Integrated FDD system for valve stiction in a paperboard machine

The performance of a modern industrial plant can be severely affected by the performance of its key devices, such as valves. In particular, valve stiction can cause poor performance in control loops and can consequently lower the efficiency of the plant and the quality of the product. This paper presents an integrated FDD system for valve stiction which employs various FDD methods in a parallel configuration. A reliability index was integrated into each method in order to estimate their degree of influence in the final diagnosis of the system. Each method and the integrated system were tested using industrial data.     

Monitoring of caliper sensor fouling in a board machine using self-organising maps

This paper presentes a process-monitoring scheme utilising adaptive self-organising maps (SOM) to detect process conditions that lead to the fouling of a caliper sensor in a board machine. The scheme is based on mapping on a SOM the process measurements and the calculated variables which provide insight into the chemical phenomena involved in fouling to classify faulty process conditions. The time-variant nature of the board making process was taken into account by regularly re-training the SOM. The monitoring scheme is demonstrated with industrial data, and the results are presented and discussed.     

Fault detection and diagnosis approach based on nonlinear parity equations and its application to leakages and blockages in the drying section of a board machine

This study aims at providing a fault detection and diagnosis (FDD) approach based on nonlinear parity equations identified from process data. Process knowledge is used to reduce the process nonlinearity from high to low-dimensional nonlinear functions representing common process devices, such as valves, and incorporating the monotonousness properties of the dependencies between the variables. The fault detection approach considers the obtained process model to be nonlinear parity equations, and fault diagnosis is carried out with the standard structured residual method. The applicability of the approach to complex flow networks controlled by valves is tested on the drying section of an industrial board machine, in which the key problems are leakages and blockages of valves and pipes in the steam–water network. Nonlinear model equations based on the mass balance of different parts of the network are identified and validated. Finally, fault detection and diagnosis algorithms are successfully implemented, tested, and reported.     

A neural network simulation method of aphasic naming errors: Properties and behaviour

Aphasia is a language disorder caused by brain damage. Naming errors which are common in aphasia are applied to reveal the type and the effects of a disorder. Naming in this research field refers to psycholinguistic tests where a subject is asked to say the name of an object presented as a picture to him or her. We have earlier presented a simulation model on the basis of neural networks [1,2]. The model is further developed here, and its properties and behaviour are described in the present paper. The simulation model includes a bounded set of Finnish words in their base lingual form. The principle of activation spreading is used to process naming errors with the method to simulate actual aphasic errors. All computation in the model is executed with words as text or with textual components of words, although the system processes naming errors, i.e. human speech.This work was partially supported by the Academy of Finland.