Fault Diagnosis In Complex Chemical ProcessesUsing Hierarchical Neural Networks (original) (raw)

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%.

Neural networks in process fault diagnosis

Systems, Man and …, 1991

Fault detection and diagnosis is an important problem in process automation. Both model-based methods and expert systems have been suggested to solve the problem, along with the pattern recognition approach. A number of possible neural network ...

A neural network methodology for process fault diagnosis

Aiche Journal, 1989

The ability of knowledge-based expert systems to facilitate the automation of difficult problems in process engineering that require symbolic reasoning and an efficient manipulation of diverse knowledge has generated considerable interest recently. Rapid deployment of these systems, however, has been difficult because of the tedious nature of knowledge acquisition, the inability of the system to learn or dynamically improve its performance, and the unpredictability of the system outside its domain of expertise.This paper proposes a neural-network-based methodology for providing a potential solution to the preceding problems in the area of process fault diagnosis. The potential of this approach is demonstrated with the aid of an oil refinery case study of the fluidized catalytic cracking process. The neural-network-based system successfully diagnoses the faults it is trained upon. It is able to generalize its knowledge to successfully diagnose novel fault combinations it is not explicitly trained upon. Furthermore, the network can also handle incomplete and uncertain data. In addition, this approach is compared with the knowledge-based approach.

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.

Neural Networks for Process Monitoring, Control and Fault Detection: Application to Tennessee Eastman Plant

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.

Optimum parameters for fault detection and diagnosis system of batch reaction using multiple neural networks

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.

PROCESS FAULT DETECTION USING HIERARCHICAL ARTIFICIAL NEURAL NETWORK DIAGNOSTIC STRATEGY

2007

This paper focuses on the use of artificial neural network (ANN) to detect and diagnose fault in process plant. In this work, the ANN uses two layers of hierarchical diagnostic strategy. The first layer diagnoses the node where the fault originated and the second layer classifies the type of faults or malfunctions occurred on that particular node. The architecture of the ANN model is founded on a multilayer feed forward network and used back propagation algorithm as the training scheme. In order to find the most suitable configuration of ANN, a topology analysis is conducted. The effectiveness of the method is demonstrated by using a fatty acid fractionation column. Results show that the system is successful in detecting original single and transient fault introduced within the process plant model.