Detection of multiple sensor faults in a palm oil fractionation plant using artificial neural network (original) (raw)
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Detection of Sensor Failure in a Palm Oil Fractionation Plant Using Artificial Neural Network
2002
Artificial neural network by virtue of its pattern recognition capabilities has been explored to systematically detect failures in process plants. In this paper, a two-stage approach integrating a neural network dynamic estimator and a neural network fault classifier is proposed to overcome the problem of malfunction in sensors. The process estimator was designed to predict the dynamic behavior of the normal or unfaulty operating process even in the presence of sensor failures. As such, any variables that are related or influenced by the failures under investigation cannot be used as model inputs. The difference between this estimated "normal" and the actual process measurements, termed the residuals are fed to the classifier for fault detection purposes. The classifier that was founded on feedforward network architecture then identifies the source of faults. The estimator was constructed using externally recurrent network where the estimated values are fed back to the input neurons as delayed signals. The scheme was implemented to detect sensor failure in a palm oil fractionation process. To generate the required simulation data, HYSYS.Plant dynamic process simulator was employed. The proposed scheme was successful in detecting pressure and temperature sensor failures introduced within the system.
ARTIFICIAL NEURAL NETWORKS FOR FAULT DETECTION AND DIAGNOSIS IN A PROCESS PLANT
Artificial Neural Networks (ANNs) consist of simple processing elements or nodes that work in unison to solve specific problem. Information processing is achieved through activation and inhibition of the interconnections among the units in the network. The effective computational ability of the Feed-forward neural networks based on the back-propagation algorithm was used to develop a neural network computer program for fault detection and diagnosis in complex chemical plants. Fault detection and diagnosis are mapped as a pattern recognition and classification problem. The catalytic dehydrogenation of heptanes to toluene was simulated as a case study to illustrate the great potential of the program developed. A three-layered network was used in this case study, the network was made up of 18 units in the input layer, 5 units in the first hidden layer, 7 units in the second hidden layer and 6 units in the output layer. Data abstracted from sensor measurements for normal and faulty operating conditions was used to train the network. After training the network for 8,000 epochs, the network learning was seen to be stable and was able to recognize and classify all patterns for the fault scenarios presented with accuracy above 99%.
A neural-network approach to fault detection and diagnosis in industrial processes
IEEE Transactions on Control Systems Technology, 1997
Using a multilayered feedforward neural-network approach, the detection and diagnosis of faults in industrial processes that requires observing multiple data simultaneously are studied in this paper. The main feature of our approach is that the detection of the faults occurs during transient periods of operation of the process. A two-stage neural network is proposed as the basic structure of the detection system. The first stage of the network detects the dynamic trend of each measurement, and the second stage of the network detects and diagnoses the faults. The potential of this approach is demonstrated in simulation using a model of a continuously well-stirred tank reactor. The neural-network-based method successfully detects and diagnoses pretrained faults during transient periods and can also generalize properly. Finally, a comparison with a model-based method is presented.
2001
This paper discusses the application of artificial neural networks in the area of process monitoring, process control and fault detection. Since chemical process plants are getting more complex and complicated, the need of schemes that can improve process operations is highly demanded. Artificial neural network can provide a generic, non-linear solution, and dynamic relationship between cause and effect variables for complex and non-linear processes. This paper will describe the application of neural network for monitoring reactor temperature, estimation and inferential control of a fatty acid composition in a palm oil fractionation process and detection of reactor sensor failures in the Tennessee Eastman Plant (TEP). The potential for the application of neural network technology in the process industries is great. Its ability to capture and model process dynamics and severe process non-linearities makes it powerful tools for process monitoring, control and fault detection.
Use of neural networks for sensor failure detection in a control system
IEEE Control Systems, 1990
This paper discusses the use of the back-propagation neural network for sensor failure detection in process control systems. The back-propagation paradigm along with traditional fault detection algorithms such as the finite integral squarederror method and the nearest-neighbor method are discussed. The algorithm is applied to the Internal Model Control structure for a firs-order linear time-invariant plant subject to high model uncertainty. Compared with traditional methods, the backpropagation technique is shown to be able to discern accurately the supercritical failures from their subcritical counterparts. The use of on-line adapted back-propagation fault detection systems in nonlinear plants is also investigated.
IOP Conference Series: Materials Science and Engineering
In this paper was used the data set collected in a research project between private companies from Romania and Italy, for the development of a basic approach of artificial neural network techniques, as an application in Matlab, aiming to detect the degree of degradation of oil, an automated installation, measuring online the physicochemical properties of the oil. Physical-chemical parameters measured lead to the creation of generous time series, but accessible by numerical and statistical calculation, for the application of artificial intelligence techniques. Applying neural network techniques to parameters that measure oil degradation, oxidation and humidity have generated the results of this work. The main function of monitoring the state of operation of a mechanical system, machine, or plant is to provide the almost correct diagnosis of the machine’s state and rate of change so that preventive measures can be taken at a given time.
Journal of Loss Prevention in the Process Industries, 2012
Batch process usually differs from the continuous process because of its time-varying variables and the process parameters. An early detection and isolation of faults in the process will help to reduce the process upsets and keep it safe and reliable. This paper discusses on the application of multi-layer perceptron neural network in detecting various faults in batch chemical reactor based on an esterification process that involves the reaction of ethanol and acetic acid catalyzed by sulfuric acid. A multilayer feed forward neural network with double hidden layers has been used in the neural network architecture. The detection was based on the different patterns generated between normal and faulty conditions. An optimum network configuration was found when the network produced the minimal error with respect to the training, testing and data validation.
Use of Artificial Neural Networks to Fault Detection and Diagnosis
In a real process, all used resources, whether physical or developed in software, are subject to interruptions or operational commitments. However, in situations in which operate critical systems, any kind of problem can bring big consequences. A coupled water tank system was used as a study case model for implementing and testing the proposed methodology. The developed system should generate a set of signals to notify the process operator about the faults that are occurring, enabling changes in control strategy or control parameters. Due to the damage risks involved with sensors, actuators and amplifiers of the real plant, the data set of the faults are computationally generated and the results will be collected from numerical simulations of the process model. The system will be composed by structures with Artificial Neural Networks.