1. Introduction
Intelligent or smart sensors have been known for more than two decades. These sensors are more sophisticated than traditional sensors as they gather, analyse and transmit data. A state-of-the-art of intelligent sensors covering the last two decades are found in [1,2]. According to IEEE 1451.2 specifications, a smart sensor is a version of smart transducer that provides functions beyond those necessary for generating a correct representation of a sensed or controlled quantity. This functionality typically simplifies the integration of the transducer into applications in a networked environment.
The basic principle of an intelligent (i.e. smart) sensor is that its complexities must be concealed internally and must be transparent to the host system. Smart sensors are designed to present a simple face to the host structure via a digital interface, such that the complexity is borne by the sensor and not by the central signal processing system [3]. So far, an exact definition is still indefinite. However, generic concept of an intelligent sensor can be described according to Schodel [4]: ‘It is common to call a sensor intelligent, if just a microprocessor device is assembled at the location of the sensor transducer, to implement filtering and other simple pre-processing tasks at the location of the sensor’. In addition to the previous concept, Brignell [5] describes intelligent sensor that ‘modifies its internal behaviour to optimise its ability to collect data from the physical world and communicate them in a responsive manner to a host system’. This concept is similar to the concept given by Chita [6]. The ultimate intention of developing intelligent sensors is to imitate human abilities such as multiple functions for sensing objects simultaneously, learning with adapting capabilities and decision making [7]. The development of intelligent sensors rests on advances in hardware (i.e. measurement technology) and advances in software (i.e. processing technology). Advancing in microelectronic, microcomputer and manufacturing technologies enables an integration of sensing elements and signal processing elements embedded into a single chip found for example in Micro-Electro-Mechanical System (MEMS). For multiple measurements, Nagel [8] introduced the concept of ‘cluster’; the integral of several different sensing elements uses common computing and communicating capabilities and shares a power supply to imitate the human abilities. Continuous improvement in MEMS technology can ease this implementation. The use of such sensors is very promising in manufacturing as there is a clear trend towards modularity of future holistic intelligent Computer Numerically Controlled Machines (CNC), PLCs and Robots based on distributed control design which allows flexible control configuration and adaptation of systems. Such systems are governed by intelligent control systems consisting of a hierarchical structure of production control, machine control and drive control layers that have to implement open interfaces, learning capabilities, self-tuning mechanisms and sophisticated model-based prediction instruments in order to allow automated error-free machining for example. The future multi process autonomous machine is expected to be equipped with new concepts of multi agent sensors cluster to enable a full control of diverse variables such as cutting parameters, in-process dimensional measurement, geometric and form defects (Figure 1). After a static, dynamic and thermal analysis, a sensor mapping strategy on the machine is of paramount importance to ensure continuous and accurate feedback. As an example, various wireless sensors are placed at high amplitudes locations of major modes of vibrations, while thermal sensors are located in critical locations subject to larger expansions. With such a sensor mapping, it is planned to implement compensation of errors and forces while machining to secure zero defect workpiece in the autonomous mode
The use of reconfigurable sensors for maintenance requires data to participate in feature recognition in order to help in the identification of possible failures types and immediately decide on the actions to be taken (self diagnostic and self service strategy, self healing requiring autonomous supervision). In large complex plants such as chemical applications requiring safety and reliability, the processes are based on highly automated control systems involving a large number of sensors. These should be reconfigurable and capable of performing data interpretation and fusion from multiple sensors with the validation of local and remotely collected data. This will help in the reduction of false alarms and downtime as well as avoid jeopardizing personnel and environmental safety. Moreover, the data generated by manufacturing plants requires suitable techniques to improve their accuracy and to extract useful information about the operational status of the process. To enable such remarkable features in sensors technology, a number of core characteristics are presented and discussed next
1. บทนำเซนเซอร์อัจฉริยะ หรือสมาร์ทได้รับทราบสำหรับมากกว่าสองทศวรรษ เซนเซอร์เหล่านี้มีความซับซ้อนมากขึ้นกว่าเซ็นเซอร์แบบดั้งเดิมเป็นผู้รวบรวม วิเคราะห์ และส่งข้อมูล พบรัฐของเด่นของเซนเซอร์อัจฉริยะที่ครอบคลุมสองทศวรรษใน [1, 2] ตามข้อกำหนด IEEE 1451.2 เซนเซอร์สมาร์ทเป็นรุ่นพิกัดสมาร์ทที่มีฟังก์ชันเกินกว่าที่จำเป็นสำหรับการสร้างตัวแทนที่ถูกต้องของเหตุการณ์ หรือการควบคุมปริมาณ ฟังก์ชันนี้จะช่วยให้ง่ายรวมของพิกัดนี้ไปยังโปรแกรมประยุกต์ในสภาพแวดล้อมบนเครือข่ายThe basic principle of an intelligent (i.e. smart) sensor is that its complexities must be concealed internally and must be transparent to the host system. Smart sensors are designed to present a simple face to the host structure via a digital interface, such that the complexity is borne by the sensor and not by the central signal processing system [3]. So far, an exact definition is still indefinite. However, generic concept of an intelligent sensor can be described according to Schodel [4]: ‘It is common to call a sensor intelligent, if just a microprocessor device is assembled at the location of the sensor transducer, to implement filtering and other simple pre-processing tasks at the location of the sensor’. In addition to the previous concept, Brignell [5] describes intelligent sensor that ‘modifies its internal behaviour to optimise its ability to collect data from the physical world and communicate them in a responsive manner to a host system’. This concept is similar to the concept given by Chita [6]. The ultimate intention of developing intelligent sensors is to imitate human abilities such as multiple functions for sensing objects simultaneously, learning with adapting capabilities and decision making [7]. The development of intelligent sensors rests on advances in hardware (i.e. measurement technology) and advances in software (i.e. processing technology). Advancing in microelectronic, microcomputer and manufacturing technologies enables an integration of sensing elements and signal processing elements embedded into a single chip found for example in Micro-Electro-Mechanical System (MEMS). For multiple measurements, Nagel [8] introduced the concept of ‘cluster’; the integral of several different sensing elements uses common computing and communicating capabilities and shares a power supply to imitate the human abilities. Continuous improvement in MEMS technology can ease this implementation. The use of such sensors is very promising in manufacturing as there is a clear trend towards modularity of future holistic intelligent Computer Numerically Controlled Machines (CNC), PLCs and Robots based on distributed control design which allows flexible control configuration and adaptation of systems. Such systems are governed by intelligent control systems consisting of a hierarchical structure of production control, machine control and drive control layers that have to implement open interfaces, learning capabilities, self-tuning mechanisms and sophisticated model-based prediction instruments in order to allow automated error-free machining for example. The future multi process autonomous machine is expected to be equipped with new concepts of multi agent sensors cluster to enable a full control of diverse variables such as cutting parameters, in-process dimensional measurement, geometric and form defects (Figure 1). After a static, dynamic and thermal analysis, a sensor mapping strategy on the machine is of paramount importance to ensure continuous and accurate feedback. As an example, various wireless sensors are placed at high amplitudes locations of major modes of vibrations, while thermal sensors are located in critical locations subject to larger expansions. With such a sensor mapping, it is planned to implement compensation of errors and forces while machining to secure zero defect workpiece in the autonomous modeใช้เซ็นเซอร์ reconfigurable สำหรับการบำรุงรักษาต้องการข้อมูลการมีส่วนร่วมในการรับรู้คุณลักษณะเพื่อช่วยในการระบุชนิดของความล้มเหลวได้ และตัดสินใจทันทีในการดำเนินการที่จะใช้ (บริการตนเอง และวิเคราะห์ตนเองกลยุทธ์ ซ่อมแซมต้องปกครองดูแลตัวเอง) ในขนาดใหญ่ซับซ้อนพืชเช่นโปรแกรมประยุกต์เคมีที่ต้องการความปลอดภัยและความน่าเชื่อถือ กระบวนงานในระบบควบคุมอัตโนมัติสูงที่เกี่ยวข้องกับเซนเซอร์จำนวนมาก เหล่านี้ควรจะสามารถดำเนินการตีความข้อมูลและผสมผสานจากหลายเซนเซอร์มีความถูกต้องของข้อมูลท้องถิ่น และรวบรวมจากระยะไกล และ reconfigurable นี้จะช่วยในการลดและหยุดทำงาน ตลอดจนหลีกเลี่ยง jeopardizing บุคลากรและความปลอดภัยสิ่งแวดล้อม นอกจากนี้ ข้อมูลที่สร้างขึ้น โดยโรงงานผลิตต้องเหมาะสมเทคนิค การปรับปรุงความแม่นยำของพวกเขา และแยกข้อมูลที่เป็นประโยชน์เกี่ยวกับสถานะการดำเนินงานของกระบวนการ เพื่อเปิดใช้งานคุณลักษณะที่โดดเด่นเช่นเทคโนโลยีเซนเซอร์ ลักษณะหลักที่แสดง และกล่าวต่อไป
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