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Mechatronic Systems – A Short Introduction 19.5 Design Procedures for Mechatronic Systems 325

Actu-

ators

Signal-based

fault detection

s analytical symptoms

f diagnosed faults

Sen-

sors

Process

model

Feature

generation

Threshold-

plausibil.-

check

Change detection

Fault diagnosis

Vibration

signal

models

Process

model-based

fault detection

Normal

behavior

Faults

r, Θ, x features

Fig. 19.9 Scheme for model-based fault detection

higher-leveltasks are sometimes summarized as process

management. Information processing at several levels

under real-time condition is typical for extensive pro-

cess automation (Fig.19.8).

With the increasing number of automatic functions

(autonomy) including electronic components, sensors,

and actuators, increasing complexity, andincreasing de-

mands on reliability and safety, integrated supervision

with fault diagnosis becomes increasingly important.

This is, therefore, a signiﬁcant natural feature of

an intelligent mechatronic system. Figure 19.9 shows

a process inﬂuenced by faults. These faults indicate

unpermitted deviations from normal states and can

be generated either externally or internally. External

faults are, e.g., caused by the power supply, contam-

ination or collision, internal faults by wear, missing

lubrication, and actuator or sensor faults. The classic

methods for fault detection are limit-value checking

and plausibility checks of a few measurable variables.

However, incipient and intermittent faults cannot usu-

ally be detected, and in-depth fault diagnosis is not

possible with this simple approach. Therefore, model-

based fault detection and diagnosis methods have been

developed in recent years, allowing early detection

of small faults with normally measured signals, also

in closed loops [19.48–51]. Based on measured in-

put signals U(t), output signals Y(t), and process

models, features are generated by, e.g., parameter esti-

mation, state and output observers, and parity equations

(Fig.19.9).

19.5 Design Procedures for Mechatronic Systems

The design of mechatronic systems requires system-

atic development and use of modern software design

tools. As with any design, mechatronic design is also

an iterative procedure. However, it is much more in-

volved than for pure mechanical or electrical systems.

Figure 19.10 shows that, in addition to traditional

domain-speciﬁc engineering, integrated simultaneous

(concurrent) engineering is required due to the inte-

gration of engineering across traditional boundaries

that is typical of the development of mechatronic

systems.

Hence, themechatronic designrequires asimultane-

ous procedure in broad engineering areas. Traditionally,

the design of mechanics, electrics and electronics, con-

trol, and human–machine interface were performed in

different departments with only occasionally contact,

Part C 19.5

326 Part C Automation Design: Theory, Elements, and Methods

System definition

Requirements engineering

(specification)

Generation of synergetic effects

Integrated mechan. electronic system

Reliability & safety engineering

Manufacturing engineering

Mechatronic system

Electronic

engineering

Mechanical & electrical

engineering

Traditional

engineering

Integrated (concurrent)

engineering

Operating

engineering

Information & control

engineering

Electronic hardware

design

Process design

Human machine

interface design

Inform. processing &

software design

Integration of components

(hardware)

Integration by information processing

(software)

Fig. 19.10 From domain-speciﬁc traditional engineering to integrated, simultaneous engineering (iteration steps are not

indicated)

sometimes sequentially (bottom-up design). Because of

the requirements for integration of hardware and soft-

ware functions, these areas have to work together and

the products have to be developed more or less simulta-

neously to an overall optimum (concurrent engineering,

top-down design). Usually, this can only be realized

with suitable teams.

The principle procedure for the design of mecha-

tronic systems is, e.g., described in the VDI-Richtlinie

(guideline) 2206 [19.11]. A ﬂexible procedural model is

described, consisting of the following elements:

1. Cycles of problem solutions at microscale:

•

Search for solutions by analysis and synthesis of

basis steps

•

Comparison of requirements and reality

•

Performance and decisions

•

Planning

2. Macroscale cycles in the form of a V-model:

•

Logical sequence of steps

•

Requirements

•

System design

•

Domain-speciﬁc design

•

System integration

•

Veriﬁcation and validation

•

Modeling (supporting)

•

Products: laboratory model, functional model, pre-

series product

3. Process elements for repeating working steps:

•

Repeating process elements

•

System design, modeling, element design, integra-

tion, ...

The V-model, according to [19.11, 52], is distin-

guished with regard to system design and system

Part C 19.5

Mechatronic Systems – A Short Introduction 19.5 Design Procedures for Mechatronic Systems 327

Degree of maturity

Validation

Requirements

• Overall functions

• Rated values

• Costs & milestones

Specifications

• Fulfillment of requirements

• Sources, limitations

• Reliability, safety

System design

• Paritioning

• Modules

• Mechanics vs. electronics

• Synergies

Modeling & simulation

• Models of component

• Behavior analysis

• Requirements for components

• Design

Prototypes

• Laboratory solutions

• Modifications former products

• Prototype computers/algorithms

Component testing

• Hardware-in-the-loop simulation

• Stress analysis

System integration (hardware)

• Assembling

• Mutual adaption

• Optimization

• Synergies

System integration (software)

• Signal analysis

• Filtering

• Tuning of algorithms

System testing

• Test rigs

• Stress testing

, electromagnetic compatibility

• Behavior testing

• Reliability, safety

Field testing

• Final product

• Normal use

• Statistics

• Certification

Production

• Simultaneous planning

• Technologies

• Assembling

• Quality control

Mechatronic components

• Mechanics

• Electronics

• Control-software

• Human machine interface

Verification

System

design

System

integration

Component design (domain specific)

Mechanics Electronics Automatic

control

Human machine

interface

Fig. 19.11 A “V” development scheme for mechatronic systems

integration with domain-speciﬁc design in mechani-

cal engineering, electrical engineering, and information

processing. Usually, several design cycles are re-

quired, resulting, e.g., in the following intermediate

products:

•

Laboratory model: ﬁrst functions and solutions,

rough design, ﬁrst function-speciﬁc investigations

•

Functional model: further development, ﬁne-tuning,

integration of distributed components, power mea-

surements, standard interfaces

•

Pre-series product: consideration of manufacturing,

standardization, further modular integration steps,

encapsulation, ﬁeld tests.

The V-model originates most likely from software

development [19.53]. Some important design steps for

mechatronic systems areshownin Fig. 19.11 inthe form

of an extended V-model, where the following are distin-

guished: system design up to laboratory model, system

integration up to functional model, and system tests up

to pre-series product.

The maturity of the product increases as the individ-

ual steps of the V-model are followed. However, several

iterations have to be performed, which is not illustrated

in the ﬁgure.

Depending on the type of product, the degree of

mechatronic design is different. For precision mechanic

devices the integration is already well developed. In the

case of mechanical components one can use as a basis

well-proven constructions. Sensors, actuators, and elec-

tronics can be integrated by corresponding changes, as

can be seen, e.g., in adaptive shock absorbers, hydraulic

brakes, and ﬂuidic actuators. In machines and vehicles it

Part C 19.5

328 Part C Automation Design: Theory, Elements, and Methods

can be observed that the basic mechanical construction

remains constant but is complemented by mechatronic

components, as is the case for machine tools, combus-

tion engines, and automobiles.

19.6 Computer-Aided Design of Mechatronic Systems

The general goal in the design of mechatronic systems

is the use of computer-aided design tools from differ-

ent domains. A survey is given in [19.11]. The design

model given in [19.52] distinguishes the following inte-

gration levels:

•

Basic level:speciﬁc productdevelopment, computer-

aided engineering (CAE) tools

•

Process-oriented level: design packages, status, pro-

cess management, data management

•

Model-oriented level: common product model for

data exchange (STEP)

Process model

Simulation tool High-performance

real-time computer

(full pass, bypass)

Control

algorithm

pwm

2,setpoint

2,stat

eng

Real process (engine)

Real ECU + real actuator

(injection pump)

Integrated

mechatronic system

(final product)

ECU model

SiL

HiL

RCP

Fig. 19.12 Different couplings of process and electronics for a mechatronic design. SiL: software-in-the-loop; RCP:

rapid control prototyping; HiL: hardware-in-the-loop

•

System-oriented level: coupling of information

technology (IT) tools with, e.g., CORBA, DCOM,

a nd JAVA

The domain-speciﬁc design is usually designed on

general CASE tools, such as CAD/CAE for mechan-

ics, two-dimensional (2-D)and three-dimensional(3-D)

design with AutoCAD, computational ﬂuid dynamics

(CFD) tools for ﬂuidics, electronics and board-layout

(PADS), microelectronics (VHDL), and computer aided

design of control systems CADCS tools for the control

design (see, e.g., [19.52]).

Part C 19.6

Mechatronic Systems – A Short Introduction References 329

For overall modeling, object-oriented software is

especially of interest based on the use of general

model-building laws. The models are ﬁrst formulated

as noncausal objects installed in libraries. They are then

coupled with graphical support (object diagrams) by

using methods of herit and reusability.

Examples are MODELICA, MOBILE, VHDL-

AMS, 20 SIM; see, e.g., [19.54–59]. A broadly used

tool for simulation and dynamics design is MAT-

LAB/SIMULINK.

To design mechatronic systems various simulation

environment models have been developed, as shown in

the V-model (Fig. 19.11). In the case of software-in-

the-loop (SiL) simulation the process and its control is

simulated in a higher language to carry out basic inves-

tigations (Fig.19.12). This does not require real-time

simulation and is directed towards consideration of gen-

eral process behavior and control structure in an earlier

stage to avoid too many prototypes.

If ﬁrst mechatronic prototypes exist but the ﬁnal

hardware of the control is missing, the rapid-control-

prototyping (RCP) procedure can be used. In this,

a mechatronic prototype operates as a real system with

a simulated control on a test rig in order to test, e.g.,

control algorithms under real conditions. The prototyp-

ing computer is a powerful real-time computer with

higher-language programming.

Hardware-in-the-loop (HiL) simulation is used to

perform various testsin a laboratorywith ﬁnal hardware

(electronic control unit: ECU) and the ﬁnal software

together with the simulated process on a powerful com-

puter. Through HiL simulation, extreme operational

and environmental conditions can also be investigated,

along with faults and failures that cannot be realized

with a real process on a test rig or a real vehicle, be-

cause the situations would be either too dangerous or

too expensive. HiL simulation requires special electron-

ics for reconstruction of the sensor signals and usually

includes real actuators (e.g., hydraulics, pneumatics or

injection pumps). Through these simulation methods

the development of mechatronic systems can be per-

formed without synchronous development on the side

of the process, the electronics, or the software.

When designing mechatronic systems the tradi-

tional borders of various disciplines have to be crossed.

For the classical mechanical engineer this frequently

means that knowledge of electronic components, in-

formation processing, and systems theory has to be

deepened, and for the electrical/electronic engineer that

knowledge on thermodynamics, ﬂuid mechanics, and

engineering mechanics has to be enlarged. For both,

more knowledge on modern control principles, soft-

ware engineering, and information technology may be

necessary (see also [19.60]).

19.7 Conclusion and Emerging Trends

This Chapter could only give a brief overview of

mechatronic systems. As outlined, mechatronic sys-

tems cover a very broad area of engineering disciplines.

Advanced mechatronic components and systems are

realized in many products such as automobiles, com-

bustion engines, aircraft, electrical drives, actuators,

robots, and precision mechanics and micromechanics.

However, the integration aspects of mechanics and

electronics include increasingly more components and

systems in the wide areas of mechanical and electrical

engineering.

As the development towards the integration of

computer-based information processing into products

and their manufacturing comprises large areas of engi-

neering, suitable education in modern engineering and

also training is fundamental for technological progress.

This means, among others, to take multidisciplinary so-

lutions and method-oriented procedures into account.

The development of curricula for mechatronics as

a proper combination of electrical and mechanical en-

gineering and computer science during the last decade

shows this tendency.

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Part C 19

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333

Sensors and S

20. Sensors and Sensor Networks

Wootae Jeong

Sensors are essential devices in many indus-

trial applications such as factory automation,

digital appliances, aircraft/automotive applica-

tions, environmental monitoring, and system

diagnostics. The main role of those sensors is

to measure changes of physical quantities of

surroundings. In general, sensors are embed-

ded into sensory devices with a circuitry as

a part of a system. In this chapter, various

types of sensors and their working principles

are brieﬂy explained as well as their techni-

cal advancement to recent smart microsensors

is introduced. Speciﬁcally, the individual sen-

sor issue is also extended to emerging networked

sensors and their applications from recent re-

search activities. Through this chapter, readers can

also understand how multisensors or networked

sensors can be conﬁgured and how they can

collaborate with each other to provide higher per-

formance and reliability within networked sensor

systems.

20.1 Sensors................................................ 333

20.1.1 Sensing Principles ....................... 333

20.1.2 Position, Velocity,

and Acceleration Sensors.............. 335

20.1.3 Miscellaneous Sensors ................. 336

20.1.4 Micro- and Nanosensors .............. 336

20.2 Sensor Networks................................... 338

20.2.1 Sensor Network Systems............... 338

20.2.2 Multisensor Data Fusion Methods.. 339

20.2.3 Sensor Network Design

Considerations............................ 341

20.2.4 Sensor Network Architectures........ 342

20.2.5 Sensor Network Protocols ............. 343

20.2.6 Sensor Network Applications......... 345

20.3 Emerging Trends .................................. 346

20.3.1 Heterogeneous Sensors

and Applications ......................... 346

20.3.2 Security...................................... 346

20.3.3 Appropriate Quality-of-Service

(QoS) Model ................................ 346

20.3.4 Integration with Other Networks ... 346

References .................................................. 347

20.1 Sensors

A sensor is an instrument that responds to a speciﬁc

physical stimulus and produces a measurable corre-

sponding electrical signal. A sensor can be mechanical,

electrical, electromechanical, magnetic or optical. Any

devices that are directly altered in a predictable, mea-

surable way by changes in a real-world parameter

can be a sensor for that parameter. Sensors have an

important role in daily life because of the need to

gather information and process it conveniently for

speciﬁc tasks. Recent advances in microdevice technol-

ogy, microfabrication, chemical processes, and digital

signal processing have enabled the development of mi-

cro/nanosized, low-cost, and low-power sensors called

microsensors. Microsensors have been successfully ap-

plied to many practical areas, including medical and

space devices, military equipment, telecommunication,

and manufacturing applications [20.1, 2]. When com-

pared with conventional sensors, microsensors have

certain advantages, such as interfering less with the en-

vironment they measure, requiring less manufacturing

cost, being used in narrow spaces and harsh environ-

ments, etc. The successful application of microsensors

depends on sensor capability, cost, and reliability

20.1.1 Sensing Principles

Sensors can be technically classiﬁed into various

types according to their working principle, as listed

Part C 20

334 Part C Automation Design: Theory, Elements, and Methods

Table 20.1 Technical classiﬁcation of sensors according to their working principle

Sensing principle Sensors

Resistance change Strain gage, potentiometer, potentiometric throttle position sensor (TPS),

resistance temperature detector (RTD), thermistor, piezoresistive sensor,

magnetoresistive sensor, photoresistive sensor

Capacitance change Capacitive-type torque meter, capacitance level sensor

Inductance change Linear variable differential transformer (LVDT), inductive angular position sensor

(magnetic pick-up), inductive torque meter

Electromagnetic induction Electromagnetic ﬂow meter

Thermoelectric effect Thermocouple

Piezoelectric effect Piezoelectric accelerometer, sound navigation and ranging (SONAR)

Photoelectric effect Photodiode, phototransistor, photo-interrupter (optical encoder)

Hall effect Hall sensor

in Table 20.1. That is, sensors can measure physical

phenomena by capturing resistance change, capaci-

tance change, inductance change, thermoelectric effect,

piezoelectric effect, photoelectric effect, Hall effect,

andsoon[20.3,4]. Among these effects, most sensors

utilize the resistance change of a conductor, i.e., re-

sistivity. As long as the current density is uniform in

the insulator, the resistance R of a conductor of cross-

sectional area A can be computed as

R =

ρl

(20.1)

where l is the length of the conductor, A is the cross-

sectional area, and ρ is the electrical resistivity of the

material. Resistivity is a measure of the material’s abil-

ity to oppose electric current. Therefore, change of

resistance can be measured by detecting physical defor-

mation (l or A) of conductive materials or by sensing

resistivity (ρ) of conductor. As an example, a strain

gage is a sensor that measures resistance by deforma-

tion oflength or cross-sectional area, and athermometer

is a sensor that measures resistance by examining the

resistivity change of a material. By expanding (20.1)

in a Taylor series and then simplifying the equation,

resistance change can be expressed as

ΔR =Δρ

+Δl

−ΔA

ρl

. (20.2)

By dividing both side of (20.2) by the resistance R,the

resistance change rate can be expressed as

ΔR

Δρ

Δl

−

ΔA

Δρ

+ε +2νε ,

(20.3)

where ε and ν are thestrain andthe Poisson’s ratio ofthe

material, respectively. When theresistivity(ρ)ofasens-

ing material is close to constant, the resistance can be

determined from the values of strain (ε) and Poisson’s

ratio (ν) of the material (e.g., strain gage). When the

resistivity of a sensing material is sensitive to the mea-

suring targets and values of ε, ν can be neglected; the

resistance can be measured from the resistivity change

(Δρ/ρ) (e.g., resistance temperature detector (RTD)).

In capacitance-based sensors, the sensor measures

the amount of electric charge stored between two plates

of capacitors. The capacitance C can be calculated as

C =

εA

(20.4)

where A is the area of each plate, d is the separa-

tion between the plates, and ε is the dielectric constant

(or permittivity) of the material between the plates.

The dielectric constant for a number of very useful di-

electrics changes as a function of the applied electrical

ﬁeld. Thus, capacitance-based sensors utilize capac-

itance change by measuring the dielectric constant,

the area (A) of, or the separation (d) between the

plates. A capacitive-type torque meter is an example of

a capacitance-based sensor.

Inductance-based sensors measure the ratio of the

magnetic ﬂux to the current. Linear variable differential

transformers (LVDT;Fig.20.1) and magnetic pick-up

sensors are representative inductance-based sensors.

Electromagnetic induction-based sensors are based

on Faraday’s law of induction, which is involved in the

operation of transformers, inductors, and many forms

of electrical generators. The law states that the induced

Part C 20.1