Neural Comput & Applic (2014) 24:1929–1941 DOI 10.1007/s00521-013-1429-4
ORIGINAL ARTICLE
Fault detection and measurements correction for multiple sensors using a modified autoassociative neural network Javier Reyes • Marley Vellasco • Ricardo Tanscheit
Received: 26 July 2012 / Accepted: 20 May 2013 / Published online: 20 June 2013 Ó Springer-Verlag London 2013
Abstract Periodic manual calibrations ensure that an instrument will operate correctly for a given period of time, but they do not assure that a faulty instrument will remain calibrated for other periods. In addition, sometimes such calibrations are even unnecessary. In industrial plants, the analysis of signals provided by process monitoring sensors is a difficult task due to the high dimensionality of the data. A strategy for online monitoring and correction of multiple sensors measurements is therefore required. Thus, this work proposes the use of autoassociative neural networks, trained with a modified robust method, in an online monitoring system for fault detection and self-correction of measurements generated by a large number of sensors. Unlike the existing models, the proposed system aims at using only one neural network to reconstruct faulty sensor signals. The model is evaluated with the use of a database containing measurements collected by industrial sensors that monitor and are used in the control of an internal combustion engine installed in a mining truck. Results show that the proposed model is able to map and correct faulty sensor signals and achieve low error rates. Keywords Sensors Calibration Fault detection Autoassociative neural networks Signal monitoring system
J. Reyes M. Vellasco (&) R. Tanscheit Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro, Rua Marqueˆs de Sa˜o Vicente, 225, Rio de Janeiro, RJ 22.452-900, Brazil e-mail:
[email protected] J. Reyes e-mail:
[email protected] R. Tanscheit e-mail:
[email protected]
1 Introduction Automation processes used in large industrial companies in areas such as oil, mining, gas, paper and cellulose, and water [3, 5, 6, 22, 24], involve a permanent search for the optimization of control processes and monitoring systems associated with them. Instrument selection, distribution, installation, and control are considered to play an important and key role in a company’s engineering operations. The existing fieldbus protocols, also known as industrial network protocols (Profibus, Modbus, Hart, ASI), allow for better communication and interaction between operators and engineers and the field equipment. As a result, reliable measurements are obtained as well as information regarding possible system failures [29]. However, it is not unusual for a sensor, and sometimes the most critical one, to have a degradation that is overlooked by the operator and for this reason be the cause of an undesired shutdown of the production process [2]. The last few decades have witnessed the development of technologies for monitoring industrial process conditions during plant operations [20]. To this end, industries have been seeking to replace periodic maintenance by conditionbased maintenance strategies as a means by which to obtain a potentially more efficient online method. Of special interest are the techniques for controlling the status of sensors and their associated instrumentation circuits. Commonly known as online monitoring methods, such techniques are developed for the purpose of monitoring industrial sensors and for correcting measurement data collected from a single sensor [12, 31, 32]. Sensor reliability is one of the problems commonly encountered by designers of computer-based systems that are expected to monitor the performance of a dynamic process in order to ensure that the operation is within the
123
1930
limits imposed. Since the sensor output is the only objective source of information for decision making, it is essential that the operating status of the sensors be known. Intelligent sensors are able to send alert messages to the controller when there is a connection failure, a drop in voltage, or a rise in ambient temperature, but they do not send messages about their physical degradation, a condition that may lead to an imprecise measurement of the quantity involved. The problems of sensor validation and faulty sensor replacement must be considered as an integral part of the design of a modern monitoring, diagnostic, or control system [29]. Manual calibrations are performed periodically in order to validate that an instrument is operating correctly. However, depending on the frequency of calibration, faulty instruments may remain unnoticed over long periods of time. On the other hand, manual calibration strategies for instruments that are functioning properly are not only unnecessary, but also generate premature aging of their components. Failure to comply with calibration programs may have unfavorable effects on the process and, as a result, may affect the product quality, causing economic losses and, in many cases, accidents due to imprecise feedback control signals [11]. Computational intelligence techniques have been used in the development of fault detection methods, as, for example, the methodology described in [9, 13, 28]. This methodology, in which computational models are designed for the purpose of predicting real system outputs, is unsuitable for performing sensor diagnosis because it is based on correct input data (measurements via sensors) and assumes that the inputs into the real system and into the model are fault-free. When there is a noticeable difference between the output of the real system and the output of the model, one assumes that there is a problem in the real system. The focus in the case of sensor monitoring and diagnosis is different; that is to say, the goal is to find the malfunctioning sensors (system inputs) that have caused such problems [21]. Traditional fault monitoring and diagnosis systems make use of what is known as hardware redundancy, where more than one sensor performs the same measurement. Although this technique is still being used because it obtains reliable results [28], it presents a few disadvantages, such as the high cost of the greater number of sensors required or the space needed for their installation. There is also the so-called software redundancy technique, which is used to estimate signals by means of a mathematical model. The main advantage of this latter method is that it does not involve the addition of components to the existing system and, as a result, both the cost and the installation space are minimized.
123
Neural Comput & Applic (2014) 24:1929–1941
Projects related to field monitoring instruments first appeared in Nuclear Energy plants in the United States [7, 30]. Research has also focused on the development of selfcalibration and self-validation models with the use of techniques such as fuzzy logic [19] and neural networks [10], which were applied to the monitoring of a single sensor or to the monitoring of a plant [4, 8, 23]. The use of these models in online performance monitoring would generate more complexity than desired, since it would be necessary to design a system that comprised a distinct model for each sensor associated with a same process. The motivation of this work was to develop a model that would be able to perform online monitoring and self-correction of multiple sensor measurements in order to reduce maintenance costs, minimize the risk of using uncalibrated or faulty sensors, increase instrument reliability, and consequently reduce equipment inactivity. The concept of the model proposed in this work was based on a study of the characteristics desired in a system to be used for self-correction and diagnosis of sensors that operate within a continuous process. The autoassociative neural network architecture [14] was chosen because it allows the filtering of measurement noise, the self-correction of faulty signals due to sensor drift faults—a type of error defined as a low rate of change in a signal’s expected value over time—and the treatment of data related to gross faults—defined as drastic sensor failures. This work contains five additional sections. Section 2 presents the architecture proposed to provide the autoassociative neural network (AANN) with the ability to selfcorrect sensor measurements and includes a description of the robust training method for the AANN. Section 3 presents the model for online monitoring of process instruments, which is based on a self-correction module trained with a modified training method for the AANN (MAANN). Section 4 presents the case study, which is based on industrial sensors that monitor and are used in the control of an internal combustion engine of a mining truck. The large number of input variables (sensor measurements) in this application makes it possible to evaluate the model proposed. Lastly, Sect. 5 concludes this work.
2 Autoassociative neural network architecture Autoassociative neural networks are inspired by the nonlinear principal component analysis (NLPCA) methodology [14, 27]. In simplified terms, in this method, the system input data are mapped to the output data by means of a nonlinear function G. The reconstruction of the original data is carried out by a ‘‘demapping’’ function expressed by a nonlinear function H. Functions G and H are selected so
Neural Comput & Applic (2014) 24:1929–1941
as to minimize information loss during the mapping– demapping process. Since this process is represented, in this case, by a neural network, it is important to bear in mind that neural networks can implement a wide range of nonlinear functions. This characteristic can be used to build a network for extracting nonlinear principal components [27]. The architecture in question makes use of two serially connected neural networks responsible for the implementation of the mapping function, G, and of the demapping function, H, as shown in Fig. 1. The process measurements form the neural network input vector X; as a result, the dimension m of the input layer corresponds to the dimension of the measurement vector. The output layer produces a reconstructed data vector of same dimension as the input layer. The first hidden layer represents the mapping function G, and the number of neurons it contains is greater than the number of inputs. The second hidden layer represents the bottleneck layer T, whose dimension is smaller than that of the input layer. The third hidden layer represents the demapping layer H, whose dimension is equal to that of the mapping layer. It should be noted that the hidden layer T is the projection of the input vector onto the feature space. In this network, the weights are updated for each sample so that at the end of the process, the distribution of weights can form an identity matrix that characterizes the neural network. In other words, one seeks to optimize the network topology so as to reconstruct the m input variables with the highest possible precision, for which purpose a small set of neurons is used in the bottleneck layer. The bottleneck layer plays a key role in the functionality of an AANN because it forces the input data to be encoded and compressed and subsequently decoded or decompressed. Possible hidden correlations existing in the data are captured by the bottleneck layer and the effects of noncorrelation—produced by signal noise, for example—are excluded in this layer [14]. If the network is trained with suitable data, the output for new data drawn from the same
1931
distribution as that of the training set will be as close as possible to the noise-free state. There is no available method for determining the number of neurons needed in the hidden layers of the AANN model. As for the mapping and demapping layers, the dimension to be adopted must be large enough to ensure that the network will be able to retrieve the input signals without the risk of overfitting. The appropriate number of neurons in these layers can also be chosen by means of cross-validation [15, 16, 17]. 2.1 Robust training A robust training method [15] is an important tool for solving problems associated with abrupt errors (drift and offset) and noise in the measured values. The neural network is supplied with a set of data in which some randomly drawn patterns are corrupted such that it is forced to obtain the correct results (without disturbances). The patterns to be corrupted are selected from a training set with a uniform probability distribution and each corruption consists of changing a randomly selected input from among the samples while all the other input values remain correct [17]. Let us consider that an input variable X ¼ ½x1 ; x2 ; . . .; xn contains n samples and that a random noise d is added to one sample j, such that the new input X ¼ x1 ; x2 ; . . .; xj þ d; xjþ1 ; . . .; xn is created; the AANN is forced to provide the original data of X as its output. This procedure is repeated several times for the m inputs to the neural network, in such a way that all the samples in the training set are corrupted on several different occasions. The goal of the learning algorithm is to tune the neural network so that its responses may be close to the desired values whenever noise or abrupt errors are detected in the input signals. A network thus trained is able to correct measurements when only one sensor has failed; in case there are other faulty sensors, it will be unable to correct the errors in a suitable manner. Approaches that seek to overcome this deficiency [1, 18, 21, 26] make use of more complex or computationally expensive structures that are not appropriate for online applications. The model proposed in this work uses only one neural network to reconstruct the faulty sensor signals without the help of additional algorithms or of another network, thus simplifying the training process.
3 Monitoring model
Fig. 1 Autoassociative neural network
Monitoring models can be divided into two major categories: redundant models and non-redundant models. In redundant models, a set of sensors verifies the same process operation. The average of the several different sensor
123
1932
Neural Comput & Applic (2014) 24:1929–1941
to all the inputs by means of a large number of weights and patterns. The degree of coherence between parameters i and j is determined by the correlation function Cði; jÞ Sði; jÞ ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Cði; iÞ Cðj; jÞ
Fig. 2 Online monitoring model
readings can be used, for example, to obtain the result. By contrast, the non-redundant modeling techniques are based on the estimates of correlated sensor measurements [11]. In the case of non-redundant models, the selection of the sensors that will be included as inputs is very important. Models built with groups of highly correlated sensors generate fewer errors in the predicted measurements when compared with models that have low correlations in the input vectors [12, 14, 21]. Figure 2 shows a block diagram of the model for an online monitoring system. The sensor measurement vector X is the input to the self-correction model, which in turn calculates the best estimate of the input vector. The objective of the self-correction model is to repair the measurements from a set of sensors when their performance is degraded due to improper sensor installation, aging or deformation as a result of exposure to unsuitable ambient conditions. The estimated vector provided by the self-correction model can be compared with the input vector, creating a vector of residuals r (see Fig. 2). This vector of residuals can then be used to evaluate the status of each sensor. The main element of the self-correction model is the AANN, which is used to reconstruct the signals. As mentioned in Sect. 2, the AANN plays the role of an identity matrix when the measurements are fault-free, and of a nonlinear function that is able to reconstruct signals when any degradation is detected in them. Thus, the model developed in this work is considered non-redundant because it uses historical data from the sensor operations and covers the entire range of process operations. The development of the self-correction model comprises the following steps. 3.1 Step 1: sensor correlation In this stage, a correlation analysis is performed on the sensor measurement data. The degree of correlation between the variables is a significant aspect when an AANN is to be used as a monitoring system [14, 31]. In situations where the correlation is high, drift, offset, or noise in one of the sensors does not have significant effects on the response of the AANN because its output is related
123
where C represents the covariance matrix. Values close to ±1 represent a high degree of correlation in terms of the sensor working ranges. It was observed in this study that although all the sensors were monitoring the same plant, they did not always show a high degree of correlation. When there is a large number of sensors and correlations are not uniform, it is proposed in this work that the sensor set be subdivided into smaller groups with similar correlations such that better signal approximations can be obtained. It is common to establish the groups on the basis of physical correlation, engineering sense selection, or selection based on the physical proximity of the sensors. The correlation analysis we have performed has actually confirmed what a physical selection—based on the regions where the sensors are installed—would suggest: three groups. The thresholds for the three groups where defined in an empirical manner: low correlation—below 0.4, medium correlation—between 0.4 and 0.8, and high correlation—above 0.8. 3.2 Step 2: data preprocessing Once the sensor subgroups have been identified, the database must be cleaned: inconsistent or incomplete data are removed before training the neural network. In this work, outliers were evaluated through box plots for every sensor (they were mostly observed in the exhaust port temperature sensors). After that, data normalization is performed. In the case of this work, in which the activation function of the hidden layer neurons is of the tansig (tangent sigmoid) type, measurements were mapped to a range of [-1, 1] in order to simplify the training process. 3.3 Step 3: estimating the number of neurons The complexity of an AANN is defined by the number of neurons in the bottleneck layer [11, 14]. Although the number of neurons in the mapping and demapping layers affects the neural network performance, the number of neurons in the bottleneck layer has a more significant effect on the quality of the response. Distinct topologies are trained and tested in order to collect the information that will be used to evaluate the number of neurons required to provide the AANN with the ability to correct a sensor’s measurements. The network must be trained with a database containing disturbed data, as explained in Sect. 2.1. The number of neurons in the mapping
Neural Comput & Applic (2014) 24:1929–1941
1933
Fig. 3 Self-correction model
Table 1 Temperature sensors in the cylinders—Group 1
0.8
Group 1 sensors
0.7
5 LB cylinder exhaust T
0.6
1 RB cylinder exhaust T
5 RB cylinder exhaust T
0.5
2 LB cylinder exhaust T
6 LB cylinder exhaust T
2 RB cylinder exhaust T
6 RB cylinder exhaust T
3 LB cylinder exhaust T
7 LB cylinder exhaust T
3 RB cylinder exhaust T
7 RB cylinder exhaust T
0.2
4 LB cylinder exhaust T
8 LB cylinder exhaust T
0.1
4 RB cylinder exhaust T
8 RB cylinder exhaust T
MAPE
1 LB cylinder exhaust T
M=18 M=19 M=20 M=21 M=22
0.4 0.3
0
0
2
4
6
8
10
12
14
Bottleneck
and demapping layers is then selected with the aim that the desired response can be obtained under noise-free conditions (a larger number of neurons generally provide better filtering results). The network will then be able to reconstruct a sensor signal with the help of the existing correlation with the signals from other sensors. As opposed to prior studies, this work has evaluated the neural network’s response to each reconstructed signal. In the training process, the mean square error (MSE) criterion is employed. The decision on the best model is made via cross-validation [10], based on the value of the mean average percentage error (MAPE). At the testing phase, the root mean square error (RMSE) is used to evaluate the model’s performance. 3.4 Step 4: modified robust method (M-AANN) The aim of this step is to provide the network with the ability to reconstruct signals that present abrupt errors at the same time. The robust training method proposed by Kramer makes use of one or more autoassociative neural networks to reconstruct measurement data resulting from faulty sensors, provided the faults are not simultaneous [3, 12, 17, 25]. The robust AANN model is able to reconstruct sensor measurements when a single sensor in the group has
Fig. 4 MAPE (Group 1) as a function of the number of neurons in the bottleneck layer
failed, but in the reconstruction of the faulty signal, there are deviations in the outputs of the fault-free sensors. With the objective of improving the response of the AANN in the presence of multiple faults and of developing a method that can be employed in real systems, this work proposes a modification of the robust training method. Let us consider a training set X ¼ ½X1 x n ; X2 x n ; . . .; Xm x n , where Xi x n represents the vector of n samples from sensor i. By inserting noise only in the n samples of sensor i, for example, it is possible to observe at the output of the neural network how the reconstruction of the signals from all the sensors is carried out. Assuming that the measurements from two other sensors (i ? 2 and i ? 4, for example) initially unaffected by noise have not been reconstructed satisfactorily, random noise is added, at the input of the network already trained, to the n samples measured by those two sensors. Thus, the network is trained again and its output is evaluated, in the search for inconsistent reconstructions. This procedure is repeated for two other sensors until the data of all the sensors, always in groups of two, have been presented to the network in a corrupted
123
1934
form. The name given to the network trained in this way is modified autoassociative neural network (M-AANN).
Neural Comput & Applic (2014) 24:1929–1941
4.1 Test with the self-correction model 4.1.1 Group 1
4 Case studies The case study was chosen in order to show that the M-AANN is able to perform online reconstruction of sensor measurements affected by drifts, offsets, and noise. It may be recalled that the model proposed belongs to the non-redundant category, which is effective for monitoring industrial level sensors and appropriate for the evaluation of sensor status over time. The model was evaluated with the use of a database containing measurements from sensors that control and monitor an internal combustion engine coupled with an alternator that generates the power required to feed two electric motors responsible for the rear wheel traction of a mining truck owned by the Barrick Company in Peru. The first stage of this work was to confirm that the sensors had previously been calibrated in order that uncalibrated simulations could be performed. The values in the database provided had previously been filtered by the engine control module (ECM) during the signal acquisition stage. Therefore, the measurements that showed abrupt variations were considered as being related to the engine operation rather than to external noise. Among the 40 measurement variables provided, 32 were selected, corresponding to the measurements taken by distinct pressure and temperature sensors. The variables corresponding to calculations performed by the ECM, to pulse width modulation (PWM) measurements, and to sensors that delivered ON–OFF type outputs were disregarded. The data represent measurements from a truck when transporting products. The truck monitoring system generated a database with 2,000 samples and the measurements were based on a sampling period of 1 min for each sensor. Differently from other studies, in this work, the signals produced by the sensors are not ideal, that is, they vary according to the engine’s effort during the transport of the material. Of the 2,000 samples, 800 were used for the training phase, 200 for validation, and 1,000 for the testing phase. In order to avoid overfitting, the early-stopping technique was used to train all the neural networks tested. Based on the methodology described in Sect. 3, the measurement data were analyzed for the purpose of identifying groups of sensors with similar correlations. The sensors were divided into three groups, and a modified autoassociative neural network (M-AANN) was associated with each one of these groups. Figure 3 presents a block diagram of the proposed self-correction model. The tests performed with each of the three groups are detailed below.
123
The sensors selected for this group, presented in Table 1, measure the temperature in the internal combustion chamber of the eight cylinders of the engine and showed a correlation above 0.87. In the course of developing an algorithm capable of searching for the smallest number of neurons in the bottleneck layer, distinct M-AANN topologies were tested by varying the number of neurons in the hidden layers and introducing noise into the sensor measurements. Based on the errors obtained in the validation phase, the network having the highest generalization capability was selected. At the end of training, tests were carried out in order to ensure that the network was able to behave as a filter—or measurement corrector—in the presence of noisy measurements. Figure 4 summarizes the results achieved during the training phase for the different topologies selected. It shows the M-AANN behavior as a function of the number of neurons in the bottleneck layer (from 1 to 14), considering the different numbers of neurons in the mapping and demapping layers (identified by M in the upper right-hand box). It can be observed that regardless of the number of neurons in the mapping and demapping layers, the network produces a smaller prediction error when the number of neurons in the bottleneck layer reaches a value close to that of the number of inputs (16 in this case, as presented in
Table 2 Results without disturbance Group 1
16-18-6-18-16
Sensor
MSE
MAPE (%)
RMSE
1
0.000015
0.039246
0.761756
2
0.000103
0.166627
2.584214
3
0.000201
0.224396
3.030418
4
0.00011
0.152232
2.241799
5
0.00014
0.182628
2.601988
6
0.00002
0.051776
1.03355
7
0.000192
0.193579
2.62752
8
0.00017
0.202299
2.808072
9
0.00034
0.318357
4.39567
10
0.000201
0.237755
3.387114
11 12
0.000218 0.000152
0.242586 0.202992
3.681684 2.960583
13
0.00028
0.251261
3.614427
14
0.000189
0.246338
3.688612
15
0.000266
0.249533
3.454879
16
0.000195
0.241156
3.452701
Neural Comput & Applic (2014) 24:1929–1941
1935 1 LB cylinder exhaust temperature
1000
ºF
900
Calibrated 800
M-AANN Faulty
700 300
320
340
360
380
400
420
440
460
480
Time (min) 1 RB cylinder exhaust temperature 1200
ºF
1100 1000
Calibrated 900
M-AANN 320
340
360
380
400
420
440
460
480
Time (min)
Fig. 5 Responses of sensors 1 and 2 to an overall deviation of 100 °F for sensor 1 (1 LB cylinder exhaust temperature) Table 3 Drift of 0.7 % per minute in sensor 1
Table 4 Deviation of 100 units for sensors 1, 5, and 11
Group 2
16-18-6-18-16
Group 1
16-18-6-18-16
Sensor
MSE
MAPE (%)
RMSE
Sensor
MSE
MAPE (%)
RMSE
1
0.0017
0.6233
8.0308
1
0.0019
0.655
6.4436
2
0.0002
0.2131
3.2284
2
0.0002
0.255
3.7725
3 4
0.0002 0.0001
0.2208 0.153
3.0248 2.2289
3 4
0.0002 0.0004
0.2366 0.2622
3.245 4.01
5
0.0003
0.2775
3.7958
5
0.0021
0.7299
7.9174
6
0
0.0466
0.8067
6
0
0.088
1.4911
7
0.0002
0.1864
2.5327
7
0.0006
0.3436
4.4874
8
0.0002
0.2093
2.8926
8
0.0003
0.2727
3.7162
9
0.0003
0.3172
4.315
9
0.0005
0.3862
5.2263
10
0.0005
0.3882
5.3334
10
0.0008
0.4989
6.6872
11
0.0002
0.2481
3.7299
11
0.0003
0.2991
4.5121
12
0.0004
0.337
4.5454
12
0.0006
0.4446
6.0273
13
0.0002
0.2357
3.3597
13
0.0003
0.2389
3.6136
14
0.0002
0.2335
3.4417
14
0.0004
0.4016
5.688
15
0.0005
0.3425
4.541
15
0.001
0.5091
6.5595
16
0.0002
0.2586
3.8134
16
0.0006
0.4262
6.1295
Italic values indicate the sensor that has suffered the influence of a drift or are faulty
Italic values indicate the sensors that have suffered the influence of a drift or are faulty
Table 1). Although the network copes better with sensor disturbances when the bottleneck layer contains a greater number of neurons, the computational effort required to calculate the weights in the network is higher. Selecting the smallest number of neurons in this layer such that a good approximation of the output (without disturbance) can be obtained is essential toward reducing the computational cost and achieving a good generalization. In most of the topologies tested, when the bottleneck layer contained
more than six neurons, the prediction error value remained practically the same, in other words, the variation was insignificant. Therefore, 6 neurons were chosen for this layer. The next step was to train the M-AANN to reconstruct measurements when noise, drifts, or offsets were detected in their corresponding input vectors. The proposed modified robust method (Sect. 3.1) was then used, where random noise corresponded to 10 % of the measurement
123
1936
Neural Comput & Applic (2014) 24:1929–1941 6 LB cylinder exhaust temperature
ºF
1200
1000
Calibrated M-AANN
800
Faulty 50
100
150
200
250
300
350
400
450
Time (min) 6 RB cylinder exhaust temperature 1200 1100
ºF
1000 900
Calibrated
800
Faulty 50
100
150
200
250
300
350
400
450
Time (min)
Fig. 6 Sensor response to a low drift of 0.7 % per minute in sensors 1, 5, and 11 Table 6 Response of the M-AANN for Group 2
1 LB cylinder exhaust temperature 1200
7-15-3-15-7
Sensor
MSE
MAPE
RMSE
1000
1 2
0.006 0.0056
0.5881 0.5901
0.3075 0.2907
900
3
0.0019
0.4154
0.8958
4
0.0077
0.3233
0.6789
5
0.0037
0.6845
1.4887
6
0.0033
0.4406
0.8563
7
0.0087
0.5917
0.3823
ºF
Group 2 1100
800
Calibrated Faulty M-AANN
700
600
260
270
280
290
300
310
320
330
340
350
Time (min)
Fig. 7 Response of M-AANN1 to noise in sensor 1 Table 5 Temperature and pressure sensors—Group 2 No.
Group 2 sensors
1
Air pressure at the high left-side turbo outlet
2
Air pressure at the high right-side turbo outlet
3
Air temperature in the front left-side aftercooler
4
Air temperature in the rear left-side aftercooler
5
Air temperature in the front right-side aftercooler
6
Air temperature in the rear right-side aftercooler
7
Engine coolant pressure
range. The number of neurons in the mapping and demapping layers was increased until a response with a maximum MAPE error of 2 % was obtained. This limit falls below what is normally deemed as acceptable in real
123
applications, which, in general, consider smaller operation ranges than the ones used in our work. It should be added that results shown in previous works employing AANNs have been evaluated only graphically, without mention to any error at all. At the end of training, the topology obtained presented 18 neurons in the mapping/demapping layer. Table 2 shows the results obtained with the 16-18-6-18-16 topology. It may be observed that the MAPE is smaller than 1 % for all the sensors; hence, the objective proposed has been achieved. The network was tested for its ability to reconstruct signals in the presence of abrupt faults. Input vectors were presented with a drift of 0.7 % per minute (5 °F) during 500 min (at the end of the process, the deviation is 100 °F). Figure 5 presents the result provided by the M-AANN1 in response to the drift in sensor 1 as well as the behavior of sensor 2 in the same group; Table 3 presents the results for the 16 sensors.
Neural Comput & Applic (2014) 24:1929–1941
1937 Pressure at the high left-side turbo outlet
38
Objective M-AANN
psi
36 34 32 30 28 210
220
230
240
250
260
270
280
290
300
310
Time (min) Pressure at the high right-side turbo outlet 36
Objective Offset M-AANN
psi
34 32 30 28 26 120
140
160
180
200
220
Time (min)
Fig. 8 Responses of sensors 1 and 2 in M-AANN2 in the presence of a fault in sensor 2 (offset = 4 psi) Table 7 Response of M-AANN2 to a fault in sensor 2 (offset = 4 units) Group 2
7-15-3-15-7
Sensor
MSE
MAPE
RMSE
Table 9 Temperature and pressure sensors—Group 3 No.
Group 3 sensors
1
Rail fuel pressure
2
Oil temperature Oil pressure at filter inlet
1
0.0077
0.924
0.4278
3
2 3
0.0035 0.0084
0.602 0.8603
0.284 1.6134
4
Oil pressure at filter outlet
5
Differential pressure due to oil filters
4
0.0127
0.3595
0.7217
6
Crankcase gas pressure
5
0.0036
0.5663
1.2685
7
Engine coolant temperature
0.911
8
Compressor air inlet temperature
0.2361
9
Temperature of the ECM for the engine
6 7
0.0058 0.0048
0.439 0.4726
Italic values indicate the sensor that has suffered the influence of a drift or are faulty
Table 10 Results for M-AANN3 Group 3
9-15-7-15-9
Sensor
MSE (-6 e)
MAPE
RMSE
Table 8 Response of M-AANN2 to faults in sensors 2, 4, 5, 6, and 7 (offset = 4 units)
1
0.219
0.0165
0.0329
Group 2
7-15-3-15-7
2
0.2078
0.0745
0.0068
Sensor
MSE
MAPE
RMSE
3
0.1389
0.2133
0.1666
4
0.1234
0.2492
0.1816
1
0.0089
1.7353
0.7615
5
0.2568
0.0748
0.0046
2
0.005
1.2726
0.5651
6
0.0506
0.0425
0.0937
3
0.0015
0.3847
0.83
7
0.3123
0.0378
0.0840
4
0.01
0.4628
0.85
8
0.2808
0.4182
0.2338
5
0.0044
0.8208
1.694
9
0.1456
0.0343
0.0560
6
0.0065
0.6704
1.2708
7
0.004
0.6408
0.2405
Italic values indicate the sensors that have suffered the influence of a drift or are faulty
The results show that the MAPE for sensor 1 (1 LB cylinder exhaust temperature) has increased from 0.03 % to approximately 0.6 %, which is below the error limit
123
1938
Neural Comput & Applic (2014) 24:1929–1941
Fig. 9 Responses of M-AANN3 in the presence of a fault in sensor 1 (offset = 4 psi)
Rail fuel pressure 165 160
psi
155 150 145 Calibrated Faulty M-AANN
140 135 130
0
20
40
60
80
100
120
Time (min)
Table 11 Response of M-AANN3 to the fault in sensor 1 Group 3
9-15-7-15-9
Sensor
MSE
MAPE
RMSE
1
0.0000
0.0122
0.0357
2
0.0000
0.0578
0.0055
3
0.0001
0.1696
0.1647
4
0.0002
0.2210
0.2087
5 6
0.0000 0.0000
0.0537 0.0582
0.0039 0.1489
7
0.0000
0.0285
0.0699
8
0.0003
0.2501
0.2011
9
0.0000
0.0707
0.1410
Italic values indicate the sensor that has suffered the influence of a drift or are faulty
ECM Temperature 160 Calibrated Faulty M-AANN
150 140
ºF
130 120 110 100 90 80 70 60 500
550
600
650
700
750
800
850
900
950
1000
Time (min)
(a) ECM Temperature 160 Calibrated Faulty M-AANN
150
established for the set of sensors (2 %). In addition, the mean of the difference between the response of the M-AANN1 for the 15 remaining sensors when the measurements have not been disturbed and when there is a drift in the output of sensor 1 is 0.21–0.24 %. In other words, the network does not generate significant changes in the responses of the sensors whose input values are fault-free. Next, the network was tested for its ability to correct failures in three or more faulty sensors. Data corresponding to the measurements from three randomly selected faulty sensors were presented to the M-AANN1 (the drift rate was identical to the one in the previous test). The results for all sensors have been summarized in Table 4, where sensor 1 (1 LB cylinder exhaust temperature), sensor 5 (3 LB cylinder exhaust temperature), and sensor 11 (6 LB cylinder exhaust temperature) are considered to have failed. Figure 6 presents the response of the M-AANN1 for sensors 1 and 2, with regard to drifts in sensors 1, 5, and 11. It may be noticed that the M-AANN1 is able to perform selfcorrection for the measurements affected by sensor drifts and that a MAPE value of less than 1 % was obtained. By comparing the values in Tables 3 and 4, it is possible to observe that the maximum difference between these two cases occurred in sensor 5—the MAPE increased from 0.277 to 0.729 %. Lastly, the M-AANN1 was tested with random noise corresponding to 20 % of the measurement range. As an example, a normally distributed random noise and a standard deviation equivalent to 40 °F were added to the measurements from sensor 1 (1 LB cylinder exhaust temperature). Figure 7 shows that the M-AANN1 is able to filter the noise satisfactorily.
140
ºF
130
4.1.2 Group 2
120 110 100 90 80 730
740
750
760
770
780
790
800
810
Time (min)
(b) Fig. 10 Responses of M-AANN3 to a drift fault in sensor 9 (final deviation = 10 °F)
123
The sensors in Group 2 (Table 5) were found to have a correlation of approximately 0.6. In keeping with the methodology proposed in Sect. 3, first, distinct topologies were tested for the purpose of obtaining the best number of neurons in the bottleneck layer, always with a view to the filtering of noisy data and the self-correction of measurements produced by one faulty sensor. It was considered that there would be 9 to 16
Neural Comput & Applic (2014) 24:1929–1941
1939
Table 12 Response of M-AANN3 to a drift in sensor 9 Group 3
9-15-7-15-9
Sensor
MSE (e-3)
MAPE
RMSE
1
0
0.0119
0.0223
2
0
0.0408
0.004
3
0.0001
0.201
0.1591
4
0.0001
0.2364
0.178
5
0
0.0338
0.002
6
0
0.0382
0.091
7 8
0 0
0.0202 0.1382
0.0452 0.0884
9
0.0018
0.9291
1.5752
Italic values indicate the sensor that has suffered the influence of a drift or are faulty
neurons in the mapping and demapping layers and 1 to 6 neurons in the bottleneck layer. It may be recalled that the number of neurons in this layer is chosen based on the computational cost, on the error variation due to the larger number of neurons and on the network’s ability to correct the inputs when they contain errors. Next, the proposed modified training method was used, in which errors equivalent to 20 % of the sensor measurement range were inserted in the input vectors. The number of neurons in the mapping and demapping layers was increased until a response with a maximum MAPE error of 2 % was obtained. At the end of the training process, the topology obtained presented 15 neurons in the mapping and demapping layer and 3 neurons in the bottleneck layer. Table 6
shows the results obtained with the 7-15-3-15-7 topology of the M-AANN2 for the 7 sensors. As can be observed, the MAPE is smaller than 1 % for all the sensors. The next step was to test the network’s ability to reconstruct signals upon the occurrence of a fault in a single sensor. To this end, offset errors were introduced into the sensor measurements presented to the network. Figure 8 presents the outputs of the M-AANN2 for sensors 1 (pressure at the high left-side turbo outlet) and 2 (pressure at the high rightside turbo outlet), where sensor 2 was considered to have an offset of 4 psi. Table 7 summarizes the results. Lastly, the M-AANN2 was tested for its ability to deal with multiple failures caused by offset errors. The measurements from sensors 2, 4, 5, 6, and 7 (selected randomly) were corrupted by the addition of an offset of 4 units at the same time. Table 8 summarizes the results. By comparing the results obtained in the tests for single sensor faults for multiple sensor faults, it can be observed that the M-AANN2 is able to reconstruct the sensor signals with a resulting MAPE of less than 2 %. It can also be noticed that the mean of the difference between this case and the one in which the sensor signals are undisturbed has increased from 0.51 to 0.85 % (MAPE). Therefore, the errors are within the limits established for the sensor set analyzed herein. 4.1.3 Group 3 The sensors in this group are presented in Table 9; their mean correlations were 0.33. Since there are sensors with Pre-Filter oil pressure 100
160
95
155
90
150
85
psi
psi
Rail fuel pressure 165
145
80
140
75
Objective M-AANN Offset
135 130 0
20
40
60
80
100
Objective M-AANN Offset
70 65
120
0
20
40
Time (min)
60
80
100
120
Time (min)
Engine coolant temperature
ECM temperature
190
150 140
180
130
ºF
ºF
120 170
110 100
Objective M-AANN Offset
160
Objective M-AANN Offset
90 80
150
70 0
20
40
60
80
100
120
0
Time (min)
20
40
60
80
100
120
Time (min)
Fig. 11 Responses of M-AANN3 to faults in sensors 1, 3, 7, and 9 (offset = 5 units)
123
1940
Neural Comput & Applic (2014) 24:1929–1941
Table 13 Responses of M-AANN3 to faults in sensors 1, 3, 7, and 9 (offset = 5 units) Group 3
9-15-7-15-9
Sensor
MSE (e-3)
MAPE
RMSE
1
0.0063
0.0118
0.0357
2
0.0058
0.052
0.0055
3 4
0.1267 0.2235
0.1596 0.2087
0.1647 0.2087
5
0.016
0.0504
0.0039
6
0.0188
0.0553
0.1489
7
0.0217
0.0277
0.0699
8
0.2588
0.2309
0.2011
9
0.0165
0.065
0.141
Italic values indicate the sensors that have suffered the influence of a drift or are faulty
correlations of less than 0.2, this group constitutes a hard test with respect to the reconstruction of signals in the presence of abrupt sensor faults. The same method was used again to train the M-AANN3. In this case, on account of the low correlation among the sensor data, the number of samples presented to the network was increased in 50 %, resulting in a data base of 1,500 measurements. The number of training cycles has been increased accordingly. The topology of the M-AANN3 is 9-15-7-15-9 and Table 10 below summarizes the results achieved. It may be pointed out that due to the low correlation among sensor measurements, the number of neurons in the bottleneck layer is greater than the number of inputs in the network when this group is compared with Groups 1 and 2. The M-AANN3 was tested using the data for sensor 1 (rail fuel pressure) in which an offset error of 4 psi was inserted during the first 125 min of operation. Figure 9 presents the behavior of sensor 1 in response to the fault, and Table 11 summarizes the results obtained. The response is satisfactory because it produced a MAPE of less than 0.02 %; the error was corrected without affecting the other measurements. The network was then tested using the data for sensor 9 (temperature of the ECM for the engine), in which a low drift of 6.84 % per minute was inserted starting at time instant 500 (at the end of the process, the deviation was 10 °F). Figure 10a presents the drift test results, and Fig. 10b shows an amplified view of this test. In Table 12, the results for the 9 sensors in the M-AANN3 are summarized. Lastly, the M-AANN3 was tested on its ability to cope with multiple faults: the measurements taken by several sensors were corrupted by the addition of an offset of 5 units at the same time. Figure 11 presents the responses of the M-AANN3 to the simultaneous faulty measurements
123
from sensors 1, 3, 7, and 9, and the results for these faults are summarized in Table 13. When comparing the results obtained in the tests performed with undisturbed measurements and in the tests in which multiple faults were taken into account, it can be observed that the M-AANN3 was able to reconstruct the sensor signals with a resulting MAPE of less than 2 %. In addition, the mean of the difference between these latter cases and those in which the sensor signals were not disturbed was 0.34 %. Therefore, the errors are within the limits established for the sensor set analyzed herein. A fundamental conclusion, which also asserts the validity of the model proposed in this work, is that the three networks proved to have excellent generalization capabilities. When they were trained using disturbed measurements for only two sensors, for example, during the testing phase, they were able to reconstruct the signals generated by all the sensors, with minimum error.
5 Conclusions This work has presented a detailed account of the proposed model for online monitoring of industrial sensors based on the use of autoassociative neural networks. The model proposes to organize sensors with similar degrees of correlation into different groups, assign a neural network to each of these groups, and have the network perform self-correction of the faulty sensor measurements. The modified robust method proposed in this work enabled the model to generalize and reconstruct measurements when the monitoring system detected simultaneous faults associated with different sensors in a same group. The model was evaluated with the use of measurements from sensors installed in a real engine. The three M-AANNs corresponding to the three groups of sensors identified in the case study proved to be able to perform self-corrections with resulting MAPE values of less than 2 %, even when there were errors of up to 100 units in the sensor measurement. The difference between the output vector, provided by the self-correction model, and the input vector read from the sensors can be used, in a future work, to evaluate the status of each sensor. These residuals can be provided to a decision model that alerts the specialist when sensors are faulty and may require another calibration.
References 1. Antory D, Irwin G, Kruger U, McCullough G (2005) Improved process monitoring using nonlinear principal component models. Int J Intell Syst 23:520–544 2. Afonso P, Ferreira J, Castro J (1998) Sensor fault detection and identification in a pilot plant under process control. Chem Eng Res Design 76:490–498
Neural Comput & Applic (2014) 24:1929–1941 3. Bo¨hme Th, Fletcher I, Cox C (1999) Reliable neuro self-tuning control using auto-associative neural networks for the water treatment. e&i Elektrotechnik und Informationstechnik 116:6 4. Bueno E, Ting D, Gonc¸alves I (2007) Development of an artificial neural network for monitoring and diagnosis of sensor fault and detection in the IEA-R1 research reactor at IPEN. In: INAC 2007—international nuclear atlantic conference, INAC 2007 DVD 5. De Miguel L, Bla´zquez L (2005) Fuzzy logic-based decisionmaking for fault diagnosis in a DC motor. Eng Appl Artif Intell 18:423–450 6. Eyng E, Silva F, Palu´ F, Fileti A (2008) Neural network based control of an absorption column in the process of bioethanol production. Braz Arch Biol Technol 52:961–972 7. Fantoni PF, Hoffmann MI, Shankar R, Davis EL (2003) On-line monitoring of instrument channel performance in nuclear power plant using PEANO. Prog Nucl Energy 43:83–89 8. Galotto L, Bose B, Leite C, Pereira J, Borges da Silva L, Lambert-Torres G (2007) Auto-associative neural networks based sensor drift compensation in indirect vector controlled drive system. In: 33rd annual conference of the IEE industrial electronics society, Taiwan 9. Garcia-Alvarez D, Fuente MJ, Vega P, Sainz G (2009) Fault detection and diagnosis using multivariate statistical techniques in a wastewater treatment plant. In: Proceedings of the 7th IFAC international symposium on advanced control of chemical processes, Turkey 10. Haykin S (1998) Neural networks: a comprehensive foundation. Macmillan College Publishing Company, New York 11. Hines J, Garvey D (2007) Process and equipment monitoring methodologies applied to sensor calibration monitoring. Qual Reliab Eng Int 23:123–135 12. Hines J, Grinok A, Attieh I, Urigh R (2000) Improved methods for on-line sensor calibration verification. In: 8th International conference on nuclear engineering, Baltimore, USA 13. Koscielny J, Syfert M (2006) Fuzzy diagnostic reasoning that takes into account the uncertainty of the relation between faults and symptoms. Int J Appl Math Comput Sci 16:27–35 14. Kramer MA (1991) Nonlinear principal component analysis using autoassociative neural networks. AIChE J 37(2):233–243 15. Kramer MA (1992) Autoassociative neural networks. Comput Chem Eng 16(4):313–328 16. Marseguerra M, Zoia A (2005) The autoassociative neural networks in signal analysis I: the data dimensionality reduction and its geometric interpretation. Ann Nucl Energy 32:1191–1206 17. Marseguerra M, Zoia A (2005) The autoassociative neural networks in signal analysis II: application to on-line monitoring of a simulated BWR component. Ann Nucl Energy 32:1207–1223
1941 18. Marseguerra M, Zoia A (2006) The autoassociative neural networks in signal analysis III: enhancing the reliability of a NN with application to a BWR. Ann Nucl Energy 33:475–489 19. Mendel JM (1995) Fuzzy logic systems for engineering: a tutorial. Proc IEEE 83(3):345–377 20. Monsef WA, Fayez A (2007) Design of a neural: PLC Controller for Industrial Plant. In: International conference on machine learning; models, technologies & applications, MLMTA (June) 25–28, Las Vegas, Nevada, USA 21. Najafi M, Culp Ch, Langari R (2004) Enhanced auto-associative neural networks for sensor diagnosis (E_AANN). In: Proceedings of international journal of conference on neural networks (IJCNN) and IEEE international conference on fuzzy systems, Hungary 22. Qiao W, Venayagamoorthy G, Harley R (2009) Missing-sensorfault-tolerant control for SSSC facts device with real-time implementation. IEEE Trans Power Deliv 24:2 23. Reyes J, Vellasco M, Tanscheit R (2010) Sistemas de Infereˆncia Fuzzy para Auto-Compensac¸a˜o e Auto-Validac¸a˜o em Sensores Inteligentes. In: XVIII Congresso Brasileiro de Automa´tica, Bonito, Brazil (in Portuguese) 24. Sanz J, Perera R, Huerta C (2007) Fault diagnosis of rotating machinery based on auto-associative neural networks and wavelet transforms. J Sound Vib 302:981–999 25. Simani S, Fantuzzi C, Beghelli S (2000) Diagnosis techniques for sensor faults of industrial processes. IEEE Trans Control Syst Technol 8:848 26. Singh H (2004) Development and implementation of an artificially intelligent search algorithm for sensor fault detection using neural networks. M.Sc. Thesis, Texas A&M University 27. Soares-Filho W, Seixas J, Caloba L (2001) Principal component analysis for classifying passive sonar signals. IEEE Int Symp Circuits Syst Syd Aust 2:592–595 28. Theilliol D, Noura H, Ponsart J (2002) Fault diagnosis and accommodation of a three-tank system based on analytical redundancy. ISA 41(3):365–382 29. Tian GY, Zhao ZX, Baines RW (1999) A Fieldbus-based intelligent sensor. Mechatronics 10:835–849 30. Upadhyaya BR, Eryurek E (1992) Application of neural networks for sensor validation and plant monitoring. Nucl Technol 97:170–176 31. Wrest D, Hines W, Uhrig R (1996) Instrument surveillance and calibration verification through plant wide monitoring using autoassociative neural networks. University of Tenn-Knoxville, USA 32. Xiau X, Hines JW, Uhrig RE (1998) Online sensor calibration monitoring and fault detection for chemical processes. Maintenance and Reliability Center, University of Tennessee, USA
123
