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Development of Intelligent Sensor System

Info: 5410 words (22 pages) Dissertation
Published: 12th Dec 2019

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Tagged: EngineeringInformation Systems

Chapter 1

1.1 Introduction

What is Automation?

Automation in general, can be explained as the use of computers or microcontrollers to control industrial machinery and processes thereby fully replacing human operators. Automation is a kind of transition from mechanization. In mechanization, human operators are provided with machinery to assist their operations, where as automation fully replaces the human operators with computers.

The advantages of automation are:

  1. Increased productivity and higher production rates.
  2. Better product quality and efficient use of resources.
  3. Greater control and consistency of products.
  4. Improved safety and reduced factory lead times.

Home Automation

Home automation is the field specializing in the general and specific automation requirements of homes and apartments for their better safety, security and comfort of its residents. It is also called Domotics. Home automation can be as simple as controlling a few lights in the house or as complicated as to monitor and to record the activities of each resident. Automation requirements depend on person to person. Some may be interested in the home security while others will be more into comfort requirements. Basically, home automation is anything that gives automatic control of things in your house.

Some of the commonly used features in home automation are:

  • Control of lighting.
  • Climate control of rooms.
  • Security and surveillance systems.
  • Control of home entertainment systems.
  • House plant watering system.
  • Overhead tank water level controllers.

Intelligent Sensors

Complex large-scale systems consist of a large number of interconnected components. Mastering the dynamic behavior of such systems, calls for distributed control architectures. This can be achieved by implementing control and estimation algorithms in several controllers. Some algorithms manipulate only local variables (which are available in the local interface) but in most cases, algorithms implemented in some given computing device will use variables which are available in this device’s local interface, and also variables which are input to the control system via remote interfaces, thus rising the need for communication networks, whose architecture and complexity depend on the amount of data to be exchanged, and on the associated time constraints. Associating computing (and communication) devices with sensing or actuating functions, has given rise to intelligent sensors. These sensors have gained a huge success in the past ten years, especially with the development of neural networks, fuzzy logic, and soft computing algorithms.

The modern definition of smart or intelligent sensors can be formulated now as: ‘Smart sensor is an electronic device, including sensing element, interfacing, signal processing and having several intelligence functions as self-testing, self-identification, self-validation or self-adaptation’. The keyword in this definition is ‘intelligence’. The self-adaptation is a relatively new function of smart sensors and sensor systems. Self-adaptation smart sensors and systems are based on so-called adaptive algorithms and directly connected with precision measurements of frequency-time parameters of electrical signals.

The later chapters will give an elaborate view on why we should use intelligent sensors, intelligent sensor structure, characteristics and network standards.

Chapter 2

2.1 Conventional Sensors

Before talking more on intelligent sensors, first we need to examine regular sensors in order to obtain a solid foundation on which we can develop our understanding on intelligent sensors. Most of the conventional sensors have shortcomings, both technically and economically. For a sensor to work effectively, it must be calibrated. That is, its output must be made to match some predetermined standard so that its reported values correctly reflect the parameter being measured. In the case of a bulb thermometer, the graduations next to the mercury column must be positioned so that they accurately correspond to the level of mercury for a given temperature. If the sensor is not calibrated, the information that it reports won’t be accurate, which can be a big problem for the systems that use the reported information.

The second concern one has when dealing with sensors is that their properties usually change over time, a phenomenon knows as drift. For instance, suppose we are measuring a DC current in a particular part of a circuit by monitoring the voltage across a resistor in that circuit. In this case, the sensor is the resistor and the physical property that we are measuring the voltage across it. As the resistor ages, its chemical properties will change, thus altering its resistance. As with the issue of calibration, some situations require much stricter drift tolerances than others; the point is that sensor properties will change with time unless we compensate for the drift in some fashion, and these changes are usually undesirable.

The third problem is that not only do sensors themselves change with time, but so, too, does the environment in which they operate. An excellent example of that would be the electronic ignition for an internal combustion engine. Immediately after a tune-up, all the belts are tight, the spark plugs are new, the fuel injectors are clean, and the air filter is pristine. From that moment on, things go downhill; the belts loosen, deposits build up on the spark plugs and fuel injectors, and the air filter becomes clogged with ever-increasing amounts of dirt and dust. Unless the electronic ignition can measure how things are changing and make adjustments, the settings and timing sequence that it uses to fire the spark plugs will become progressively mismatched for the engine conditions, resulting in poorer performance and reduced fuel efficiency. The ability to compensate for often extreme changes in the operating environment makes a huge difference in a sensor’s value to a particular application.

Yet a fourth problem is that most sensors require some sort of specialized hardware called signal-conditioning circuitry in order to be of use in monitoring or control applications. The signal-conditioning circuitry is what transforms the physical sensor property that we’re monitoring (often an analog electrical voltage that varies in some systematic way with the parameter being measured) into a measurement that can be used by the rest of the system. Depending upon the application, the signal conditioning may be as simple as a basic amplifier that boosts the sensor signal to a usable level or it may entail complex circuitry that cleans up the sensor signal and compensates for environmental conditions, too. Frequently, the conditioning circuitry itself has to be tuned for the specific sensor being used, and for analog signals that often means physically adjusting a potentiometer or other such trimming device. In addition, the configuration of the signal-conditioning circuitry tends to be unique to both the specific type of sensor and to the application itself, which means that different types of sensors or different applications frequently need customized circuitry.

Finally, standard sensors usually need to be physically close to the control and monitoring systems that receive their measurements. In general, the farther a sensor is from the system using its measurements, the less useful the measurements are. This is due primarily to the fact that sensor signals that are run long distances are susceptible to electronic noise, thus degrading the quality of the readings at the receiving end. In many cases, sensors are connected to the monitoring and control systems using specialized (and expensive) cabling; the longer this cabling is, the more costly the installation, which is never popular with end users. A related problem is that sharing sensor outputs among multiple systems becomes very difficult, particularly if those systems are physically separated. This inability to share outputs may not seem important, but it severely limits the ability to scale systems to large installations, resulting in much higher costs to install and support multiple redundant sensors.

What we really need to do is to develop some technique by which we can solve or at least greatly alleviate these problems of calibration, drift, and signal conditioning.

2.2 Making Sensors Intelligent

Control systems are becoming increasingly complicated and generate increasingly complex control information. Control must nevertheless be exercised, even under such circumstances. Even considering just the detection of abnormal conditions or the problems of giving a suitable warning, devices are required that can substitute for or assist human sensation, by detecting and recognizing multi-dimensional information, and conversion of non visual information into visual form. In systems possessing a high degree of functionality, efficiency must be maximized by division of the information processing function into central processing and processing dispersed to local sites. With increased progress in automation, it has become widely recognized that the bottleneck in such systems lies with the sensors.

Such demands are difficult to deal with by simply improvising the sensor devices themselves. Structural reinforcement, such as using array of sensors, or combinations of different types of sensors, and reinforcement from the data processing aspect by a signal processing unit such as a computer, are indispensible. In particular, the data processing and sensing aspects of the various stages involved in multi-dimensional measurement, image construction, characteristic extraction and pattern recognition, which were conventionally performed exclusively by human beings, have been tremendously enhanced by advances in micro-electronics. As a result, in many cases sensor systems have been implemented that substitute for some or all of the intellectual actions of human beings, i.e. intelligent sensor systems.

Sensors which are made intelligent in this way are called ‘intelligent sensors’ or ‘smart sensors’. According to Breckenridge and Husson, the smart sensor itself has a data processing function and automatic calibration/automatic compensation function, in which the sensor itself detects and eliminates abnormal values or exceptional values. It incorporates an algorithm, which is capable of being altered, and has a certain degree of memory function. Further desirable characteristics are that the sensor is coupled to other sensors, adapts to changes in environmental conditions, and has a discriminant function.

Scientific measuring instruments that are employed for observation and measurement of physical world are indispensible extensions of our senses and perceptions in the scientific examination of nature. In recognizing nature, we mobilize all the resources of information obtained from the five senses of sight, hearing, touch, taste and smell etc. and combine these sensory data in such a way as to avoid contradiction. Thus more reliable, higher order data is obtained by combining data of different types. That is, there is a data processing mechanism that combines and processes a number of sensory data. The concept of combining sensors to implement such a data processing mechanism is called ‘sensor fusion’

2.2.1 Digitizing the Sensor Signal

The discipline of digital signal processing or DSP, in which signals are manipulated mathematically rather than with electronic circuitry, is well established and widely practiced. Standard transformations, such as filtering to remove unwanted noise or frequency mappings to identify particular signal components, are easily handled using DSP. Furthermore, using DSP principles we can perform operations that would be impossible using even the most advanced electronic circuitry.

For that very reason, today’s designers also include a stage in the signal-conditioning circuitry in which the analog electrical signal is converted into a digitized numeric value. This step, called analog-to-digital conversion, A/D conversion, or ADC, is vitally important, because as soon as we can transform the sensor signal into a numeric value, we can manipulate it using software running on a microprocessor. Analog-to-digital converters, or ADCs as they’re referred to, are usually single-chip semiconductor devices that can be made to be highly accurate and highly stable under varying environmental conditions. The required signal-conditioning circuitry can often be significantly reduced, since much of the environmental compensation circuitry can be made a part of the ADC and filtering can be performed in software.

2.2.2 Adding Intelligence

Once the sensor signal has been digitized, there are two primary options in how we handle those numeric values and the algorithms that manipulate them. We can either choose to implement custom digital hardware that essentially “hard-wires” our processing algorithm, or we can use a microprocessor to provide the necessary computational power. In general, custom hardware can run faster than microprocessor-driven systems, but usually at the price of increased production costs and limited flexibility. Microprocessors, while not necessarily as fast as a custom hardware solution, offer the great advantage of design flexibility and tend to be lower-priced since they can be applied to a variety of situations rather than a single application.

Once we have on-board intelligence, we’re able to solve several of the problems that we noted earlier. Calibration can be automated, component drift can be virtually eliminated through the use of purely mathematical processing algorithms, and we can compensate for environmental changes by monitoring conditions on a periodic basis and making the appropriate adjustments automatically. Adding a brain makes the designer’s life much easier.

2.2.3 Communication Interface

The sharing of measurements with other components within the system or with other systems adds to the value of these measurements. To do this, we need to equip our intelligent sensor with a standardized means to communicate its information to other elements. By using standardized methods of communication, we ensure that the sensor’s information can be shared as broadly, as easily, and as reliably as possible, thus maximizing the usefulness of the sensor and the information it produces.

Thus these three factors consider being mandatory for an intelligent sensor:

  • A sensing element that measures one or more physical parameters (essentially the traditional sensor we’ve been discussing),
  • A computational element that analyzes the measurements made by the sensing element, and
  • A communication interface to the outside world that allows the device to exchange information with other components in a larger system.

It’s the last two elements that really distinguish intelligent sensors from their more common standard sensor relatives because they provide the abilities to turn data directly into information, to use that information locally, and to communicate it to other elements in the system.

2.3 Types of Intelligent Sensors

Intelligent sensors are chosen depending on the object, application, precision system, environment of use and cost etc. In such cases consideration must be given as to what is an appropriate evaluation standard. This question involves a multi-dimensional criterion and is usually very difficult. The evaluation standard directly reflects the sense of value itself applied in the design and manufacture of the target system. This must therefore be firmly settled at the system design stage.

In sensor selection, the first matter to be considered is determination of the subject of measurement. The second matter to be decided on is the required precision and dynamic range. The third is ease of use, cost, delivery time etc., and ease of maintenance in actual use and compatibility with other sensors in the system. The type of sensor should be matched to such requirements at the design stage. Sensors are usually classified by the subject of measurement and the principle of sensing action.

2.3.1 Classification Based on Type of Input

In this, the sensor is classified in accordance with the physical phenomenon that is needed to be detected and the subject of measurement. Some of the examples include voltage, current, displacement and pressure. A list of sensors and their categories are mentioned in the following table.



Dynamic Quantity

Flow rate, Pressure, force, tension

Speed, acceleration

Sound, vibration

Distortion, direction proximity

Optical Quantities

Light (infra red, visible light or radiation)

Electromagnetic Quantities

Current, voltage, frequency, phase, vibration, magnetism

Quantity of Energy or Heat

Temperature, humidity, dew point

Chemical Quantities

Analytic sensors, gas, odour, concentration, pH, ions

Sensory Quantities or Biological Quantities

Touch, vision, smell

Table 2.3.1: Sensed items Classified in accordance with subject of measurement.

2.3.2 Classification Based on Type of Output

In an intelligent sensor, it is often necessary to process in an integrated manner the information from several sensors or from a single sensor over a given time range. A computer of appropriate level is employed for such purposes in practically y all cases. For coupling to the computer when constructing an intelligent sensor system, a method with a large degree of freedom is therefore appropriate. It is also necessary to pay careful attention to the type of physical quantity carrying the output information to the sensor, and to the information description format of this physical quantity or dynamic quantity, and for the description format an analog, digital or encoded method etc., might be used.

Although any physical quantities could be used as output signal, electrical quantities such as voltage are more convenient for data input to a computer. The format of the output signal can be analog or digital. For convenience in data input to the computer, it is preferable if the output signal of the sensor itself is in the form of a digital electrical signal. In such cases, a suitable means of signal conversion must be provided to input the data from the sensor to the computer

2.3.3 Classification Based on Accuracy

When a sensor system is constructed, the accuracy of the sensors employed is a critical factor. Usually sensor accuracy is expressed as the minimum detectable quantity. This is determined by the sensitivity of the sensor and the internally generated noise of the sensor itself. Higher sensitivity and lower internal noise level imply greater accuracy.

Generally for commercially available sensors the cost of the sensor is determined by the accuracy which it is required to have. If no commercial sensor can be found with the necessary accuracy, a custom product must be used, which will increase the costs. For ordinary applications an accuracy of about 0.1% is sufficient. Such sensors can easily be selected from commercially available models. Dynamic range (full scale deflection/minimum detectable quantity) has practically the same meaning as accuracy, and is expressed in decibel units. For example a dynamic range of 60dB indicates that the full scale deflection is 103 times the minimum detectable quantity. That is, a dynamic range of 60dB is equivalent to 0.1% accuracy.

In conventional sensors, linearity of output was regarded as quite important. However, in intelligent sensor technology the final stage is normally data processing by computer, so output linearity is not a particular problem. Any sensor providing a reproducible relationship of input and output signal can be used in an intelligent sensor system.

Chapter 3

3.1 Sensor selection

The function of a sensor is to receive some action from a single phenomenon of the subject of measurement and to convert this to another physical phenomenon that can be more easily handled. The phenomenon constituting the subject of measurement is called the input signal, and the phenomenon after conversion is called the output signal. The ratio of the output signal to the input signal is called the transmittance or gain. Since the first function of a sensor is to convert changes in the subject of measurement to a physical phenomenon that can be more easily handled, i.e. its function consists in primary conversion, its conversion efficiency, or the degree of difficulty in delivering the output signal to the transducer constituting the next stage is of secondary importance

The first point to which attention must be paid in sensor selection is to preserve as far as possible the information of the input signal. This is equivalent to preventing lowering of the signal-to-noise ratio (SNR). For example, if the SNR of the input signal is 60 dB, a sensor of dynamic range less than 60 dB should not be used. In order to detect changes in the quantity being measured as faithfully as possible, a sensor is required to have the following properties.

  • Non-interference. This means that its output should not be changed by factors other than changes in the subject of measurement. Conversion satisfying this condition is called direct measurement. Conversion wherein the measurement quantity is found by calculation from output signals determined under the influence of several input signals is called indirect measurement.
  • High sensitivity. The amount of change of the output signal that is produced by a change of unit amount of the input quantity being measured, i.e. the gain, should be as large as possible.
  • Small measurement pressure. This means that the sensor should not disturb the physical conditions of the subject of measurement. From this point of view, modulation conversion offers more freedom than direct-acting conversion.
  • High speed. The sensor should have sufficiently high speed of reaction to track the maximum anticipated rate of variation of the measured quantity.
  • Low noise. The noise generated by the sensor itself should be as little as possible.
  • Robustness. The output signal must be at least more robust than the quantity being measured, and be easier to handle. ‘Robustness’ means resistance to environmental changes and/or noise. In general, phenomena of large energy are more resistant to external disturbance such as noise than are phenomena of smaller energy, they are easier to handle, and so have better robustness.

If a sensor can be obtained that satisfies all these conditions, there is no problem. However, in practice, one can scarcely expect to obtain a sensor satisfying all these conditions. In such cases, it is necessary to combine the sensor with a suitable compensation mechanism, or to compensate the transducer of the secondary converter.

Progress in IC manufacturing technology has made it possible to integrate various sensor functions. With the progressive shift from mainframes to minicomputers and hence to microcomputers, control systems have changed from centralized processing systems to distributed processing systems. Sensor technology has also benefited from such progress in IC manufacturing technology, with the result that systems whereby information from several sensors is combined and processed have changed from centralized systems to dispersed systems. Specifically, attempts are being made to use silicon-integrated sensors in a role combining primary data processing and input in systems that measure and process two-dimensional information such as picture information. This is a natural application of silicon precision working technology and digital circuit technology, which have been greatly advanced by introduction of VLSI manufacturing technology. Three-dimensional integrated circuits for recognizing letter patterns and odour sensors, etc., are examples of this. Such sensor systems can be called perfectly intelligent sensors in that they themselves have a certain data processing capability. It is characteristic of such sensors to combine several sensor inputs and to include a microprocessor that performs data processing. Their output signal is not a simple conversion of the input signal, but rather an abstract quantity obtained by some reorganization and combination of input signals from several sensors.

This type of signal conversion is now often performed by a distributed processing mechanism, in which microprocessors are used to carry out the data processing that was previously performed by a centralized computer system having a large number of interfaces to individual sensors. However, the miniaturization obtained by application of integrated circuit techniques brings about an increase in the flexibility of coupling between elements. This has a substantial effect. Sensors of this type constitute a new technology that is at present being researched and developed. Although further progress can be expected, the overall picture cannot be predicted at the present time. Technically, practically free combinations of sensors can be implemented with the object of so-called indirect measurement, in which the signals from several individual sensors that were conventionally present are collected and used as the basis for a new output signal. In many aspects, new ideas are required concerning determination of the object of measurement, i.e. which measured quantities are to be selected, determination of the individual functions to achieve this, and the construction of the framework to organize these as a system.

3.2 Structure of an Intelligent Sensor

The rapidity of development in microelectronics has had a profound effect on the whole of instrumentation science, and it has blurred some of the conceptual boundaries which once seemed so firm. In the present context the boundary between sensors and instruments is particularly uncertain. Processes which were once confined to a large electronic instrument are now available within the housing of a compact sensor, and it is some of these processes which we discuss later in this chapter. An instrument in our context is a system which is designed primarily to act as a free standing device for performing a particular set of measurements; the provision of communications facilities is of secondary importance. A sensor is a system which is designed primarily to serve a host system and without its communication channel it cannot serve its purpose. Nevertheless, the structures and processes used within either device, be they hardware or software, are similar.

The range of disciplines which arc brought together in intelligent sensor system design is considerable, and the designer of such systems has to become something of a polymath. This was one of the problems in the early days of computer-aided measurement and there was some resistance from the backwoodsmen who practiced the ‘art’ of measurement.

3.2.1 Elements of Intelligent Sensors

The intelligent sensor is an example of a system, and in it we can identify a number of sub-systems whose functions are clearly distinguished from each other. The principal sub-systems within an intelligent sensor are:

  • A primary sensing element
  • Excitation Control
  • Amplification (Possibly variable gain)
  • Analogue filtering
  • Data conversion
  • Compensation
  • Digital Information Processing
  • Digital Communication Processing

The figure illustrates the way in which these sub-systems relate to each other. Some of the realizations of intelligent sensors, particularly the earlier ones, may incorporate only some of these elements.

The primary sensing element has an obvious fundamental importance. It is more than simply the familiar traditional sensor incorporated into a more up-to-date system. Not only are new materials and mechanisms becoming available for exploitation, but some of those that have been long known yet discarded because of various difficulties of behaviour may now be reconsidered in the light of the presence of intelligence to cope with these difficul­ties.

Excitation control can take a variety of forms depending on the circumstances. Some sensors, such as the thermocouple, convert energy directly from one form to another without the need for additional excitation. Others may require fairly elaborate forms of supply. It may be alternating or pulsed for subsequent coherent or phase-sensitive detection. In some circumstances it may be necessary to provide extremely stable supplies to the sensing element, while in others it may be necessary for those supplies to form part of a control loop to maintain the operating condition of the clement at some desired optimum. While this aspect may not be thought fundamental to intelligent sensors there is a largely unexplored range of possibilities for combining it with digital processing to produce novel instrumentation techniques.

Amplification of the electrical output of the primary sensing element is almost invariably a requirement. This can pose design problems where high gain is needed. Noise is a particular hazard, and a circumstance unique to the intelligent form of sensor is the presence of digital buses carrying signals with sharp transitions. For this reason circuit layout is a particularly important part of the design process.

Analogue filtering is required at minimum to obviate aliasing effects in the conversion stage, but it is also attractive where digital filtering would lake up too much of the real-time processing power available.

Data conversion is the stage of transition between the continuous real world and the discrete internal world of the digital processor. It is important to bear in mind that the process of analogue to digital conversion is a non-linear one and represents a potentially gross distortion of the incoming information. It is important, however, for the intelligent sensor designer always to remember that this corruption is present, and in certain circumstances it can assume dominating importance. Such circumstances would include the case where the conversion process is part of a control loop or where some sort of auto-ranging, overt or covert, is built in to the operational program.

Compensation is an inevitable part of the intelligent sensor. The operating point of the sensors may change due to various reasons. One of them is temperature. So an intelligent sensor must have an inbuilt compensation setup to bring the operating point back to its standard set stage.

Information processing is, of course, unique to the intelligent form of sensor. There is some overlap between compensation and information processing, but there are also significant areas on independence.

An important aspect is the condensation of information, which is necessary to preserve the two most precious resources of the industrial measurement system, the information bus and the central processor. A prime example of data condensa­tion occurs in the Doppler velocimctcr in which a substantial quantity of informa­tion is reduced to a single number representing the velocity. Sensor compensation will in general require the processi

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