Uwe Kruger

Statistical monitoring of complex multivariate processes : with applications in industrial process control

The development and application of multivariate statistical techniques in process monitoring has gained substantial interest over the past two decades in academia and industry alike. Initially developed for monitoring and fault diagnosis in complex systems, such techniques have been refined and applied in various engineering areas, for example mechanical and manufacturing, chemical, electrical and electronic, and power engineering. The recipe for the tremendous interest in multivariate statistical techniques lies in its simplicity and adaptability for developing monitoring applications. In contrast, competitive model, signal or knowledge based techniques showed their potential only whenever cost-benefit economics have justified the required effort in developing applications.

Application of Auto-associative Neural Networks to Transient Fault Detection in an IC Engine

The tailpipe emissions from automotive engines have been subject to steadily reducing legislative limits. This reduction has been achieved through the addition of sub-systems to the basic four-stroke engine which thereby increases its complexity. To ensure the entire system functions correctly, each system and / or sub-systems needs to be continuously monitored for the presence of any faults or malfunctions. This is a requirement detailed within the On-Board Diagnostic (OBD) legislation. To date, a physical model approach has been adopted by the automotive industry for the monitoring requirement of OBD legislation. However, this approach has restrictions from the available knowledge base and computational load required. A neural network technique incorporating Multivariant Statistical Process Control (MSPC) has been proposed as an alternative method of building interrelationships between the measured variables and monitoring the correct operation of the engine. Building upon earlier work for steady state fault detection, this paper details the use of non-linear models based on an Auto-associate Neural Network (ANN) for fault detection under transient engine operation. The theory and use of the technique is shown in this paper with the application to the detection of air leaks within the inlet manifold system of a modern gasoline engine whilst operated on a pseudo-drive cycle.Copyright

Compensation terms to improve fault detection in multivariate auto-correlated processes

This paper analyses the use of ARMA filters for detecting abnormal conditions in complex processes. Such filters were recently introduced in the multivariate statistical process control (MSPC) framework to address the issue of auto-correlation in the recorded variables [1]. While these filters can indeed remove auto-correlation from the associated MSPC monitoring scheme, this paper shows that their application influences the sensitivity for fault detection. A compensation term is introduced here to correctly identify the magnitude of abnormal conditions.