The proper handling of alarms is crucial to automated process control. In practice, many alarms are only distractive and do not represent a fault situation. Here, a toolbox is proposed that aims to reduce the occurrence of the so called nuisance alarms. It is a general computerized tool that takes advantage of the control system's built-in functions, and is a first step to an enhanced overall alarm situation. In the first prototype, a significant reduction of the alarms in a biofueled district heating plant has been achieved.

Presents an overview of algorithms for performing alarm analysis on technical processes. These new algorithms combine existing algorithms for alarm analysis using multilevel flow models (MFM) with quantitative and probabilistic algorithms. The existing MFM algorithms handle multiple alarm situations but they are sensitive to uncertainties, for example, noise and signals close to decision limits. Combining MFM alarm analysis with fuzzy logic is shown as an interesting approach to this problem.

This paper describes an informal but systematic method for how to test and verify a knowledge-based system in a large open-ended medical target domain. The system used is Guardian, an intelligent system for monitoring and diagnosis of post-cardiac surgery patients in an intensive-care unit. The knowledge base is tested and verified by running the system on a series of realistic test scenarios, both with an embedded simulator and with an external simulation system. The same scenarios are presented to human test subjects, making it possible to compare and analyze the performance of the knowledge-based system with that of human physicians. The use of simulators instead of clinical data also means that it is possible to test crucial scenarios which occur seldom in medical practice. Our results show that a system like Guardian might indeed be useful in medical care.

Describes the use of explicit models of goals and functions for monitoring and diagnosis of intensive-care patients. The method is based on multilevel flow models (MFM) and used in the Guardian system. It provides this system with alarm analysis, fault diagnosis, and automatic generation of explanations. Advantages include a relatively easy knowledge engineering effort and good properties for use in a system with hard real time deadlines. The results of some experiments are also reported.

This article describes three diagnostic methods for use with industrial processes. They are measurement validation, i.e., consistency checking of sensor and measurement values using any redundancy of instrumentation; alarm analysis, i.e., analysis of multiple alarm situations to find which alarms are directly connected to primary faults and which alarms are consequential effects of the primary ones; and fault diagnosis, i.e., a search for the causes of and remedies for faults. The three methods use multilevel flow models (MFM), to describe the target process. They have been implemented in the real-time expert system tool G2, in C, and in Common Lisp, and successfully tested on simulations of several processes. The knowledge representation ontology used is based on the notion of flows, of mass, energy, and information, which are used to describe physical systems. The relationships between structure and function of a system is described by teleological relations, which connect the flow structures into a graph, built at model construction time. This allows the diagnostic reasoning to be implemented as searches in a static graph structure, and it can thus be performed extremely rapidly. As with other model-based approaches, general algorithms are used over a representation with generative capacities. The representation gains strength from being functional with a very abstract physical level, more abstract than most qualitative physics models. It works well with systems that can be described using flows, while it currently lacks the capability of capturing important aspects of other types of systems, for example, electronic circuits.

A domain-specific software architecture (DSSA) is presented that supports development of a variety of intelligent patient monitoring (IPM) agents through component reuse and reconfiguration. Specifically, the IPM DSSA comprises: (a) a reference architecture that supports the shared functional requirements of IPM agents and provides a framework for configuring diverse application-specific sets of components; (b) principles for decomposing IPM expertise into highly reusable components, along with a growing library of such components; and (c) an interactive applications configuration tool that helps a user to select application-relevant components from the library and automatically configures selected components within the architecture. The efficacy of the IPM DSSA is demonstrated with results from the Guardian project, a series of experimental agents for monitoring intensivecare patients.