Proceedings of the 10th Mediterranean Conference on Control and Automation - MED2002 Lisbon, Portugal, July 9-12, 2002.
NEURO-FUZZY FAULT DETECTION APPROACH USING A PROFIBUS NETWORK J. M. F. Calado (1), M. Kowal(2), M. J. G. C. Mendes(1), J. Korbicz (2) and J. M. G. Sá da Costa(3) (1)
IDMEC/ISEL - Instituto Superior de Engenharia de Lisboa Polytechnic Institute of Lisbon, Mechanical Engineering Studies Centre Rua Conselheiro Emídio Navarro, 1949-014 Lisboa, Portugal Fax: + 351 21 8317057, E-mail: {jcalado, mmendes}@dem.isel.ipl.pt (2)
University of Zielona Góra, Institute of Control and Computation Engineering ul. Podgórna 50, 65-246 Zielona Gora, Poland Fax: + 48 68 3254615, E-mail: {M.Kowal, J.Korbicz}@issi.uz.zgora.pl (3)
IDMEC/IST – Instituto Superior Técnico Technical University of Lisbon, Control, Automation and Robotics Group Av. Rovisco Pais, 1049-001 Lisboa Codex, Portugal Fax: + 351 21 8498097, E-mail:
[email protected] Keywords: neuro-fuzzy, fault detection, fieldbus, safety, reduce time to market, and increase plant abrupt and incipient faults. flexibility. However, optimisation requires a plant’s different networks, gateways and systems to be Abstract reduced, while simultaneously increasing Generally three methodologies to develop and test price/performance and information integration of FDI algorithms can de distinguished: software field devices, automation systems and management benches, hardware benches and industrial data. The information systems. current approach uses a hardware bench constructed with components commonly used in industry that consists on a pilot plant under supervision, a supervision unit, a fault detection unit and a fault simulation unit. All elements are connected to a PROFIBUS network that acts as the communication system exchanging information between automation system and distributed field devices. A fault detection methodology, which is based on neuro-fuzzy models, has been developed and implemented. During the current studies actuator faults, sensor faults and leakages have been considered as incipient and abrupt faults. Several studies have also been performed under multiple simultaneous faulty scenarios. 1 Introduction
Increasingly, end-users are recognising that Foundation fieldbus is a useful tool for implementing plant optimisation strategies. The technology enables optimisation by replacing proprietary automation systems and networks with a single, open, integrated fieldbus architecture that provides direct integration of sensors, devices, subsystems, data servers and application software packages. Because it is an open specification, a complete range of interoperable, Foundation fieldbus-compliant products are available from the world’s major suppliers of automation equipment. Therefore, in the modern manufacturing industrial processes fieldbus act as the communication system exchanging information between automation systems and distributed field devices, as for instance actuators [9].
Nowadays, plant optimisation is a strategic goal for most organisations. The need to optimise is driven FDI approaches are real time supervisory systems by global economic pressures to increase product with the main aim of to detect and isolate faults in quality, reduce costs, reduce downtime, improve the process under concern. Thus, the reliability of
such systems depends mainly on the information available about the process behaviour [2]. Implementing FDI systems by using fieldbus communications affords more sophisticated functionality increasing the system performance due to the increase amount of information available about field devices behaviour. There are available in the market several fieldbus networks including PROFIBUS, CAN, Interbus, FIP, LON, etc. During the current studies and in order to create a very realistic and flexible industrial environment, a lab pilot plant has been built where all sensors, actuators and controllers are connected to a PROFIBUS network. The pilot plant mentioned includes a level control loop together with a flow control loop and has been built allowing its expansion to include a pressure control loop. The overall process has been constructed using sensors, actuators and controllers commonly used in industry.
system with transparent knowledge. Furthermore, the proposed fault detection approach requires modelling non-linear dynamic systems, which can be described, by a NARX (non-linear autoregressive with exogenous input) input-output model. Then, the paper is organised as follows. Section 2, provides a description of the hardware bench used to test the current fault detection approach. In section 3 is given a detail description of the neurofuzzy methodology used for fault detection purposes. Section 4 presents the results achieved during the current studies. In section 5 some concluding remarks are provided. 2 System Architecture
The goal of the current approach is to overcome the problems mentioned above by using a hardware bench. The architecture of such a bench is shown in Figure 1. It contains six main blocks: Pilot Plant Software benches are the easiest and cheapest way (process under supervision), supervising unit, FDI to develop and test FDI algorithms. However, in unit, PLC controller, fault simulation unit and a order to achieve accurate results a very accurate PROFIBUS network. model of the process under concern is needed, which often is hard or impossible to obtain. In The Pilot Plant consists of four interconnected order to consider a real environment to test new stations that provide the control of filling level, FDI algorithms, real data achieved from flow, pressure and temperature. However, during measurements on a real industrial plant could be the current studies only the stations corresponding used. However, when real processes are to the level control loop and the flow control loop, considered, data for operation under faulty have been considered as it is described in section 4. scenarios are often quite scarce relative to data A realistic industrial environment has been corresponding to normal process operation. achieved, where the behaviour of one station Therefore, all that problems motivate the influences the other stations and their control loops. development of a hardware bench as previously In other words, under the current approach there quoted allowing the test of new FDI are coupling effects between control loops. The methodologies. The lab pilot plant developed overall process consists of sensors, actuators and avoids the limitation of software benches or data controllers commonly used in industry. achieved from real industrial plants. The supervising unit is a PC station with the During the current studies actuator faults, sensor software installed for the process monitoring and faults and leakages have been considered as logging. Graphic oriented software was prepared in incipient and abrupt faults. Multiple simultaneous the InTouch environment to achieve that task. The scenarios have also been considered. The fault fault simulation unit is responsible for introducing detection task is based on neuro-fuzzy models. It the faults in the process under supervision. integrates fuzzy modelling techniques and neural Additional actuators that are mounted to the native networks learning abilities. The main feature of structure of the pilot plant and are interacted such approach is to join the strengths of both through the PLC controller from the PC station quantitative and qualitative modelling (fault simulation unit) allows the simulation of methodologies, in order to achieve a learning some fault actuators.
FDI system
Supervising unit
Fault simulation unit
PROFIBUS network
PLC Station 1
Station 2
Pilot Plant
Figure 1: Hardware bench architecture. The fault detection algorithm has been implemented through the Matlab/Simulink environment and installed on the PC computer fitted with a PROFIBUS card. Thus, all necessary measurements will be accessible through the PROFIBUS FMS DDE Server. All elements of the bench, such as sensors, actuators and controllers, are connected to the PROFIBUS network in order to allow data exchange between nodes of the network. PROFIBUS is an international, vendor independent open fieldbus standard, under the European Fieldbus standard. In manufacturing industrial process and building automation applications, serial fieldbus can act as the communication system for exchanging information between automation systems and distributed field devices. Both high-speed time critical data transmission and complex communication tasks can utilise PROFIBUS. The standard also allows devices from multiple vendors to communicate without special interface adjustments. PROFIBUS is a polled protocol with a layered architecture designed specifically for industrial control networks [9]. Operation specific to industrial control, such as fail-safe operation and globally co-ordinated device updates, are included in the protocol specification. Reliable operation is augmented by powerful error detection algorithms (CRC or Cyclic Redundancy Checking) and watchdog timers. PROFIBUS use a non-powered twisted-pair transmission medium and industry standard RS-485 carried levels, which result in exceptional noise immunity. Typically, a PROFIBUS network is controlled by one or more PLC’s like in the current approach.
A PROFIBUS network may have up to 126 nodes. It can transfer a maximum of 244 bytes of data per node per cycle. Communication baud rates are selectable but overall end-to-end network distance varies with speed. The maximum communication baud rate is 12 Mb/s with a maximum distance of 100 meters. The maximum distance is 1200 meters at 93.75 Kb/s without repeaters. PROFIBUS connects to a wide variety of field devices including discrete and analogue I/O, drives, robots, HMI/MMI products, pneumatic valves, barcode readers, transducers and some measuring equipment. The disadvantages of using PROFIBUS networks are the following: high overhead to message ratio for small amounts of data, no power on the bus and slightly higher cost than some other buses. 3. Fault Detection System Generally, the methods of fault detection can be divided into two groups: a process variable monitoring and a more complex model based methods. For a simple fault that can be detected by a single measurement, a conventional alarm circuit may be appropriated. However, since it is usually very difficult in complex industrial systems to directly measure the state of the process, more sophisticated solutions are needed. In this case a model-based approach will be more suitable. This requires process modelling and may be called an analytical redundancy because the model and the process work in parallel. The idea of model based fault detection is to compare output signals of the model with the real values measured from the process, thereby generating residuals, which are fault indicators. This approach makes it possible to detect small scale and incipient faults quickly and reliably. These are the reasons of using such systems in life-critical applications where even small defects can cause big damages. Different methods of model design are available. The most popular are analytical (the Kalman filter, the Luenberger observer, etc.) and AI methods (neural nets, expert systems, fuzzy systems and neuro-fuzzy systems). In the recent years, neural nets and neuro-fuzzy applications have received much attention due to their fast and robust
implementation, their performance in learning arbitrary non-linear mappings, and their abilities of generalisation [5,7]. Especially, the neuro-fuzzy (N-F) approach has been actively employed for modelling. It integrates fuzzy modelling techniques and neural nets learning abilities. The main feature of such approach is to join the strengths of both quantitative and qualitative modelling methodologies, in order to achieve a learning system with transparent knowledge. However, N-F techniques encounter the problem of exponential growth of the network structure when the dimension of input-output space increases or the number of fuzzy partition increases. The complexity of the N-F structure causes the drastic growth of the computational cost during the learning process. Moreover, knowledge coded as rule base is unclear due to a large number of rules. Such situation can reduce the usage of the N-F networks to simply industrial processes. Fortunately, there are several methods, which can help to reduce the N-F network structure [3,4,9].
Generally Takagi-Sugeno N-F structures have a better performance in modelling than other structures due to their possibility to representation of non-linear systems by several local linear models as depicted in Figure 2. For this reason the N-F network with Takagi-Sugeno topology are used in the proposed fault detection approach as models. The structure of Takagi-Sugeno system can be presented in the form of layered topology similar to the neural networks. Such structure is shown in Figure 3 where the following notations are used: x1,...,xn – are the inputs, y – is the output, n – is the number of inputs, N – is the number of rules, Ni – is the number of fuzzy partitions for i-th input and c, w and a are the weights.
Thus, the current N-F network consists of five layers. The elements of first layer are responsible for calculation of the input signals membership degrees. The membership functions are typically defined as Gaussian functions where c is the center of the Gaussian function and parameter w is the Two types of N-F networks are commonly used for width as shown in Figure 4. modelling purposes: Mamdani N-F network and Takagi-Sugeno N-F network [1]. The relationships It should be noticed that not all network inputs between inputs and outputs in the presented have to be connected with nodes in the first and networks are described by the if-then fuzzy rules, fifth layer. Some inputs can be connected only with which structure depends on the type of the network. In Mamdani N-F network the antecedents and conclusions are represented by fuzzy sets as represented in the heuristic rule (1). IF x1 is A1 and … and xn is An THEN y is B (1) In Takagi-Sugeno N-F network the antecedents are also defined as fuzzy sets but the rules consequent are expressed by linear combinations of input variables as shown in the IF-THEN rule (2). IF x1 is A1 and … and xn is An THEN y=a0+a1x1+…+an,xn
(2)
Figure 2: Pice-wise linear approximation of a nonlinear system.
Figure 3: Takagi-Sugeno neuro-fuzzy network.
N
∑ µ (x ) f ( x ) i
y=
N
nodes in fifth layer. In Figure 3 inputs x1,...,xn are directly connected with first and fifth layer but inputs xn+1,...,xn+m are connected only with fifth layer. The nodes in the second layer realize algebraic product given by equation (3) that is used to do the operation of aggregation in order to achieve the rules firing levels.
j =1
(6)
i
of membership functions, which express antecedents and the weights a are the parameters of linear functions, which express the consequences.
Figure 4: Gaussian function.
k
.
∑µ (x ) i =1
τ i = ∏ Aij (x j )
i
i =1
After the Process Control System has been studied, it was decided to build two models using the neurofuzzy technique as depicted in Figure 5: -
model of the filling level control; model of the flow control.
The proposed models are used to generate residuals, which are computed as a difference between system and model outputs given by (3) equation (7).
The number of elements in the second layer determines the number of defined rules. If all combinations of nodes from first layer are used to construct the rules, the number of rules is described by the equation (4).
r1=yl – ylm
r2=yf – y fm
(7)
where r1,r2 – residuals, yf – measured flow, yl – measured level of the fluid, yfm - flow predicted by the flow model, ylm – level predicted by the level model. Proposed fault detection scheme requires modelling of non-linear dynamic systems (level n control, flow control). Such systems can be N = ∏Ni (4) described by the NARX (non-linear autoregressive i =1 with exogenous input) input – output model as where Ni is the number of fuzzy partitions defined represented by equation (8). for i-th input and n the number of inputs. In order to reduce the number of rules, it is possible to use only some necessary combinations of nodes from first layer to build the rules. The third layer realizes the inference operation that is commonly defined as an algebraic product. The fifth layer express conclusions described by linear combinations of input variables as represented by equation (5). f(x)=a0+a1x1+...+an+mxn+m
(5)
The fourth layer is responsible for defuzzification of computed results and is realized by two summation nodes and one division node. The most commonly used method of defuzzification is a height method given by equation(6). Three types of weights c, w and a are tuned during the learning process of the current N-F network structure. The weights c and w are the parameters
Figure 5: Two suggested models for residual generation purpose.
y(k+1)=f( y(k),y(k-1),...,y(k-na+1),u(k),u(k-1),..., u(k-nb+1) )
(8)
where y(k+1) is a predicted system output, y(k), y(k-1), y(k-na+1) past outputs, u(k), u(k-1), u(knb+1) past inputs, f – unknown function. The system identification process can be seen as a searching for an appropriate function f. In the presented approach, the unknown function f will be approximated by the N-F system. Generally system identification procedure can be divided into two steps: structure identification and parameters estimation. In this work the first step consists on two phases: input variable selection and rule base self-generation. During the first phase the input variables and number of input and output lags were selected and next it was decided, which input variables should be included in the antecedent part of fuzzy rules. Different structures of the models were achieved and next compared during tests. The performance criteria for tested structures were defined in the form of the sum of the squared errors. Implemented structures were tested using validation data and the best was chosen to build the model. During the second phase clustering algorithm called Mountain Method was used to networks structure self-generation [8]. The basic idea of such approach is to group the input-output data into clusters and use one rule for each cluster. Such methodology was possible to applied because Mountain Method do not require predetermination of the number of the clusters, it needs only intersection points of grid lines drawn on the inputoutput space as candidates for the cluster centers. The Mountain Method cannot determine the sizes or shapes of the discovered clusters. The second step of the system identification is required to estimate the parameters of N-F network. At first clustering algorithm called FCM is used to determine the sizes of discovered clusters and their centers and next the obtained information is utilized to estimate the centers and widths of membership functions. The parameters of rules linear consequent (Takagi-Sugeno model) are initialized using ARX method and during the next step all parameters are tuned using the backpropagation algorithm.
The proposed models are applied to generate residuals, which are used by the second stage of the FDI system (fault classification) [6]. 4. Case Study A Pilot Plant has been developed for didactic purpose and can be used for training in the field of automatic control, communication and FDI. This training environment is very realistic due to employing components, which are commonly used in industrial processes. It consists of four individual modules, which can be combined, in various ways. Each module can be easily constructed or converted due to the flexible structure. The networked system with four stations may have different process configurations. However, the process structure that was chosen to prepare a bench for test the fault detection system is shown in Figure 7. It consists of two stations: station 1 filling level control (Figure 6a), station 2 - flow control (Figure 6b). The stations are connected together for fluid transmission. A closed control loop scheme is used to keep the fluid level in tank T1 at the desired value. The centrifugal pump, which is shown in Figure 8a, supports the adjustment of the controlled variable. A PID controller via the motor regulation, which enables the supply and pump speed variation, actuates over the controlled variable. In order to make the filling level value accessible to the controller, the level ultrasonic sensor depicted Figure 9a, measures it.
a)
b)
Figure 6: Pilot Plant Stations - a) filling level control; b) flow control.
Figure 7: Pilot Plant Diagram. The second station provides the flow control and it is connected with the station 1. The closed control loop technology supports the task of keeping the flow at the desired value. Two modes of control are a) b) available in this system. The PID controller can Figure 9: Sensors - a) Filling level ultrasonic actuate a centrifugal pump equal to the one used in sensor; b) Flow sensor. station 1 (Figure 8a) or a proportional valve (Figure 8b). For bench purposes, the second control scheme - Valve V1 closing (incipient fault); was chosen. In this case, pump runs constantly and - Ultrasonic sensor LI30 with a false the proportional valve can adjust the flow. In order measurement; to measure the flow, fluid is guided into a flow - Leakage in Tank 1 (abrupt and incipient sensor (Figure 9b) and directed into a lightweight fault). triple vane rotor. The rotor speed is proportional to All the abrupt faults have been successful detected , the flow rate and is measured through a built-in as well as the corresponding incipient faults if the optoelectronic infrared system. component degradation speed value isn’t very During the current studies it has been considered small. the following faults: Two models have been implemented using the - Pump P1 stopped; described neuro-fuzzy techniques: flow control - Pump P1 uncontrollable (pump will be model and level control model. The structures of turned on at the maximum speed without designed models are presented in Table 1 where the control); following notation is used ul(k) – level control - Valve V1 closed and blocked (abrupt fault); value, uf(k) – flow control value, l(k) – real level, - Valve V1 opened and blocked (abrupt f(k) – real flow, lm(k) – predicted level, fm(k) – fault); predicted flow. Based on knowledge about the - Valve V1 opening (incipient fault); systems, it was decided to include only some important inputs in antecedents of fuzzy rules. Such approach allows achieving simplified structures of models.
a) b) Figure 8: Actuators - a) Centrifugal pump; b) Proportional valve.
It must be remembered that level control station and flow control station are interconnected for fluid transmission and it was detected that setpoint changes on the flow control station influence the behaviour of level control station. Such coupling effect was simulated by introducing in the level control model an additional input variable defined as flow f(k). It was also observed that steady state characteristic of the flow control station contains
hysteresis as an effect of friction in the valve V1. The simple idea to simulate hysteresis in the neurofuzzy model considers increasing the number of input variables that are included in antecedent (Figure 10a) (for points (u1,f1), (u1,f2), (u2,f2) three different rules R8, R2 and R3 are fired). However when hysteresis is very small the model generates an errors (Figure 10b) (for three different points of steady state characteristic (u1,f1), (u1,f2), (u2,f2) the same rule R5 is fired).
f(k)
Models
Inputs
level control model flow control model
ul(k), ul(k-1), ul(k-1), lm(k), lm(k-1), f(k) uf(k), uf(k-1), uf(k-2), f m(k), f m(k-1), f m(k-2)
Inputs No. Fuzzy included in of Outputs partitions antecedents rules ul(k), f(k)
[4,5]
12
lm(k+1)
uf(k)
4
4
f m(k+1)
Table 1: Structures of designed models.
R1
R2
R3
R4
R5
R6
R7
R8
R9
u1
u 2 u(k)
b) Input - output space
f(k)
R1
R2
R3
f2 f2 f1
R4
R6
f1
Increasing the number of delayed inputs or outputs included in antecedent and increasing the number f(k) of rules can solve the problem. However, the model obtained in this way could be very complicated and completely unclear for human (Figure 10c). This problem can be seen as a trade-off between ff2 1 simplicity and quality of the model. It was decided that for the problem under consideration the better solution will be to use a simply model because the error introduced by the hysteresis effect is very small (Figure 12d). The second phase of model designing considered the model structure simplification using accessible measurements. The structures of models were simplified using the Mountain Method. The mountain function was computed for intersection points drawn on the input space (Figure 11a) and next the point with the maximum value of the mountain function was chosen as a center of the new cluster. For each found cluster one rule was generated in the N-F network structure. The process was repeated until the next maximum value of the mountain function for the intersection point was smaller than defined î. All found clusters centers for the level control model are shown in Figure 11b.
a) Input - output space
R7
c)
R8
R9
u1 u2
u(k )
d) Input - output space
f(k)
Input - output space R1
R2
R3
f2 f1
u1 u2
u1
u(k)
u(k)
Figure 10: Hysteresis as an effect of friction in valve. a)
b)
Figure 11: Level control model structure simplification a) mountain function, b) found clusters. The models parameters were estimated during three-step procedure. The parameters of antecedents were determined using FCM clustering algorithm and the obtained clusters were projected on the input variables to achieve fuzzy partitions for each input (Figure 12a and 12b). Similar membership functions were joined. The fuzzy partitions defined for inputs of the level control model and the flow control model are shown in Figure 13a and 13b respectively.
Figure 14: Validation data a) level control model response, b) residuals r1. Figure 12: Fuzzy partitions achieved by cluster projection on input variables: a) control value, b) flow. In the next step, the parameters of the heuristic rules linear conseqent were initialized using ARX method and next all parameters of both models were tuned using the backpropagation algorithm with adaptive learning rate and momentum rate. All feedbacks in models during the learning task were opened and values of delayed outputs were taken from the real processes. The learning set for the level control model contains 2500 vectors and the learning set for the flow control model contains 4600 vectors. These data were generated using randomly made changes of the working setpoints. After the learning task, the performances of the implemented models using validation data can be seen in Figure 14 and 15. The performance index was defined in the form of the sum of the squared errors divided by the number of samples. a)
b)
Figure 15: Validation data a) flow control model response, b) residuals r2. All faults mentioned above have been simulated and successful detected. In Figure 16 can be seen the result achieved under an abrupt faulty scenario. Figure 17 depicts the results achieved under a double incipient fault situation. Also successful results have been obtained under triple abrupt and incipient faulty scenarios. In order to isolate the faults, the current approach will be coupled with a hierarchical structure of fuzzy neural networks, which will be fed with the residuals generated by the current neuro-fuzzy approach [6]. 5 Conclusions
Figure 13: Fuzzy partitions defined for a) level control model, b) flow control model.
In modern process plants using fieldbus communications through a network like for instance PROFIBUS, the implementation of FDI systems could afford more sophisticated functionality increasing the system performance
suggests a great potential of that methodology for practical Acknowledgement This research was supported by the EC Framework 5 Programme “Human Potential” and Research Training Network DAMADICS. References
Figure 16: Abrupt fault: pump uncontrolable a) level control model response, b) residuals r1. Flow modelling
Level modelling
1
0.8 Real flow Model response
0.8
Real level Model response 0.6
Level
Flow
0.6 0.4
0.4
0.2
0.2 0
0 0
100
200
300
0
100
Samples Flow residuals
300
Level residuals
1
1
0.5
0.5 Residuals
Residuals
200 Samples
0
-0.5
0
-0.5
-1
-1 0
100
200 Samples
300
0
100
200
300
Samples
Figure 17: Double incipient fault: valve opening incipient fault and ultrasonic sensor fault a) flow control model response, b) level control model response, c) residuals r2, d) residuals r1. due to the increase amount of information available about field devices behaviour. A hardware bench has been built using industrial components connected through a PROFIBUS network, which has proven to be very useful to test new FDI methodologies in a very realistic environment avoiding the limitations of software benches or data achieved from real industrial plants. The successful results achieved with a FDI approach, which consists on a neuro-fuzzy system for fault detection purposes coupled with a hierarchical structure of fuzzy neural networks, tested in real time in a very realistic pilot plant,
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