997.
George Devol applied for the first robotics patents in 1954. The first company to produce a robot was Unimation, founded by Devol and Joseph F. Engelberger in 1956. Unimation robots were called programmable transfer machines since their main use at first was to transfer objects from one point to another, less than a dozen feet or so apart, they used hydraulic actuators and were programmed in joint coordinates, i.e. the angles of the various joints were stored during a teaching phase and replayed in operation. They were accurate to within 1/10,000 of an inch. Unimation licensed their technology to Kawasaki Heavy Industries and GKN, manufacturing Unimates in Japan and England respectively. For some time Unimation's only competitor was Cincinnati Milacron Inc. of Ohio. This changed radically in the late 1970s when several big Japanese conglomerates began producing similar industrial robots. In 1969 Victor Scheinman at Stanford University invented the Stanford arm, an all-electric, 6-axis articulated robot designed to permit an arm solution.
This allowed it to follow arbitrary paths in space and widened the potential use of the robot to more sophisticated applications such as assembly and welding. Scheinman designed a second arm for the MIT AI Lab, called the "MIT arm." Scheinman, after receiving a fellowship from Unimation to develop his designs, sold those designs to Unimation who further developed them with support from General Motors and marketed it as the Programmable Universal Machine for Assembly. Industrial robotics took off quite in Europe, with both ABB Robotics and KUKA Robotics bringing robots to the market in 1973. ABB Robotics introduced IRB 6, among the world's first commercially available all electric micro-processor controlled robot; the first two IRB 6 robots were sold to Magnusson in Sweden for grinding and polishing pipe bends and were installed in production in January 1974. In 1973 KUKA Robotics built its first robot, known as FAMULUS one of the first articulated robots to have six electromechanically driven axes.
Interest in robotics increased in the late 1970s and many US companies entered the field, including large firms like General Electric, General Motors. U. S. startup companies included Adept Technology, Inc.. At the height of the robot boom in 1984, Unimation was acquired by Westinghouse Electric Corporation for 107 million U. S. dollars. Westinghouse sold Unimation to Stäubli Faverges SCA of France in 1988, still making articulated robots for general industrial and cleanroom applications and bought the robotic division of Bosch in late 2004. Only a few non-Japanese companies managed to survive in this market, the major ones being: Adept Technology, Stäubli, the Swedish-Swiss company ABB Asea Brown Boveri, the German company KUKA Robotics and the Italian company Comau. Number of axes – two axes are required to reach any point in a plane. To control the orientation of the end of the arm three more axes (yaw, pit
Supervisory Control and Data Acquisition is a control system architecture that uses computers, networked data communications and graphical user interfaces for high-level process supervisory management, but uses other peripheral devices such as programmable logic controller and discrete PID controllers to interface with the process plant or machinery. The use of SCADA has been considered for management and operations of project-driven-process in construction; the operator interfaces that enable monitoring and the issuing of process commands, such as controller set point changes, are handled through the SCADA computer system. However, the real-time control logic or controller calculations are performed by networked modules that connect to the field sensors and actuators; the SCADA concept was developed as a universal means of remote access to a variety of local control modules, which could be from different manufacturers allowing access through standard automation protocols. In practice, large SCADA systems have grown to become similar to distributed control systems in function, but using multiple means of interfacing with the plant.
They can control large-scale processes that can include multiple sites, work over large distances as well as small distance. It is one of the most commonly-used types of industrial control systems, however there are concerns about SCADA systems being vulnerable to cyberwarfare/cyberterrorism attacks; the key attribute of a SCADA system is its ability to perform a supervisory operation over a variety of other proprietary devices. The accompanying diagram is a general model which shows functional manufacturing levels using computerised control. Referring to the diagram, Level 0 contains the field devices such as flow and temperature sensors, final control elements, such as control valves. Level 1 contains the industrialised input/output modules, their associated distributed electronic processors. Level 2 contains the supervisory computers, which collate information from processor nodes on the system, provide the operator control screens. Level 3 is the production control level, which does not directly control the process, but is concerned with monitoring production and targets.
Level 4 is the production scheduling level. Level 1 contains the programmable logic controllers or remote terminal units. Level 2 contains the SCADA computing platform; the SCADA software exists only at this supervisory level as control actions are performed automatically by RTUs or PLCs. SCADA control functions are restricted to basic overriding or supervisory level intervention. For example, a PLC may control the flow of cooling water through part of an industrial process to a set point level, but the SCADA system software will allow operators to change the set points for the flow; the SCADA enables alarm conditions, such as loss of flow or high temperature, to be displayed and recorded. A feedback control loop is directly controlled by the RTU or PLC, but the SCADA software monitors the overall performance of the loop. Levels 3 and 4 are not process control in the traditional sense, but are where production control and scheduling takes place. Data acquisition begins at the RTU or PLC level and includes instrumentation readings and equipment status reports that are communicated to level 2 SCADA as required.
Data is compiled and formatted in such a way that a control room operator using the HMI can make supervisory decisions to adjust or override normal RTU controls. Data may be fed to a historian built on a commodity database management system, to allow trending and other analytical auditing. SCADA systems use a tag database, which contains data elements called tags or points, which relate to specific instrumentation or actuators within the process system according to such as the Piping and instrumentation diagram. Data is accumulated against these unique process control equipment tag references. Both large and small systems can be built using the SCADA concept; these systems can range from just tens depending on the application. Example processes include industrial and facility-based processes, as described below: Industrial processes include manufacturing, Process control, power generation and refining, may run in continuous, repetitive, or discrete modes. Infrastructure processes may be public or private, include water treatment and distribution, wastewater collection and treatment and gas pipelines, electric power transmission and distribution, wind farms.
Facility processes, including buildings, airports and space stations. They monitor and control heating and air conditioning systems and energy consumption. However, SCADA systems may have security vulnerabilities, so the systems should be evaluated to identify risks and solutions implemented to mitigate those risks. A SCADA system consists of the following main elements: This is the core of the SCADA system, gathering data on the process and sending control commands to the field connected devices, it refers to the computer and software responsible for communicating with the field connection controllers, which are RTUs and PLCs, includes the HMI software running on operator workstations. In smaller SCADA systems, the supervisory computer may be composed of a single PC, in which case the HMI is a part of this computer. In larger SCADA systems, the master station may include several HMIs hosted on client computers, multiple servers for data acquisition, distributed software applications, disaster recovery sites.
To increase the integrity of the system the multiple servers will be configured in a dual-redundant or hot-standby formation