Special Sensors

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3.2.3 Special Purpose Sensors

There are various sensing devices which do not meet the criteria of being either digital or analog.

3.2.3.1 Encoders and Resolvers
Encoders

use a rotating glass or metal disk with slots or perforations along the circumference. An LED emits light along the path of the slots creating a train of pulses which can be used to count or measure distance. By placing two sets of slots 90 degrees out of phase with each other the direction of rotation can also be determined. These two signals are known as the A and B pulses of the encoder. The inverse of the encoder A and B pulses are also often used, commonly know as A not and B not. A single slot is also place along the circumference known as the Z pulse; this is used for identifying the home or reference position of the encoder or device attached to it. This offset A and B pulse configuration is known as quadrature.

Encoders are usually of the multi-turn variety, that is they will turn multiple times providing a count much higher than the number of slots on the disk. This means that the High Speed Counter or servo module that the encoder is connected to must keep track of the number of turns or total count of the pulses. If the power is removed from the counter or control system it is necessary to “home” the axis or device attached to the encoder, typically to an external “home” sensor and the Z pulse.

Absolute encoders use a parallel signal to provide a binary count of the position of the encoder. This means that the encoder will have a fixed range or number of turns. These are often used when a system or axis must know its position even when powered off and moved.

Resolvers

are a type of rotating electrical transformer used for measuring degrees of rotation. The most common type is the brushless transmitter resolver. This type of resolver is similar to an electric motor in that it has a rotor and stator. The stator portion houses three windings, an exciter winding and two two-phase windings, usually labeled X and Y. The X and Y windings are located at the bottom wound on a lamination while the exciter winding is located at the top. The primary winding of the transformer, fixed to the stator, is excited by a sinusoidal electric current, which by electromagnetic induction induces current to flow through the secondary windings along the stator. The two two-phase windings, fixed at right (90°) angles to each other on the stator, produce a sine and cosine feedback current by the same induction process. The relative magnitudes of the two-phase voltages are measured and used to determine the angle of the rotor relative to the stator. Upon one full revolution, the feedback signals repeat their waveforms. Because resolvers are analog, they effectively have infinite resolution until converted to a digital signal.

3.2.3.2 Vision Systems

Also known as Machine Vision, vision systems apply computer based vision processing to industry and manufacturing. Whereas computer vision is mainly focused on machine-based image processing, machine vision most often requires also digital input/output devices and computer networks to control other manufacturing equipment such as robotic arms. Machine Vision is a subfield of engineering that incorporates computer science, optics, mechanical engineering, and industrial automation. One of the most common applications of Machine Vision is the inspection of manufactured goods such as semiconductor chips, automobiles, food and pharmaceuticals. Just as human inspectors working on assembly lines visually inspect parts to judge the quality of workmanship, so machine vision systems use digital cameras, smart cameras and image processing software to perform similar inspections.

Machine vision systems are programmed to perform narrowly defined tasks such as counting objects on a conveyor, reading serial numbers, and searching for surface defects. Manufacturers favor machine vision systems for visual inspections that require high-speed, high-magnification, 24-hour operation, and/or repeatability of measurements. Frequently these tasks extend roles traditionally occupied by human beings whose degree of failure is classically high through distraction, illness and circumstance. However, humans may display finer perception over the short period and greater flexibility in classification and adaptation to new defects and quality assurance policies.

Computers do not ‘see’ in the same way that human beings are able to. Cameras are not equivalent to human optics and while people can rely on inference systems and assumptions, computing devices must ‘see’ by examining individual pixels of images, processing them and attempting to develop conclusions with the assistance of knowledge bases and features such as pattern recognition engines. Although some machine vision algorithms have been developed to mimic human visual perception, a number of unique processing methods have been developed to process images and identify relevant image features in an effective and consistent manner. Machine vision and computer vision systems are capable of processing images consistently, but computer-based image processing systems are typically designed to perform single, repetitive tasks, and despite significant improvements in the field, no machine vision or computer vision system can yet match some capabilities of human vision in terms of image comprehension, tolerance to lighting variations and image degradation, parts’ variability etc.

A typical machine vision system will consist of several among the following components:
1. One or more digital or analog cameras (black-and-white or color) with suitable optics for acquiring images
2. Camera interface for digitizing images (widely known as a “frame grabber”)
3. A processor (often a PC or embedded processor, such as a DSP)
4. In some cases, all of the above are combined within a single device, called a smart camera.
5. Input/Output hardware (e.g. digital I/O) or communication links (e.g. network connection or RS-232) to report results
6. Lenses to focus the desired field of view onto the image sensor.
7. Suitable, often very specialized, light sources (LED illuminators, fluorescent or halogen lamps etc.)
8. A program to process images and detect relevant features.
9. A synchronizing sensor for part detection (often an optical or magnetic sensor) to trigger image acquisition and processing.
10. Some form of actuator used to sort or reject defective parts.

The sync sensor determines when a part (often moving on a conveyor) is in position to be inspected. The sensor triggers the camera to take a picture of the part as it passes beneath the camera and often synchronizes a lighting pulse to freeze a sharp image. The lighting used to illuminate the part is designed to highlight features of interest and obscure or minimize the appearance of features that are not of interest (such as shadows or reflections). LED panels of suitable sizes and arrangement are often used to this purpose.

The camera’s image is captured by the framegrabber. A framegrabber is a digitizing device (within a smart camera or as a separate computer card) that converts the output of the camera to digital format (typically a two dimensional array of numbers, corresponding to the luminous intensity level of the corresponding point in the field of view, called a pixel) and places the image in computer memory so that it may be processed by the machine vision software.

The software will typically take several steps to process an image. Often the image is first manipulated to reduce noise or to convert many shades of gray to a simple combination of black and white (binarization). Following the initial simplification, the software will count, measure, and/or identify objects, dimensions, defects or other features in the image. As a final step, the software passes or fails the part according to programmed criteria. If a part fails, the software may signal a mechanical device to reject the part; alternately, the system may stop the production line and warn a human worker to fix the problem that caused the failure.

Though most machine vision systems rely on black-and-white cameras, the use of color cameras is becoming more common. It is also increasingly common for Machine Vision systems to include digital camera equipment for direct connection rather than a camera and separate framegrabber, thus reducing signal degradation.

“Smart” cameras with built-in embedded processors are capturing an increasing share of the machine vision market. The use of an embedded (and often very optimized) processor eliminates the need for a framegrabber card and external computer, thus reducing cost and complexity of the system while providing dedicated processing power to each camera. Smart cameras are typically less expensive than systems comprising a camera and a board and/or external computer, while the increasing power of embedded processors and DSPs is often providing comparable or higher performance and capabilities than conventional PC-based systems.

3.2.3.3 Gas Chromatography

Gas chromatography-mass spectrometry (GC-MS) is a method that combines the features of gas-liquid chromatography and mass spectrometry to identify different substances within a test sample. This can be used in some chemical and process plants as a means of identifying and separating substances.

3.2.3.4 RFID, Inductive ID and Bar Codes
RFID

or Radio Frequency Identification systems are used as a means of tagging and identifying parts as they move within an area. Battery powered or passive RF tags are attached to objects such as pallets or containers. These tags contain serial number or itemized information concerning the object they are attached to for tracking purposes. Strategically located antennas or readers are located along the path of movement and can read or write information to and from the tags as they pass.
Most RFID tags contain at least two parts. One is an integrated circuit for storing and processing information, modulating and demodulating a radio-frequency (RF) signal, and other specialized functions. The second is an antenna for receiving and transmitting the signal.

There are generally two types of RFID tags: active RFID tags, which contain a battery and thus can transmit its signal autonomously, and passive RFID tags, which have no battery and require an external source to initiate signal transmission. RFID Systems usually operate either in the HF (High Frequency) or UHF (Ultra High Frequency) range of the radio spectrum.

Inductive ID systems

serve a similar function to RFID systems but use a coil of wire similar to a proximity switch. The reader will excite an oscillator circuit in the tag which will transmit a serial code. Inductive ID systems can be lower cost and less susceptible to radio interference, but typically handle less information.

Bar code

readers and tags are used to track parts also. The reader contains an LED or laser light source which reflects off of a tag with light and dark marks usually arranged in lines of varying thickness. These are then decoded into alphanumeric information. To cover a larger read area, the transmitted light will sometimes “raster” or move up and down.

There are several codes used for the bar code tags. The reader must be set up to read the proper code.

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