The Intelligent Multivariate

Measurement System

Yusuf Hendrawan, STP., M.App.Life Sc., Ph.D

Konsep Alat Ukur Konsep Alat Ukur:

(1) Daya guna unsur-unsur fungsional sistem alat ukur (2) Karakteristik statis dan dinamis

Unsur-unsur Fungsional:

(1) Unsur penginderaan primer unsur pertama yang menerima energi dari medium yang diukur.

(2) Unsur pengkonversi peubah (variabel) menukar keluaran dari unsur pengindera primer dengan peubah yang lebih cocok.

(3) Unsur pengubah peubah (manipulator) perubahan-perubahan nilai numerik sesuai aturan tertentu.

(4) Unsur pengiriman data

(5) Unsur penyaji data dalam bentuk yang dapat ditanggapi oleh indera manusia

Medium

yang

diukur

Pengindera

pertama

Pekonversi

peubah

Pengubah

Peubah Pengirim data Penyaji data Pengamat

Jumlah yang diukur

data

sajian

Contoh Unsur-unsur fungsional pada Alat Pengukur Tekanan

Piston merupakan

pengindera primer

Batang Piston

merupakan unsur

pengirim data

Juga merupakan pengkonversi peubah,

karena mengubah tekanan cairan (gaya per

satuan luas) menjadi gaya resultan pada

permukaan piston

Juga merupakan pengkonversi peubah,

karena mengkonversi gaya menjadi

perpindahan yang proporsional

Batang Penghubung

merupakan unsur

pengubah peubah

Batang penghubung memperbesar perpindahan dr

batang piston untuk memperoleh perpindahan yang

lebih besar pada jarum penunjuk

Jarum penunjuk dan

skala merupakan unsur

penyaji data

Alat ukur pada suatu jarak tertentu dari

sumber tekanan, unsur pengirim data berupa

pipa kecil, wireless connection, dll.

pressure

Karakter Statis

Karakter statis: karakteristik yang harus diperhatikan apabila alat ukur tersebut

digunakan untuk mengukur suatu kondisi yang tidak berubah karena waktu atau

hanya berubah secara lambat laun.

(1) Kalibrasi: satu keadaan dimana semua masukan kemudian diubah-ubah

sepanjang rentang nilai konstanta yang sama, yang menyebabkan nilai keluaran

berubah sepanjang rentang nilai konstanta teretentu.

(2) Ketelitian: kecocokan pembacaan / jika nilai yang sama dari peubah yang

terukur, diukur beberapa kali dan memberikan hasil yang kurang-lebih sama,

maka alat ukur tersebut dikatakan mempunyai ketelitian yang tinggi, dan juga alat

ukur tidak mempunyai penyimpangan.

(3) Ketepatan: tingkat perbedaan yang sekecil-kecilnya antara nilai pengamatan

dengan nilai sebenarnya. perlu kalibrasi yang rutin (4) Jangkauan: perbandingan pembacaan meter maksimum ke pembacaan meter

minimum.

(5) Kesalahan pengukuran: tingkat kegagalan dalam menspesifikasi besaran yang

diukur secara pasti / variasi kuantitas nilai yang dinyatakan dari nilai sebenarnya.

Karakter Statis

Sumber Kesalahan Pengukuran:

(1) Derau (noise)

(2) Waktu tanggap (response time)

(3) Keterbatasan Rancangan (design limitation)

(4) Pertambahan atau kehilangan energi karena interaksi

(5) Transmisi

(6) Keausan / kerusakan sistem pengukuran

(7) Pengaruh ruangan terhadap sistem

(8) Kesalahan penafsiran oleh pengamat

Karaketeristik Dinamis

Karakteristik Dinamis merupakan pengembangan suatu model matematika

yang berlaku umum yang mencakup hal-hal penting berkenaan dengan

karakteristik hubungan dinamis antara masukan dan keluaran.

Measured Variables

The purpose of measurement system is to link the observer to the

process.

Information variables is the measured variable the input of the measurement system is the true value of the variable the output is the measured value of the variable

Ideal measurement system: the measured value = the true value

Information

Variables

Observer:

(1) Car driver

(2) The plant operator

(3) nurse

(4) etc

Accuracy The accuracy of the system: the closeness of the measured value to the

true value measurement system error (E)

E = measured value true value

E = system output system input

E.g. If the measured value of the flow rate of gas in a pipe is 11.0 m3/h and

the true value is 11.2 m3/h, then the error E = -0.2 m3/h.

E.g. If the measured value of the rotational speed of an engine is 3140 rpm

and the true value is 3133 rpm, then E = +7 rpm.

Error is the main performance indicator for a measurement

system.

Structure of Measurement Systems

Four type of element of measurement systems:

(1) Sensing element

This is in contact with the process and gives an output which is depends in some way on the variable to be measured

- Thermocouple where millivolt depends on temperature

- Strain gauge where resistance depends on mechanical strain

- Orifice plate where pressure drop depends on flow rate

(2) Signal Conditioning Element

This takes the output of the sensing element and converts it into a form more suitable for further processing i.e. voltage, current, frequency signal, etc

(3) Signal Processing Element

This takes the output of the conditioning element and converts it into a form more suitable for presentation e.g. ADC which convert a voltage into a digital form for input to a computer;

computer which calculates the measured value of the variable from the incoming digital data.

(4) Data Presentation Element

The measured value in a form which can be easily recognized by the observer e.g. visual display unit (VDU)

Example of Measurement Systems

The word Transducer is commonly used in connection with measurement and

instrumentation. This is a manufactured package which gives an output voltage (usually)

corresponding to an input variable such as pressure or acceleration

Transducer

The Intelligent Multivariate

Measurement System

Discusses the principles and implementation of intelligent

multivariable measurement system, which have the ability to

estimate the measured values of a number of process variable.

sensor sensor sensor sensor sensor . . . .

Output

Measured

variable

Measured

variable

Measured

variable

Measured

variable

Measured

variable

Other

Process

variables

The system should also have the

ability to calculate estimates of the

values of process variables which

are not measured from estimates

of variables which are measured

Process Models

Virtual

Instrument Process

variables

Process Models

The Structure of an Intelligent

Multivariable System Example:

-A chemical Plant

-Power Station

-Steel mill

-Oil Refinery

-Car

-Plant Factory

-Ship or Aircraft

Measured Variable

(n1)

Measured Variable

(n2)

Measured Variable

(n3)

Measured Variable

(n4)

Measured Variable

(n)

.

.

.

Process

variables

(P)

Unmeasured

Variables

Process Variables for a gas pipeline

{P} = {volume flow rate; temperature; pressure; density; mass flow rate; enthalpy flow rate}

n1 n2 n3 n4 n5 n6

True values of

process variables

*it could be impossible, impractical, uneconomic to

measure all n process variables

{I} = {volume flow rate; temperature; pressure}

m1 m2 m3 Measured

variables

(m n)

n4 n5 n6

*The system should have the ability to calculate

the n-m unmeasured variables from m measured

variables

Inverse Sensor Models

I1 depends on U1 and I3

I2 depends on U2 and 11

I3 depends on U3 and I2

m inverse sensor

equations

I = KU + N(U) + a + KMIMU + KIII

IM = modifying input II = interfering input

modifying input

Interfering input

modifying input Interfering input

Process Models

g: {I} {P}

m measured

variables

{P} measured

values of the n

process variables

Pi = Ii for i = 1, m

Pi = gi {Ii} for i = m+1, 2, n

{P} = {volume flow rate Q; temperature ; pressure P; density ; mass flow rate M; enthalpy flow rate H}

{I} = {Q; ; P} The three non-measured process variables , M, H can be calculated from Q; ; P using:

*R is the gas constant; Cp is constant pressure

Process Models

g: {I} {P}

Data

Presentation

Display Observer

Modeling methods for multivariable systems

{x} {y} p input

variables

r output

variables

Function f ???

f: {x} {y}

Physical and Chemical Equations used in Sensor Models

Some physical and chemical equations which can be used to model sensors.

Physical and Chemical Equations used in Process Models

Some physical and chemical equations which can be used to model processes.

These equations can be used to calculate process variables which are not

measured from variables which are measured.

Physical and Chemical Equations used in Process Models

Calculate the volume flow rate Q of fluid flowing through a pipe.

From the equation: divide AT into 12 area elements

Ai, i=1, 2, 12

12

1i

iiAvQ

Vortex

Flowmeter d

Svf

ii fS

dv

*S = Strouhal number

*d = the width of the bluff body

12

1i

iiAfS

dQ

Q can be found from the measured fi if the constants

Ai, S, and d are known.

Multivariable Regression Equations There are many situations where an equation with a well-defined form

does not exist to describe a given physical or chemical effect.

E.g. The non-linear relation between ET ( thermoelectric) and T

(temperature) has to be described by a polynomial or power series of

the form:

The coefficients a1, a2, a3, etc., are found from experimental data values

of ET ( thermoelectric) and T (temperature) using regression analysis.

Artificial Neural Network (ANNs) (1) In complex multivariable processes and sensors arrays, well-defined

physical and chemical equations may not exist to provide a

sufficiently accurate model of the system

(2) It may be impossible to predict the form of a suitable multivariable

regression equation (in case: a large number of variables are required

to represent the system accurately)

(3) Artificial Neural Networks (ANNs) can be used as a modeling

technique

What is ANNs?? Empirical models which approximate the

behavior of neurons in the human brain

Artificial Neural Network (ANNs) ANNs consists of three layers:

(1) Input layer; (2) hidden layer; (3) Output layer.

Gas turbine sensor validation through classification with artificial

neural networks Applied Energy

Volume 88, Issue 11, November 2011, Pages 38983904

The proposed method is based on training artificial neural

networks as classifiers to recognize sensor drifts. The

method is evaluated on two types of gas turbines, i.e.,

one single-shaft and one twin-shaft machine. The results

show the method is capable of early detection of sensor

drifts for both types of machines as well as accurate

production of soft measurements. The findings suggest that

the use of artificial neural networks for sensor validation

could contribute to more cost-effective maintenance

as well as to increased availability and reliability of power

plants.

Comparative analysis of artificial neural networks and dynamic

models as virtual sensors Applied Soft Computing

Volume 13, Issue 1, January 2013, Pages 181188

Three emissions predictive models were investigated in this study; direct and series artificial neural

network (ANN) models and a MATLAB dynamic model. The direct models takes variables lambda, throttle

position, engine and vehicle speed to predict the engine power and the emissions CO, CO2 and HC. The

series model first takes the mentioned input to predict the engine power and consequently using the

engine power as the fifth input to predict the emissions. For the ANN models, two multilayered networks

were compared and analyzed; the backpropagation (BP) and optimization layer-by-layer (OLL) algorithms.

Monitoring and classifying animal behavior using ZigBee-based mobile

ad hoc wireless sensor networks and artificial neural networks Computers and Electronics in Agriculture

Volume 82, March 2012, Pages 4454

We designed a high performance MANET-based animal behavior monitoring system. We classify the behavior of a flock into five classes (i.e. grazing, lying down, walking, standing and others) Behavior classification was carried out using an MLP-based neural networks. The performance of the neural network was better than any other method.

Classification of sugar beet and volunteer potato reflection spectra with a neural

network and statistical discriminant analysis to select discriminative wavelengths Computers and Electronics in Agriculture

Volume 73, Issue 2, August 2010, Pages 146153

10 optimal wavebands were selected for each of the 11 available datasets individually. Second, by calculating the discriminative power of each selected waveband, 10 fixed wavebands were selected for all 11 datasets

analyses. Third, 3 fixed wavebands were determined for all 11 datasets. These three wavebands were chosen

because these had been selected by both DA and NN and were for sensor 1: 450, 765, and 855 nm and for

sensor 2: 900, 1440, and 1530 nm.

Predictions of apple bruise volume using artificial neural network Computers and Electronics in Agriculture

Volume 82, March 2012, Pages 7586

Bruise damage is a major cause of fruit quality loss. Bruises occur under dynamic and static loading when

stress induced in the fruit exceeds the failure stress of the fruit tissue. In this article the potential of an

artificial neural network (ANN) technique has evaluated as an alternative method for the prediction of

apple bruise volume.

General view of the pendulum device for measuring impact

force and impact velocity of the apple fruit

General view of the radius meter and (b) schematic representation

of geometry to calculate the radius of the apple fruit

Neural modeling of relative air humidity Computers and Electronics in Agriculture

Volume 60, Issue 1, January 2008, Pages 17

The objective of the present study was to use artificial neural networks for the estimation and prediction

of relative air humidity. The forecasting horizon was one time interval (3 h). The forecast was extended to

48 h (16 measurements) by re-introducing a newly estimated value as an input.

Topology of a multilayer perceptron (1:10-3-1-1:1)

for predicting relative air humidity

Forecasting maturity of green peas: an application of neural networks Computers and Electronics in Agriculture

Volume 70, Pages 151-156

Maturity index (MI) is a key determinant of pea softness and ultimately retail value. Pea seed

development goes through the optimal market stage for human consumption about a week before harvest.

We developed an Artificial Neural Network (ANN) model that complements field sampling by forecasting

the MI trend several days ahead. It was built using historical harvest information along with weather and

climate forecasts. We implement and evaluate the ANN in a large pea growing region in Tasmania, Australia,

and this paper highlights key results.

Residual soil nitrate prediction from imagery and non-imagery information using

neural network technique Biosystem Engineering

Volume 110, Pages 20-28

Textural features extracted from LANDSAT satellite image and non-imagery information like soil

electrical conductivity, crop yield, topography, and crop dry residue matter etc., were used to develop

residual soil nitrate prediction models using three neural networks; back propagation, modular, and radial basis

function architectures.

Modelling of jute production using artificial neural networks Biosystem Engineering

Volume 105, Pages 350-356

Neural network models, trained by back propagation, were developed to predict the development of jute

using previously obtained experimental field data. The six input variables were represented by six

neurons; Julian day, solar radiation, maximum temperature, minimum temperature, rainfall, and type of

biomass. The output variable, represented by a single neuron, was plant dry matter.

Julian Day

Solar Radiation

Max. Temperature

Min. Temperature

Rainfall

Type of Biomass

Plant dry matter

Prediction of moisture content in pre-osmosed and ultrasounded dried

banana using genetic algorithm and neural network Food and Bioproducts Processing

Volume 89, Pages 362-366

In this study, application of a versatile approach for estimation moisture content of dried banana using

neural network and genetic algorithm has been presented. The banana samples were dehydrated using two

non-thermal processes namely osmotic and ultrasound pretreatments, at different solution concentrations

and dehydration times and were then subjected to air drying at 60 and 80 C for 4, 5 and 6h.

Modeling net ecosystem metabolism with an artificial neural network

and Bayesian belief network Environmental Modelling & Software

Volume 26, Pages 1199-1210

Artificial neural networks (ANNs) and Bayesian belief networks (BBNs) utilizing select environmental

variables were developed and evaluated, with the intent to model net ecosystem metabolism (a proxy for

system trophic state) within a freshwater wetland. Net ecosystem metabolism (NEM; analogous to net ecosystem

production) represents the balance between system-level production and respiration. ANNs delivered the greatest

predictive accuracy for NEM and did not require expert knowledge about system variables for their development.

Multi-sensor integration for on-line

tool wear estimation through

artificial neural networks and fuzzy

neural network Engineering Applications of Artificial Intelligence

Volume 13, Issue 3, 1 June 2000, Pages 249261

Welcome to the

Community of

Intelligent Society