Model-based Fault Detection and Isolation using Neural Networks: An Industrial Gas Turbine Case Study
Abstract—This study proposed a model based fault
detection and isolation (FDI) method using multi-layer perceptron (MLP) neural network. Detection and isolation of realistic faults of an industrial gas turbine engine in steady-state conditions is mainly centered. A bank of MLP models which are obtained by nonlinear dynamic system identification is used to generate the residuals, and also simple thresholding is used for the intend of fault detection while another MLP neural network is employed to isolate the faults. The proposed FDI method was tested on a single-shaft industrial gas turbine prototype and it have been evaluated using non-linear simulations based on the real gas turbine data. A brief comparative study with other related works in the literature on this gas turbine benchmark is also provided to show the benefits of proposed FDI method.
Keywords- Fault detection and isolation, Neural network, industrial gas turbine, Multi-layer perceptron, System identification, Nonlinear predictor model.
I. INTRODUCTION Nowadays reliability is one of the crucial issues in
automatic system design and has received great attention during last two decades. All kind of large scale systems such as power plants are increasingly required to provide safety in operation for long periods of time. One of the critical components of a power plant that its real time monitoring and supervision must intensively be taken into account is gas turbine engine. Due to manufacturing defects, erosion-corrosion and tear, and other kind of performance deteriorations in system’s components, and in order to prevent major collapses in plant, system shutdowns, “early” diagnosis of faults is an important factor [11]. Fault detection and isolation is a two sequential phase algorithm: (1) One or several signals so-called “residual” are determined in order to characterize each fault, (2) Time and location of possible faults are determined by analyzing of the residuals.
The problem of fault detection and isolation to gas turbine engine was investigated in other previous papers in literature. Classification abilities of neural networks and fuzzy logic are used to propose signal-based FDI methods, which avoid system modeling, for designing gas turbine diagnostic [1],[8]. In most of the model based gas turbine fault detection and isolation approaches the common and frequently used residual generators are
dynamic observers while classical techniques like statistical tests are employed to evaluate the residuals [11],[12],[14]. Some other studies also used observers as residual generators, and neural networks for fault identification as a hybrid FDI scheme [9], [10]. However, the main drawback of such model-based FDI methods is inaccurate residual generation in whole operating ranges due to linear modeling of complex industrial systems. Neuro-fuzzy techniques were exploited for both residual generation and evaluation in detection and isolation of actuator fault of an industrial gas turbine [2], [13]. To the best of author’s knowledge applications of neural networks to model based FDI of industrial gas turbines using neural based predictor modeling, were not considered and could still be an open area of research. As linear models are seldom effective and straightforward in describing complex industrial processes in comparison with non-linear models and in order to overcome some of the difficulties of using mathematical models and make FDI algorithm’s more applicable to real systems the neural networks can be used for both generate residuals and isolate faults [3].
Contemporary diagnostics of processes are mainly based on process models, and in order to detect the occurrence of fault, model of the normal process behavior is needed [5]. General conceptual structure of model based fault detection and isolation is depicted in Fig.1. Fault indicative residuals are made by comparison of the observed and nominal behavior of the system, Detectable deviation of the residuals yield to analytical symptoms and subsequently suitable decision on the relation between symptoms and faults is made.
The rest of paper is organized as follows: In section 2 a brief overview of the underlying gas turbine and also description of its possible faulty scenarios are presented, section 3 introduces the proposed FDI method, and moreover generation and evaluation of residuals using MLP neural networks are also described in sections 3.1 and 3.2, respectively. Simulation results obtained by proposed techniques are included in section 4, and also in order to show the effectiveness of the proposed method a brief comparison with other proposed FDI methods on considered gas turbine benchmark is
presented. Finally in section 5 main conclusions obtained are drawn.
Figure 1. General structure of fault detection and isolation.
II. SYSTEM AND FAULTY SCENARIOS DESCRIPTION In gas turbine engine of interest in this study, air
flows via an inlet duct to the compressor and the high pressure air from the compressor is heated in combustion chambers and expands through a single stage compressor turbine. A Butterfly valve provides a means of generating a back pressure on the compressor turbine. Cooling air is bled from the compressor outlet to cool the turbine stator and rotor. A Governor regulates the combustor fuel flow to maintain the compressor speed at a set-point value [7]. For simulation purposes a prototype model of such an industrial gas turbine developed at ABB-Alstom Power, United Kingdom was used. The prototype simulates the real measurements taken from the gas turbine with a sampling rate of 0.08s. The model has two inputs and 28 output measurements. The SIMULINK model where validated in steady state conditions against the real measurements and all the model variables were found to be within 5% accuracy [11]. Four common faulty scenarios of industrial gas turbine engine are proposed to be tackled in this study:
Compressor contamination, [f1] core engine performance deterioration.Thermocouple sensor drift, [f2], (Slowly increasing reading over the time).High pressure Turbine seal damage, [f3], (turbine efficiency gradual reduction).Fuel actuator friction wear, [f4].
Figure 2. Logic sketch of the single shaft industrial gas turbine with the monitored sensors highlighted..
Among 28 output measurements four of them, that based on the preliminary analysis of the process could be sensitive to faults, are taken in to account for FDI task
[11].Valve angle(av), and fuel flow(ff) that is also a control variable are the input measurements, whereas compressor torque(Qoc), Compressor outlet temperature (Toc), combustion chamber outlet pressure (Pocc) and, combustion chamber back pressure into compressor (Pccb), are the considered output measurements used for the intent of FDI [11].
III. PROPOSED MLP BASED FDI METHOD The proposed FDI scheme is shown in Fig. 3, and can
be abbreviated in two parts: residual vectors generating and residual evaluation for fault detection and isolation. Detection of faults is implemented by modeling of the normal behavior of monitored gas turbine system using MLP- based nonlinear observers, and then the residuals are generated by comparing the predicted and system outputs. Simple thresholding is exploited in order to make decision whether or not a fault occurred. For isolation purpose classification capability of a MLP neural network is employed to make decision on the relation between symptoms and faults.
Figure 3. Logic sketch of the single shaft industrial gas turbine with the
monitored sensors highlighted..
A. Residual generation using MLP models The residual signals are generated based on
comparison between the measurements coming from plant full scale simulator and correspondent predicted signals given by the MLP models. The residual are calculated as follows:
(2) Where and are the system measurements
, and predictions, respectively. It is clear that the residual signals should have near zero behaviour in absence of faults otherwise meaningfully deviation from zero. The fact that residual should only reflect fault information is affected by presence of model uncertainty, noise and disturbance. Due to the ability of neural networks to make intelligent decisions in cases of noisy and corrupted data they can be employed to tackle to above mentioned uncontrolled effects in FDI task of industrial systems. In order to have suitable and accurate residual signals showing fault information in presence of un-avoidable effects, residual generation needs highly accurate process models. Fig.4 shows the MLP architecture with tapped delay lines (TDLs) using for prediction of system outputs that is based on the non-linear input/output model as follows:
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(2)
Where is a nonlinear function, and are the model and plant outputs respectively, and U(k) contains the inputs of plant, n denotes the numerator orders of inputs, and m is the denominator order. The order selection can be carried out experimentally using the trial and error procedure [6].
Figure 4. MLP-based predictor model with external dynamics.
The input layer of MLP network contains past values of Inputs and output that are generated by feeding the current input and output values into TDLs. The neurons in hidden layer have a tangent hyperbolic activation function, while the activation functions of output layer are linear.
B. Residual evaluation After generating of residuals, thresholds must be
determined in order to detect abnormality caused by fault. The optimal selection of the thresholds is made through a compromise between false alarm rate and revealing of fault detection. In this work the upper and lower bands of the thresholds aresettled on the basis of non-faulty residual R(t) using a simple thresholding method which can be define as follows:
Where and are the mean and standard deviation values of fault free residual R(t), respectively. � is a Tuning parameter and owing to the presence of modelling errors, it has to be properly selected in order to achieve the best performances in term of false alarm and missed fault rates [3].
In practice, � usually can be chosen according to the three sigma rule e.g. 1, 2 or 3. After setting the thresholds, fault can be detected using the following logic:
Analytical symptoms are then obtained as changes of the faulty residuals with reference to the normal values [4].
Basically isolation of faults implies to accommodate each pattern of the residual vector with one of the pre-allotted classes of faulty condition, if available, and the non-faulty condition. Hence the decision about which fault has taken place and also the correspondent location
is made in this part. In order to isolate the faulty patterns, classification ability of a two hidden layer MLP network is considered as well. The topology of a MLP with two hidden layer suitable for fault classification is depicts in Fig.5. In order to tackle to fault isolation problem of, the MLP based classifier has n inputs because of n analytical symptoms and two L3 outputs because each class of faulty behaviour is coded with a L3-bit (binary) representation. To achieve better classification results, MLP network with two hidden layer with tangent hyperbolic activation function and one output layer with the sigmoidal neurons is employed. For fault indication purposes, the real-valued response of the classifier is transformed to the binary one considering the distance between the classifier output and each class of system behaviour. Hence, the binary representation which results the shortest Euclidean distance is selected as the classifier binary output and can be represented as follows
(5) Where denotes Euclidean distance and is the
number of classes, and is the output of the classifier, and is binary representation of -th class of system behaviour. Consequently, the binary representation of the classifier output can be determined in the form of
.
Figure 5. MLP architecture for fault classification.
Figure 6. Optimal neuron number of compressor torque MLP model.
IV. SIMULATION RESULTS In the case of neural network based system
identification the important factor is number of neurons as well as epochs. Large number of neurons caused complexity in computations and also over parameterization problem. Thus, small and reasonable
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neuron number is preferable. Optimal neuron and epoch number are determined by means of mean square error curves. Since four outputs are taken into account for intent of FDI, four MLP models are made. Fig.6 show optimal number of neurons for first MLP model. These procedures are also exploited to determine other three MLP model based on other outputs of the plant. In order to obtain an accurate identification results for all of four cases a Levenberg–Marquardt training algorithm was employed and training was terminated when a minimum in the mean-square error of the test data was achieved. The identification results are presented in Table . Through trial and error during identification, the best number of input and output dynamics is obtained. The best numbers of dynamics used for identification are presented in table 2. As an example, the model output for the case of compressor torque for both training and validation data are illustrated along with correspondent system outputs in Figs.7-8.
Figure 10. Generated residual based on compressor torque for f2.
Figure 11. Generated residual based on compressor torque for f3.
Figure 12. Generated residual based on compressor torque for f2.
After training four accurate MLP models in fault free condition, these models are used to generate residuals by running the simulator with fault free and then all faulty cases operating one by one over the operating ranges. By using the scheme presented in Fig.3, residuals are generated in different faulty cases and are shown in Figs.9-12. According to Fig.2, due to the feedback control action in steady state condition, even if actuator fault takes place as gradual development, its effects on output measurements cannot be seen in a ramp mode, but the effects of this fault will be revealed in dynamics. In this case absolute values of this residual vector are taken into account and then are passed through a post-filter for intent of fault classification. However, it is obvious that the residuals are almost close to zero be for fault occurrence at 250-th time step and deviate from zero
Figure 7.MLP Actual and model output(training)of compressor torque.
Figure 9. Generated residual based on compressor torque for f1.
Figure 8. MLP model performance of compressor torque.
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gradually after commencing of fault in different manners. In order to perform fault detection the thresholds were settled in fault-free condition. Residuals indicate fault occurrence according to the relation (4), whether their values are lower or higher than the thresholds fixed in fault free conditions.
As an example Fig. 13 represents the fault free (green continuous line) and the faulty (violet continuous line) residuals R2 (t) related to the Toc (t) thermocouple sensor fault in the test phase, and also the fault detection thresholds reported in the relations (3) are represented as black continuous constant lines selecting � =2. Table 3 shows the imposed threshold values for the generated residuals according to (3), and their values were properly settled in order to lead to both minimize the false alarm rates and maximize the correct detection rates. The results of the fault detection on test phase are presented in Table 4. Sequentially, detectable changes of above residuals yield to analytical symptoms using to train classifier network. Generally speaking, the symptom patterns can be mapped into fault indication space by means of a static MLP network. Training of the MLP classifier was performed by employing well-known Levenberg–Marquardt (LM) method and the number of neurons ranged from 1 to 40 in each hidden layer and optimal neuron numbers as well as optimal epoch number were determined.
TABLE 1. NEURON NUMBER AND MSE FOR EACH MODEL
TABLE 2. NUMBER OF DYNAMICS FOR INPUTS AND OUTPUTS.
TABLE 3. NEURON NUMBER AND MSE FOR EACH MODEL
TABLE 4. NEURON NUMBER AND MSE FOR EACH MODEL
The signature table for the four faults is represented by Table 5.The entries” + “ , “� “ illustrate the increasing and decreasing manner of residuals and also denote that residuals (R) are non-zero and ” 0 “ entries imply that residuals are close to zero. According to fault signatures (i.e. each column) obtained in terms of pre-set thresholds all faults could be isolable.
TABLE 5. FAULT SIGNATURE TABLE.
Figure 13.Thermocouple sensor fault generated residual using Toc (A), and the fault indicator function (B).
For training of MLP network as a fault classifier, the training set was formed using 600 samples per each faulty situation, then the size of the training set was equal to 2400. After training procedure, the classifier was tested with different patterns from each faulty class. The MLP classifier with 25 hyperbolic tangent neurons in the first hidden layer, 15 hyperbolic tangent neurons in the second hidden layer, and two sigmoidal output neurons was trained for 300 epochs employing LM algorithm. Table 6 summarizes the performance of fault isolation on test data set using a mnemonic device called classification matrix in the form of a four-by-four matrix. Diagonal elements of matrix shows the effectiveness of fault classifier and the off-diagonal values shows the misclassification rates. It is worthy to note that misrecognizing can be caused by the fact that some
Model Neuron Epoch MSE Train Validation
MLP__Qoc 6 150 4 e � 6 6.5 e � 4 MLP__Toc 7 150 1.6 e � 6 3 e � 3 MLP__Pocc 7 150 8 e � 6 1.2 e � 4 MLP__Pccb 6 150 4.9 e � 6 4.2 e – 4
classes of faults are closely arranged in the symptom space or even slightly overlap each other especially owing to the noise uncontrolled effects. Such a situation can be inspect e.g. for the classes f1 and f4. However, in general terms, the achieved results of fault isolation are satisfactory.
TABLE6. PERFORMANCE INDICES FOR FAULT ISOLATION [R%]
with other comparable FDI contributions on this gas turbine benchmark, [10], [11], [13] are considered.
In [13], neuro-fuzzy approach was employed to detect and isolate only f4 from f1 among four possible faults occurring in this gas turbine, while our work presents entire results for all four possible faulty scenarios that could be one of the benefits of our work. However by comparison of the isolation results of that neuro-fuzzy FDI method with our proposed FDI technique, it can be concluded that the same accuracy performance was achieved in both work, and also correspondent models performances achieved by dynamic system identification in our work is almost equal to those presented in [13]. Additionally, obtained fault signatures in this neuro-fuzzy FDI method was quite similar to those signatures were achieved in our work. Authors in [10] and [11] used linear dynamic system identification to propose an observer based FDI method. The model performances are almost less than those achieved in our work in validation data sets that could be due to linear modelling. All possible faulty cases in gas turbine are considered in both [10] and [11] the same as our work and in contrary to above works, and also all the faults are modelled as ramp functions quite similar to our work. By comparing detection delays, in our work earlier detection times were obtained for all faulty cases i.e. f1, f2, f3, f4. However, how to set the threshold is a significant factor which leads to early or late detection. Moreover, similar fault signatures in both works were also achieved.
Remark 2-- In the case of FDI performance evaluation it must be considered that due to gradual development of incipient faults over a long period of time in real industrial applications and also for immoderately long simulation avoidance, the fault ramping rate was enlarged for the sake of significant faulty behaviour appearance after shorter periods of time.
CONCLUSION A model based fault detection and isolation method
for nonlinear systems was presented and timely fault diagnosis of an industrial gas turbine engine working on
different operating points was achieved. Both approximation and classification capabilities of well-known multi-layer perceptron neural network were exploited to accomplish fault detection and isolation task.Comparative studies show the pros and cons of our proposed FDI method in detection and isolation of all possible faulty scenarios of considered gas turbine prototype. This FDI algorithm could be also developed to exploit troupe of classifiers for isolating multiple faults that could be an open area of future research contribution. Threshold adaptation also could be an effective way for early detection of incipient faults that also can be another future contribution.
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