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FAULT DIAGNOSIS OF THE DAMADICS BENCHMARK ACTUATOR USING NEURO-FUZZY SYSTEMS WITH LOCAL RECURRENT STRUCTURE Letitia Mirea*, Ron J. Patton** * ”Gh.Asachi” Technical University of Iaşi, Dept. of Automatic Control ** University of Hull, Dept. of Engineering. 1. Introduction

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1. Introduction 2. Fuzzy inference systems and fuzzy modelling

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FAULT DIAGNOSIS OF THE DAMADICS BENCHMARK ACTUATOR USING NEURO-FUZZY SYSTEMS WITH LOCAL RECURRENT STRUCTURELetitia Mirea*, Ron J. Patton*** ”Gh.Asachi” Technical University of Iaşi, Dept. of Automatic Control** University of Hull, Dept. of Engineering

1. Introduction

2. Fuzzy inference systems and fuzzy modelling

3. Adaptive neuro-fuzzy systems with local recurrent structure

4. Neuro-fuzzy design of an FDI system

4.1 Residual generation

4.2 Residual evaluation

5. Application

### 2. Fuzzy inference systems and fuzzy modelling

 Fuzzy Inference System (FIS):

- rule base

- data base

- reasoning mechanism

 Most applied FIS for system modelling  Sugeno fuzzy system

Rule i:

if x1 is A1 and x2 is A2and ... and xn is Anthen

 Consequents of each fuzzy rule  local model

 Antecedents of each fuzzy rule  define region in input space where local model applies

 Sugeno model can be implemented as special type of neural network

Adaptive Neuro - Fuzzy System (ANFS)

### 2. Fuzzy inference systems and fuzzy modelling

 ANFS combines:

- capability to handle uncertain & imprecise information (from fuzzy systems)

- ability to learn from examples (from neural networks)

 Identification of dynamic systems  models with adequate memory

 ANFS should be provided with dynamic elements:

- ANFS, external dynamic elements (ext. cascades of linear filters)

- ANFS, internal dynamic elements (recurrent connections, internal filters)

ANFS with Local Recurrent Structure (ANFS-LRS)

### 3. Adaptive NF systems with local recurrent structure

 local model described by:

3. Adaptive NF systems with local recurrent structure

 Layer 1: adaptive, membership functions

 Layer 2: computes the firing strengths of fuzzy rules

 Layer 3: computes normalised firing strengths of the fuzzy rules

 Layer 4: adaptive, outputs of local models

 Layer 5: computes the overall output of the ANFS-LRS

3. Adaptive NF systems with local recurrent structure

 identification of MIMO system ANFS-LRS model for each output of

process:

 ANFS-LRS learning:

- number of fuzzy rules and initial values of premise parameters

 fuzzy clustering algorithm (Chiu, 1994)

- ANFS-LRS parameters  gradient method:

N - number of the training data

yP,i - the i-th output of the process

 - learning rate

4. Neuro-fuzzy design of an FDI system4.1 Residual generation

 FDI system: residual generation and residual evaluation

 Residual generation:

- an ANFS-LRS model for each system output is identified (MISO model)

- MISO models  neuro-fuzzy observer scheme

- generated symptoms (current state)  residuals

 Neuro-Fuzzy Simplified Observer Scheme (NF-SOS):

- MIMO process with I inputs uP,i[k], i=1,...,Iand O outputs yP,j[k], j=1,...,O

- NF-ARX models: normal behaviour of the process

- residuals  one-step ahead prediction error

4. Neuro-fuzzy design of an FDI system4.2 Residual evaluation

 Residual evaluation pattern classification using neural networks

- pattern classifier  static Multi-Layer Perceptron

- decision logic  Euclidean distance

5. Application

 Investigated process: actuator from the steam boiler used to control the water level in the 4th boiler station (Lublin sugar factory, Poland)

 Real data corresponding to the normal behaviour of the process have been used to:

- design the NF-SOS scheme using ANF-LRS

- generate faulty data using the DAMADICS benchmark

Considered faults:

F1: Valve clogging

F2: Valve plug or valve seat sedimentation

F3: Servo-motor’s diaphragm perforation

F4: Electro-pneumatic transducer fault

F5: Rod displacement sensor fault

F6: Positioner feedback fault

F7: Fully or partly opened bypass valve

F8: Flow rate sensor fault

5. Application

Methodology:

Data filtering: low-pass Butterworth filters  noise reduction and data decimation

Selection of used data:

 training data set: 360 out of 3600 measurements – NORMAL behaviour

 testing data sets:

- data set 1: 3600 measurements (another hour, same day)

- data set 2: 3600 measurements (previous day)

- data set with faults

Residual generation:

 system identification using ANFS-LRS

 neuro-fuzzy simplified observer scheme

Residual evaluation:

 static neural classifier (MLP/ BP)

 decision mechanism based on the Euclidean distance

5. Application

inputs:

u1 – level controller output

u2 – valve input water pressure

u3 – valve output water pressure

u4 – temperature of the water

outputs:

y1 – servo-motor rod displacement

y2 – water flow to steam boiler inlet

Testing data set 1

Testing data set 2

 Electro-pneumatic actuator:system identification using ANFS-LRS

5. Application

 data with faults  example for fault F3:

 Residuals generated with NF-SOS based on ANFS-LRS corresponding to:

- the normal behaviour

- the considered faulty behaviours

were evaluated using a neural classifier  Multi-Layer Perceptron

 Obtained recognition rate: 93.67%

### Conclusions

 The present paper investigates the development of a neuro-fuzzy system with local recurrent structure and its application to fault diagnosis of an electro-pneumatic actuator (DAMADICS benchmark).

 The advantages of using such a neuro-fuzzy system are:

- it is abble to process uncertain information;

- automatic extraction of the rule-base;

- it is able to learn from examples;

- it has a reduced input data space because of its locally recurrent structure.

 The obtained experimental results by using the suggested neuro-fuzzy system reveal its good performances of approximation and generalisation.

 Its application to fault diagnosis of an industrial process leads to good results reflected in a recognition rate greater than 90%.