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P. Subbaraj et. al. / International Journal of Engineering Science and Technology

Vol. 2(9), 2010, 4095-4107

FUZZY BASED FAULT DETECTION AND

DIAGNOSIS IN PNEUMATIC ACTUATOR

IN CEMENT INDUSTRYP. SUBBARAJ

Principal,Theni Kamavar Sangam College of Engineering and Technology,Theni, Tamil Nadu, India.

B. KANNAPIRAN*

*Department of Instrumentation & Control Engineering, Arulmigu Kalasalingam college of Engineering,Anand Nagar, Krishnankoil-626190 Srivilliputhur, Virudunagar District, Tamilnadu, India

Abstract :

Fault detection and diagnosis is an important task with increasing attention in the academic and industrial fields, due

to economical and safety related matters. The early detection of fault can help avoid system shutdown, breakdown

and even catastrophe involving human fatalities and material damage. In fault detection, the discrepancies betweensystem outputs and model outputs are called residuals, and are used to detect and diagnose faults. Computational

intelligence techniques are being investigated as an extension to the traditional fault detection and diagnosis

methods. This paper proposes a fuzzy based architecture for fault detection and diagnosis based on fuzzyclassification approach. The real time data for pneumatic actuator has obtained from the cement industry under

normal and abnormal operating conditions. In this paper the proposed fuzzy architecture is able to detect the thirteen

numbers of possible faults in pneumatic actuator for cooler water spray system in cement industry, effectively when

compared with Hazard and Operability (HAZOP) study.

Keywords: Fault detection, Fuzzy Classification approach, HAZOP, Pneumatic Actuator.

1. IntroductionThe development of model-based fault diagnosis began in the early 1970s. This method of fault detection in

dynamic systems has been receiving more and more attention over the last two decades. Generally fault is to be

understood as an unexpected change of the system functionality. It may not, however, represent the failure of

physical components. Such malfunctions may occur either in the sensors or actuators, or in the components of the

process itself. However, the same difference signal can correspond to model-plant mismatches or noise in real

measurements, which are erroneously detected as a fault. The availability of a good model of the monitored system

can significantly improve the performance of diagnostic tools, minimizing the probability of false alarms. In all but

the most trivial cases the existence of a fault may lead to situations related to safety, health, environmental, financial

or legal implications.

Condition monitoring and fault diagnosis are important aspects for the safe and reliable operation for the process as

well as the operators. The process operation and process operators in the cement industry, given enough time, mightbe able to analyze the large volume of data collected from many critical systems for features indicative of faults or

other safety related features. They use the conventional methods like manually processing the data with the help of

the vast amount of information available in the form of various documents about the various malfunctions that can

occur. This is not suitable when the plant is under operation or under emergency. Also, it is impractical to expect the

operator to perform this sort of monitoring and detection continuously. Under this condition, it may be of great use if

an automated system is developed for real time monitoring of the plant.

Due to the broad scope of the process fault detection problem and the difficulties in its real time solution, many

analytical based techniques [1-3] have been proposed during the past several years for fault detection of technical

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plants. The important aspect of this approach is the development of a model that describes the cause and effect

relationships between the system variables using state estimation or parameter estimation techniques. The problem

with these mathematical model based techniques is that under real conditions, no accurate models of the system of

interest can be obtained. In that case, the better strategy is of using knowledge based techniques where the

knowledge is derived in terms of facts and rules from the description of system structure and behavior. Classical

expert systems [4] were used for this purpose. The major disadvantage of inference method is that it has always been

that binary logical decisions with Boolean operators do not reflect the gradual nature of many real world problems.

Recently Artificial Neural networks [5] and Fuzzy logic [6] were proposed for fault detection problems. The

advantage of neural network approach is their generalization capability which lets them deal with partial or noisy

inputs. The neural networks are able to handle continuous input data and the learning must be supervised, in order to

solve the fault detection and diagnosis problem. The multilayer perceptron network is the most common network

today. Due to their powerful nonlinear function approximation and adaptive learning capabilities, neural networks

have drawn great attention in the field of fault diagnosis. But the neural network approach needs lot of data to

develop the network before being put to use for real time applications. The driving force behind a fuzzy logic system

is the idea that some uncertainty exists in categorizing the values of the system variables. This uncertainty present in

the decision making process can be incorporated into the diagnosis system via fuzzy set theory. A nonlinear fuzzy

model [7] with transparent inner structure is used for the generation of six different symptoms in electro- pneumatic

valve. The resulting symptom patterns are classified with a new self-learning classification structure based on fuzzy

rules. The strategy developed [8] can lead to the effective and proactive monitoring of degradation and diagnosis of

fault. But the reason behind the major degradation of the valve can be distinguished from other reasons such as a

badly tuned controller and external disturbances. The types and severity of degradation is identified and estimated.

Proactive maintenance is effectively implemented.

This paper focuses the fault detection and diagnosis of a pneumatic actuator in critical system like cooler water

spray system in cement industry. The fault detection and diagnosis is proposed on pneumatic actuator to avoid

hazardous operating condition in cooler water spray system. When actuator fails it will affect the spray process

system in the hot gas duct and it will also damage the ESP.

This paper is organized as follows: Section 2 deals with conventional method as HAZOP analysis, Section 3 deals

with system description in cement industry, section 4 explains physical structure of pneumatic valve, section 5

describes Fuzzy logic based fault detection and diagnosis, section 6 discusses results obtained from real time data,

then it is compared with HAZOP technique and section 7 concludes.

2. HAZOP Analysis

HAZOP analysis was developed in the late 1960s at ICI in the UK. The basic principle of HAZOP analysis [9] is

that hazards arise in a plant due to deviations from normal behavior. A group of experts systematically identify

every conceivable deviation from design intent in a plant; find all the possible abnormal causes, and the adverse

hazardous consequences of that deviation. The experts in the study team are chosen to provide the knowledge and

experience in different disciplines for all aspects of the study to be covered comprehensively.

The procedure involves examining the process flow diagram systematically, line by line or section by section

(depending on the level of detail required), by generating deviations of the process variables from their normal state.

The possible causes and consequences of each deviation so generated are then considered, and potential problems

are identified. In order to cover all possible malfunctions in the plant, the process deviations to be considered aregenerated systematically by applying a set of guide words, namely, NONE, MORE OF, LESS OF, PART OF,

REVERSE, AS WELL AS and OTHER THAN, which correspond to qualitative deviations of process variables.

2.1 Preparation for carrying out HAZOP

The amount of preparation required for a HAZOP [9] depends upon the size and complexity of the plant. Typically,

the data required consist of various drawings in the form of line diagrams, flow sheets, plant layouts, isometrics and

fabrication drawings, operating instructions, instrument sequence control charts, and logic diagrams. Occasionally

there are plant manuals and equipment manufacturers manuals. The data must be accurate and sufficiently

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comprehensive. In particular for this plant, line diagrams must be checked to ensure they are up to date and that

modifications have not been made since the plant was constructed.

2.2 Composition of the Team to Carryout a HAZOP

HAZOP are normally carried out by a multi-disciplinary team, including chemical engineers and chemists, with

members being chosen for their individual knowledge and experience in design, operation, maintenance or health

and safety. The technique allows experts in the process to bring their knowledge and expertise to bear

systematically so that problems are less likely to be missed. It is useful to record each step of a HAZOP for all the

physically meaningful deviations or, if a subset is used, to include these requiring an action plus those which

considered significant but required no action because the existing protection was deemed adequate.

3. System Description in Cement Industry

Clinker coolers, like virtually all of the process equipment in a cement plant; have undergone significant

transformations over this century. Beginning with passive, open-air clinker cooling, its development progressed

through rotary and planetary coolers, to traveling grate and finally reciprocating grate coolers largely in use today.

Obviously, the goals of the clinker cooler are to maximize the heat recovery to the kiln process, minimize the

ultimate clinker temperature and required cooling air volume, and maintain high service availability.

Red-hot clinker tumbles from the kiln onto a grate and is cooled by cooler fans. The hot air recovered from this

cooling process is recycled back into the kiln or preheated system to recover its thermal energy. The next section

deals with Water cooling system in cement industry.

3.1 Water Cooling System

Direct measurement of clinker temperature is not possible on a continuous basis. Hence the measurement of cooler

vents gas temperature, which is a function of clinker temperature. When the temperature of the clinker at the outlet

of kiln is up to 1100 C, then the clinker temperature is reduced up to 750 C at the grate cooler section by using

external blower fans. It is necessary to reduce the hot gas temperature up to 250 C before given to the

ESP(Electrostatic Precipitator) section, if it exceeds beyond this 250 C, then the ESP gets damaged. In order to

prevent this damage, water spray system is used in cement industry. Water is injected through the pneumatic valve

and then it is spray using flow nozzle at the cooler outlet.

When the hot gas temperature crosses 225 C, the first stage of water spray will be in operation comprising of two

number of spray nozzles. The temperature of the exit gas from the cooler is regularly measured by the temperature

element provided in the corresponding duct. When the temperature shoots up beyond 300 C the second stage of

spray through the nozzle is engaged. Whenever the water pump will be in operation, the pressure gauge G senses the pressure at the pump discharge and it flow through the pneumatic valve and finally it reaches the flow nozzle.

During this operation, the initial flow through the pneumatic valve and the rod displacement of the pneumatic valve

will be maintained between 2mm3/ sec to 14mm3/ sec and 8mm to 80mm respectively. The schematic layout of the

cooler water spray system setup is shown in figure 1. The parameters of cooler water spray system are listed in

Table 1. The various safety instruments used in the cooler spray system are Butter fly valve, reducer, filter, orifice

plate, shut off valve, flow transmitter, Block and bleed valve, Globe valve, Ball valve, Non-return valve, Pressure

gauge, Temperature element/ Transmitter, etc.,

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Flow ofHotGas to

S

V

V2

V3

V4

V5

V6

V7

P

CAL

AFR

F

R1

R2

R3

FZ1

FZ2

FZ 3

P1

P2

G

Fig 1. Schematic layout of the Cooler water sprays system.

The water from the storage tank is pumped through various safety instruments and then it reaches the flow nozzle

through the pneumatic valve. The major problem and risks involved is that the problem in Pneumatic valve, which

mainly occurs due to the variation in the flow rate. Further this will affect the spray process in the flow of hot gas

duct and there is no cooling process happens in the hot gas flow line. This leads to the rise in temperature of the hot

gas and it will damage the electrode plates in ESP because of its high cost. This type of valve is automatic

equipment designed to regulate the flow rate in a pipe system

The next section presents the physical structure of the pneumatic valve in cement industry.

Table 1 Parameters of Cooler water sprays system

ST Storage Tank

V1 Return Valve

V2,V3,V4 Delivery Valve for water flow

V5,V6,V7 Delivery Valve for air flow

AFR Air Filter Regulator

CAL Compressed Air line from Plant

P1-P2 Pump

R1-R3 Flow Reducer

PV Pneumatic Valve

FT Flow Transmitter

FZ1-FZ3 Flow Nozzle Banks(1-3)G Pressure gauge

4. Physical Structure of Pneumatic Valve

Fault detection and diagnosis are important tasks in pneumatic valve in cement industry. It deals with the timely

detection, diagnosis and correction of abnormal condition of faults in the plant. Early detection and diagnosis of

plants while the plant is still operating in a controllable region can help avoid abnormal event progression and

reduce productivity loss. Building a model for fault diagnosis involves embedding the heuristic knowledge by

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PSP

Pneumatic

Servomotor

T1

TT

P

Ps

CV

ControllerPositioner

ZT

FT

X

P

V2

PT

F

V3

Valve

V1

E/P

CVI

From the Water

reservoir

To flow nozzle for water spray

in the hot gas duct

experience and observations over a period of time. This knowledge has inherent fuzziness because it comes from

uncertain and imprecise nature of expressing the abstract thoughts. Fuzzy logic can afford the computers, the

capability of manipulating abstract concepts commonly used by the humans in decision-making. A pneumatic servo-

actuated industrial control valve, which is used as test bed of the fault detection approach proposed in this paper.

4.1Pneumatic actuator

The internal structure of the Pneumatic valve is shown in figure 2.The flow is set by the position of the rod, which

determines the restricted flow area. The actuator sets the position of this rod. There are many types of servo-

actuators: electrical motors, hydraulic cylinders, spring-and-diaphragm pneumatic servomotor, etc.

The most common type of actuator is the spring-and-diaphragm pneumatic servomotor due to its low cost. This

actuator consists of a rod that has, at one end, the valve plug and, at the other end, the plate. The plate is placed

inside an airtight chamber and connects to the walls of this chamber by means of a flexible diaphragm.

The descriptions of the main parameters of the servo-actuated valve are given in Table 2.

Fig 2. Internal Structure of the Pneumatic valve

Table 2.Servo-actuated pneumatic valve parameters

PSP Positioner of supply air pressure

PT Air pressure transmitter

FT Volume flow rate transmitter

TT Temperature transmitter

ZT Rod position transmitter

E/P Electro-pneumatic converter

V1, V2 Cut-off valves

V3 Bypass valve

Ps Pneumatic servomotor chamber pressure

CVI Controller output

CV Control reference value

F Volumetric flow

x Servomotor rod displacement

The flow through the valve is given by

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F=100Kvf(x)

where Kv is the flow coefficient (m3/h) (given by the manufacturer), f (x) is the valve opening function, P is the

pressure difference across the valve (MPa), is the fluid density (kg/ m3), F is the volumetric flow through the valve

(m3/h), and x is the position of the rod (m), which is the same of the plug. The valve opening function f (x) indicates

the normalized valve opening area. It varies in the interval [0, 1], where the value 0 indicates that the valve is fully

closed and the value 1 indicates that it is fully open. The value of X is defined as the percentage of valve opening.

4.2 Valve body

The valve body is the component that determines the flow through the valve. A change of the restricted area in the

valve regulates the flow. There are many types of valve bodies. The differences between them relate to the form by

which the restricted flow area changes. This paper addresses the globe valve case. However, the results expressed

here can easily be applied to other types of valve bodies. Modeling the flow through the valve body is not an easy

task, since most of the underline physical phenomena are not fully understood.

4.3 Positioner

The positioner determines the flow of air into the chamber. The positioner is the control element that performs the

position control of the rod. It receives a control reference signal (set point) from a computer controlling the process,

to get ride of noise and abrupt changes of the reference signal, prior to the PID control action that leads the rods

position to that reference signal. The positioner comprises a position sensor and an electrical-pneumatic transducer.

The first determines the actual position of the rod, so that the error between the actual and the desired position

(reference signal) can be obtained. The E/P transducer receives a signal from the PID controller transforming it in a

pneumatic valve opening signal that adds or removes air from the pneumatic chamber. This transducer is also

connected to a pneumatic circuit and to the atmosphere. If the controller indicates that the rod should be lowered, the

chamber is connected to the pneumatic circuit. If, on the other hand, the rod should be raised, the connection is

established with the atmosphere, thus allowing the chamber to be emptied. Next section deals with classification of

faults in pneumatic actuator in cement industry.

4.4 Classification of faults in Pneumatic actuator

This section presents the details of various faults in pneumatic actuator in cement industry.

1.Control valve faults2.Pneumatic servo-motor faults3.Positioner faults4.General faults/external faultsThe various faults which are considered as the four sets of critical nineteen numbers of faults along with theirsymbols are given in Table 3.

4.5 Effect of faults

This section deals with the problem arises due to the effect of fault has been occurred. It is necessary to give more

importance for the system whenever the fault occurs. Actuator vent blockage fault is due to the changes the system

dynamics by increasing the effective damping of the system. When air is supplied to the lower chamber of the

actuator, the pressure increases allowing the diaphragm to move upwards against the spring force. As the diaphragm

moves upward, air that is trapped in the upper chamber escapes through the vent. When the vent becomes partially

blocked due to debris, the pressure in the upper chamber increases creating a pressure surge that opposes the motion

of the diaphragm.

Similarly, when air is purged from the lower chamber, and the vent is partially blocked, a partial vacuum is created

in the upper chamber. Again, the motion of the diaphragm is hindered and the performance of the system isimpaired. In cases when the vent is entirely blocked, the valve cannot be stroked through its full range. Placing an

adjustable needle valve in the vent port, the full-open position of the needle valve was designated as 0% blockage

and the full-closed position was designated as 100% blockage.

Finally, the condition of the diaphragm should be monitored due to the cyclic nature of the stresses induced upon the

diaphragm as it flexes. As a result, fatigue failure of the diaphragm will inevitably occur.

Diaphragm leakage fault is an indicator of the condition of the diaphragm. Then it was simulated by diverting air

around the diaphragm by means of a flexible hose connecting the output of the PID to the upper chamber of the

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actuator. The leakage flow was controlled by a needle valve with 100% leakage (total diaphragm failure) denoting

the adjustment where the valve ceased to respond to any input signal. Valve clogging fault is due to cause appeared

to be a property of the sewage. But on the other hand, there are also plants in areas with hard water that are free from

clogging.

Leakage fault is due to pressure drop. This leakage fault is caused by the contaminants in the water system will

cause increased leakage and equipment malfunctions. These particles can also block orifices thus jamming valve

spools. Further water passes may be restricted resulting in reduced water flow and increased pressure drop at the

inlet side of pneumatic actuator.

Incorrect supply pressure fault is the fact that the supply pressure directly influences the volume of air that can be

delivered to the actuator. This adversely affects the position response of the valve. The incorrect supply pressure

fault can occur from a blockage or leak in the supply line, or by increased demand placed on the plant air supply.

The next sections deals with Fuzzy logic based fault detection and diagnosis in pneumatic actuator in cement

industry.

5. Fuzzy Logic Based Fault Detection and Diagnosis

A system that includes the capability of detecting and diagnosing faults is called the fault diagnosis system. Such a

system has to perform two tasks, namely fault detection and fault isolation. The purpose of the former is to

determine that a fault has occurred in the system. The latter has the purpose of locating the fault. In order to

accomplish these tasks, information that reflects a change in the normal behavior of the process has to be obtained.

This is generally called symptom evaluation or fault classification. Any method of fault diagnosis must characterize

how abnormal symptoms (measurements) are related to faults (malfunctions).

Table3. List of various types of faults

Types of fault Name of the fault Symbols Name of the fault Symbols

Control valve

faults

valve clogging F1 external leakage (bushing, covers,

terminals)

F5

valve or valve seat erosion F2 internal leakage (valve tightness) F6

valve or valve seat sedimentation F3 medium evaporation or critical flow F7

increased of valve or bushing friction F4 - -

Pneumatic servo-motor faults

twisted servo-motor's piston rod F8 servo-motor's diaphragm perforation F10servo-motor's housing or terminals tightness F9 servo-motor's spring fault F11

Positioner faultselectro-pneumatic transducer fault (E/P) F12 pressure sensor fault (PT) F14

rod displacement sensor fault (DT) F13

General

faults/external

faults

Positioner supply pressure drop F15 fully or partly opened bypass valves F18

increase of pressure on valve inlet or

pressure drop on valve output

F16 flow rate sensor fault (FT) F19

pressure drop on valve at inlet or increase of

pressure on valve output

F17 - -

Often formulating a diagnostic system is the lack of such a model of fault-symptom connections, due to the lack of

understanding of fault induction and propagation mechanisms in the device. The following are the set of desirablecharacteristics one would like the diagnostics system to possess: a) Quick detection and diagnosis b) Isolability c)

Robustness d) Novelty identifiability e) Classification error estimate f) Adaptability g) Explanation facility h)

Modelling requirements i) Storage and computational requirements j) Multiple fault identifiability [10, 11].Next

section presents a brief overview of fuzzy logic theory and different steps to design a fuzzy (linguistic) model.

5.1 Review of Fuzzy Logic

Fuzzy logic uses fuzzy set theory, in which a variable is a member of one or more sets, with a specified degree of

membership. Fuzzy logic when applied to computers, allows them to emulate the human reasoning process, quantify

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imprecise information, make decisions based on the vague and incomplete data, yet by applying a Defuzzification

process, arrive at definite conclusions.

Two common sources of information for building fuzzy models are the priori knowledge and data. The priori

knowledge can be of a rather approximate nature (qualitative knowledge, heuristics), which usually originates from

experts. Data are available as records of the process operation or special identification experiments can be conducted

to obtain the relevant data.

To design a (linguistic) fuzzy model based on available expert knowledge, the following steps [11, 12] can be

followed:

Select the input and output variables, the structure of the rules, and the inference and defuzzification methods. Decide on the number of linguistic terms for each variable and define the corresponding membership functions. Formulate the available knowledge in terms of fuzzy if-then rules. Validate the model (typically by using data)If the model does not meet the expected performance, iterate on the above design steps. When using fuzzy logic in a

detection environment, the following successive steps are involved, fuzzification of crisp values; inference using a

rule base in which the logical operations are performed on the membership functions; and defuzzification to obtain

crisp outputs. The next sections present the fuzzy logic based approach for fault diagnosis in pneumatic actuator in

cement industry.

Fig 3. Membership Functions for input variables in Pneumatic valve

5.2 Development of Fuzzy Diagnostic System

Fault diagnosis is a classical area for fuzzy logic applications. Compared to algorithmic approaches, the advantage

of fuzzy logic-based approach is that it gives possibilities to follow humans way of fault diagnosing and to handle

different information and knowledge in a more efficient way. This section presents the details of the fuzzy logic

based diagnostic system developed for four sets of critical nineteen number of faults in pneumatic actuator like

Control valve faults, Pneumatic servo-motor faults, Positioner faults, General faults/external faults.

The information required for the development of the fuzzy system was collected from the field experts. The

collected information includes the fault- symptom relationship for the above four critical faults and the ranges of the

variables. The objective here is to capture the implicit knowledge behind the diagnosis process, which is embeddedin the information collected from the experts, through the developed model so that it can be applied for the

diagnostic process when the plant is in operation. According to the experts view, whenever some fault occurs on

some part of the cement plant, this is reflected in the form of changes in the Rod displacement, Pressure or flow of

the liquid. Hence these variables are taken as the input of the developed fuzzy model. The input variables along with

the operating range are given in Table 4.

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Table4. Input variables and their operating range

Name of the variable Minimum Value Maximum Value

Flow 2mm3/ Sec 14mm3/ Sec

Rod Displacement 8mm 80mm

From Table 4, it is found that the operating range of the input variables for pump, different types of valves and

pneumatic actuators are same. Based up on the changes (increased beyond the maximum value and decreased below

the minimum value) in the above variables, different types of fault to be occur. The control action for this pneumatic

actuator having the variation in rod displacement with similar variation in the flow rate is tabulated while developing

the fuzzy model.

Membership functions were formed for all the input variables based on their values during the normal and abnormal

conditions. In all the cases, triangular and trapezoidal functions were used and each variable was categorized into

three fuzzy subsets. For illustration the membership functions formed for the variables flow and rod displacement is

shown in figure3. The expert knowledge relating the symptoms and the various faults are formulated in the form of

fuzzy if-then rules. A set of such rules constitutes the rule base of the Fuzzy Inference System. This form of

knowledge representation is appropriate because it is very close to the way experts themselves think about the

diagnosis and decision process. The if-then rules formulated for four critical sets of faults given in Table 5 in the

form of a matrix. The description of the first rule in Table 5 is given as:

Table 5.Fuzzy rule matrix for various faults

FLOW

ROD

DISPLACEMENT

LOW MEDIUM HIGH

LOW F4,F8 F1, F3, F6, F9 F0

MEDIUM F1, F2, F7, F10 F0 F3, F5, F10

HIGH F0F5, F9, F11,

F13F8, F12

IF Flow is high and Rod displacement is high THEN the faults are F8 and F12. The other entries in the rule matrix

can be interpreted in a similar manner. The fuzzy rule matrix along with the membership functions will help to

identify the potential faults present in the system. In order to obtain the global information concerning each fault

represented by the interconnection of its causal chains via AND connectives, is used as an aggregation support. By

placing the AND connectives with their isomorphical fuzzy operators (T-norms), the confidence level is extracted

which combines the distinct pieces of information concerning faults existence from each column into a global fault

possibility. As fault class with particular threshold is considered, for setting of the threshold compromises have to be

made between the detection of fault class and unnecessary false alarms because of normal fluctuations of the

variables. In order to validate the diagnostic hypothesis according to which fault class may explain the system

abnormal behavior the global possibility of the fault class has been projected within an implicit act of backward

reasoning on the respective column of the fuzzy diagnostic model.

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Fig 4. Shows pneumatic actuator system interfaced with PC using DAQ

Based up on the changes (increased beyond the maximum value and decreased below the minimum value) in the

above variables, different types of fault to be occur. The control action for this pneumatic actuator having the

variation in rod displacement with similar variation in the flow rate is tabulated. The variation in flow rate is

measured by using the differential pressure transmitter and this difference in pressure readings were measured and

the output voltage signal is converted in the form of current signal in the range of 4-20mA. This current signal is

obtained by interfacing DAQ (Data Acquisition Card) with the Personal Computer and this is shown in figure 4.

Then the upward and the downward movement of the stem were measured and these parameters are used as the

input variables.

6. Results and Discussion

This section presents the details of simulation carried out on the developed Fuzzy based fault diagnosis in pneumatic

actuator. The fuzzy model was developed using MATLAB version 7.4 on a IBM PC with the clock speed of 2GHz

and 512MB RAM. While developing the fuzzy model min was used for T-norm, max was used for T-conorm was

used. The developed model was tested with a number of test data collected from the Cement industry. Here the

24hours (86400 numbers of data) data was collected under normal and abnormal operating conditions of the

pneumatic actuator and this is recorded in the form of chart. Among these data it was very clearly reported only 13

number of critical faults alone in Table 6, considered four set of faults like Control valve faults, Pneumatic servo-

motor faults, Positioner faults and General faults/ external faults in pneumatic actuator in cement industry. Thecorresponding detail of fault identification is listed in Table 7. For illustration, the results produced by the thirteen

faults for a few cases are given in Table 6.

Table 6. Output produced by various faults for the given input values

RANGE FAULTS

FLOW DISP F0 F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 F11 F12 F13

3.2 15.3 0 0 0 0 1 0 0 0 1 0 0 0 0 0

7.9 14.5 0 0.30 0 0.30 0 0 0.30 0 0 0.30 0 0 0 0

14.2 13.8 1 0 0 0 0 0 0 0 0 0 0 0 0 0

2.2 31.5 0 0.46 0.46 0 0 0 0 0.46 0 0 0.46 0 0 0

7.8 33.5 1 0 0 0 0 0 0 0 0 0 0 0 0 0

14.7 32.3 0 0 0 0.518 0 0.518 0 0 0 0 0.518 0 0 0

2.5 75 1 0 0 0 0 0 0 0 0 0 0 0 0 0

7.2 73 0 0 0 0 0 0.06 0 0 0 0.06 0 0.06 0 0.06

14.8 76.3 0 0 0 0 0 0 0 0 1 0 0 0 1 0

For the given set of input data after fuzzy reasoning and fuzzy decision regarding the type of fault can be determined

by taking the fault classes, having the highest confidence level. For the above case, the inference based on this

procedure is given in Table 5.From the table it is evident that the fuzzy model has identified multiple faults and also

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developing faults. When the flow ranges and the corresponding rod displacement range were 3.2 mm3/ Sec and

15.3mm, then the critical fault is external leakage (F4), servomotor diaphragm perforation (F8) with its output range

is 1. When these faults occurs, then there is no flow through the valve and temperature of the hot gas is increased

which further damage the ESP. When the flow range and the corresponding rod displacement range were 7.9 mm3/

Sec and 14.5mm, then the faults are valve clogging (F1), valve seat erosion (F3), bushing friction (F6), spring fault

(F9)with its output range 0.30. From the above faults are the possibilities to damage the valve. Under normal

operation, the flow range and the corresponding rod displacement range were 14.2 mm 3/ Sec and 13.8mm which

results no fault (F0) with output range 1.

When the flow range and the corresponding rod displacement range were 2.2 mm3/ Sec and 31.5mm, then the faults

are valve clogging (F1), Valve Seat Sedimentation (F2), Medium Evaporation (F7), Positioner Supply Pressure Drop

(F10) with its output range 0.46. The above faults occur definitely. When the flow range and the corresponding rod

displacement range were 14.7 mm3/ Sec and 32.3mm, then the faults are Valve Seat Erosion (F3), Internal Leakage

(F5), and Positioner Supply Pressure Drop (F10) with its output range 0.518.

Table 7. Fault Identification Table

Symbol Name of the fault

F0 Normal operation (No fault)

F1 valve cloggingF2 Valve Seat Sedimentation

F3 Valve Seat Erosion

F4 External Leakage

F5 Internal Leakage

F6 Bushing Friction

F7 Medium Evaporation

F8 Servomotor Diaphragm Perforation

F9 Spring Fault

F10 Positioner Supply Pressure Drop

F11 Unexpected Pressure Change Across The Valve

F12 Fully or Partially Opened By-Pass ValveF13 Flow Rate Sensor Fault

When the flow range and the corresponding rod displacement range were 7.2 mm 3/ Sec and 73mm, then the faults

are Internal Leakage (F5), Spring Fault (F9), un-expected pressure change across the valve (F11), Flow Rate Sensor

Fault (F13) with its output range 0.06. When the flow range and the corresponding rod displacement range were

14.8 mm3/ Sec and 76.3 mm, then the critical faults are Servomotor Diaphragm Perforation (F8), fully or Partially

Opened By-Pass Valve (F12) with its output range 1.

6.1 Comparison of Results

The result produced by the fuzzy model which has displayed in Table6 is compared with the traditional risk analysis

technique namely Hazard and Operability Study (HAZOP). HAZOP study identifies the possible ways in which the

system could fail. It is a systematic technique for identifying hazards or operability problems associated with the

cement industry installations. Each segment of the selected system was analyzed and all deviations from normal

operating conditions and the mode of their occurrence examined.

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Table 8. Comparison of results

Case No

Input values Potential faults

FlowRod

DisplacementFuzzy HAZOP

1. 3.2 15.3 F4, F8 F1, F2, F4, F6, F8, F9, F132. 7.9 14.5 F1, F3, F6, F9 F1, F2, F3, F4, F5, F6, F93. 14.2 13.8 F0 F04.

2.2 31.5F1, F2, F7, F10 F1, F2, F4, F5, F6, F7, F9, F10,

F12, F13

5. 7.8 33.5 F0 F06. 14.7 32.3 F3, F5, F10 F1, F2,F3, F4, F5, F8, F107. 2.5 75 F0 F08. 7.2 73 F5, F9, F11, F13 F5, F9, F11, F139. 14.8 76.3 F8, F12 F8, F11, F12

The results produced using HAZOP for the same input are displayed in Table8. Like the fuzzy models HAZOP has

also identified the multiple faults, but it has produced some false alarms, for instance in cases 1,2,4,6. Further it was

not able to identify the developing faults using fuzzy classification approach. This is evident for cases 1 and 2.

Hence it is observed that the fuzzy model has reduced the false alarm rate and also identified a few faults which are

noticed by the conventional methods. From this comparison, it is found that the fuzzy system model has produced

more accurate results than the conventional approaches.

7. Conclusion

This paper has presented a fuzzy logic based approach for fault detection in pneumatic actuator in cement industry

Totally 19 faults out of which 13 numbers of critical faults of the pneumatic actuator in cement industry were

considered in the fuzzy classification approach. In this paper, the fault symptom relationships were expressed in the

form of fuzzy if-then rules. Simulation results from the model produced accurate results for the same input. The

output produced by the model is compared with the conventional HAZOP model. The comparison showed that

fuzzy logic approach is more suited for the fault diagnosis in pneumatic actuator in cement industry compared to theconventional approaches. To further improve the performance of the model the numerical data collected from the

system have to be used to fine-tune the membership functions and the fuzzy rule base.

Acknowledgment

The authors would like to extend their deepest thanks to Mr. K. Raveendranath, Assistant General Manager,

Instrumentation, The India Cements Limited, for his valuable suggestions and technical support during the course of

the project.

8. References

[1] Isermann, R, Supervision, fault detection and fault diagnosis methods an introduction, Control Engineering Practice, 5 (5), 1997, pp 639-652.

[2] Isermann, R. and P. Balle, Trends in the application of model-based fault detection and diagnosis of technical processes, ControlEngineering Practice, 5 (5), 1997, pp 709-719.[3] Leonhardt, S. and Ayoubi, M, Methods of Fault diagnosis, Control Practice, 5 (5), 1997, pp 683-692.

[4] Diego Ruiz, Jose Maria Nougues and Luis Puisgjaner, Fault diagnosis support system for complex chemical plants, Computers andChemical Engineering, 15, 2001, pp 151-160.

[5] Teodor Marcu, and Letitia Mirea, Robust Detection and Isolation of Process Faults using Neural Networks. IEEE Control System,1,1997, pp 72-79.

[6] Venkatasubramanian, Rengasamy, R., Yin, K., and Kavuri, S.N, A review of process Fault detection and diagnosis. Part-I: Quantitativemodel-based methods, Computers & Chemical Engineering, 27, 2003a, pp 293-311.

[7] Peter Balle, Dominik Fuessel, Closed Loop fault diagnosis based on a no-linear process model and automatic fuzzy rule generation,Engineering Applications of Artificial Intelligence 13, 2000, pp 695-704.

[8] Shengwei Wang, Zhiming Jiang, Valve fault detection and diagnosis based on CMAC neural networks, Energy and Buildings 36, 2004,pp 599-610

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[9] Venkat Venkatasubramanian, Jinsong Zhao, Shankar Viswanathan., Intelligent Systems for HAZOP Analysis of Complex Process Plants,Computers and Chemical Engineering 24, 2000, pp 2291-2302.

[10] Venkatasubramanian, Rengasamy, R., Yin, K., and Kavuri, S.N, A review of process Fault detection and diagnosis. Part-III: Processhistory based methods, Computers & Chemical Engineering, 27, 2003b, pp 327-346.

[11] Babuska, R., and Verbruggen, H.B, An overview of Fuzzy Modelling for control, Control Engineering Practice, 4(11), 1996, pp 593-1606

[12] Yegnanarayana, D. Murthy, Devaraj. D, A Fuzzy System Model for Plant Condition Monitoring. Proceedings of the ASME InternationalConference, Jaipur, India, 1999, pp 210-214.

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