Generating Natural-language Process Descriptions from Formal Process Definitions

ABSTRACT Process models are often used to support the understanding and analysis of complex systems. The accuracy of such process models usually requires that domain experts carefully review, evaluate, correct, and propose improvements to these models. Domain experts, however, are often not experts in process modeling and may not even have any programming experience. Consequently, domain experts may not have the skills to understand the process models except at a relatively superficial level. To address this issue, we have developed an approach for automatically generating natural-language process descriptions based on formal process models. Unlike natural language process descriptions in existing electronic process guides, these process descriptions are generated completely automatically and can describe complex process features, such as exception handling, concurrency, and non-determinisitc choice. The generated process descriptions have been well-received by domain experts from several different fields, and they have also proven useful to process programmers

Automatic fault tree derivation from Little-JIL process definitions

Abstract. Defects in safety critical processes can lead to accidents that result in harm to people or damage to property. Therefore, it is important to find ways to detect and remove defects from such processes. Earlier work has shown that Fault Tree Analysis (FTA) [3] can be effective in detecting safety critical proc-ess defects. Unfortunately, it is difficult to build a comprehensive set of Fault Trees for a complex process, especially if this process is not completely well-defined. The Little-JIL process definition language has been shown to be effec-tive for defining complex processes clearly and precisely at whatever level of granularity is desired [1]. In this work, we present an algorithm for generating Fault Trees from Little-JIL process definitions. We demonstrate the value of this work by showing how FTA can identify safety defects in the process from which the Fault Trees were automatically derived.

L.J.: Automatic Fault Tree Derivation from Little-JIL Process Definitions

Abstract. Defects in safety critical processes can lead to accidents that result in harm to people or damage to property. Therefore, it is important to find ways to detect and remove defects from such processes. Earlier work has shown that Fault Tree Analysis (FTA) [3] can be effective in detecting safety critical process defects. Unfortunately, it is difficult to build a comprehensive set of Fault Trees for a complex process, especially if this process is not completely welldefined. The Little-JIL process definition language has been shown to be effective for defining complex processes clearly and precisely at whatever level of granularity is desired [1]. In this work, we present an algorithm for generating Fault Trees from Little-JIL process definitions. We demonstrate the value of this work by showing how FTA can identify safety defects in the process from which the Fault Trees were automatically derived.

Towards reliable and survivable ocean wave energy converters

Ocean wave energy is a new and developing field of renewable energy with great potential. The energy contained in one meter of an average wave off the coast of Newport Oregon could supply dozens of homes with electricity. However, ocean waves are usually quite irregular which leads to large bursts and lulls in the power available for extraction. These bursts and lulls generate large cyclic system stresses that will invariably work over time to damage an ocean wave energy converter. Due to the generally remote and extreme conditions of deployment, the reliability and survivability of an Ocean Wave Energy Converter (OWEC) are expected to greatly impact the cost of generated power passed to the consumer. For this reason, it is imperative that OWECs are both highly reliable during operation, and highly survivable through extreme conditions. This thesis is a compilation of three papers relating to the reliability and survivability of OWECs. The first paper broadly addresses the probabilistic design of ocean wave energy converters for real ocean waves. The analysis conducted in this paper used 13 years of data from the Stonewall Banks data buoy off the coast of Newport Oregon (NDBC buoy 46050) to extrapolate probabilistic information that could be used throughout the design process to improve system reliability. The second paper provides a definition and metric for the widely used term survivability. Survivability is often confused with the similar concept of reliability. The paper seeks to highlight differences between the two terms with the intention of clarifying their relation to system design. The final paper presents a method for concept evaluation in the earliest stages of design. A comparative function based failure analysis is conducted during the concept stage to aid in design selection. Selecting concepts that show promising failure traits early in the design process will improve the reliability and survivability of the final system