Pilot interaction with automated airborne decision making systems

The use of advanced software engineering methods (e.g., from artificial intelligence) to aid aircraft crews in procedure selection and execution is investigated. Human problem solving in dynamic environments as effected by the human's level of knowledge of system operations is examined. Progress on the development of full scale simulation facilities is also discussed

The historical development and basis of human factors guidelines for automated systems in aeronautical operations

In order to derive general design guidelines for automated systems a study was conducted on the utilization and acceptance of existing automated systems as currently employed in several commercial fields. Four principal study area were investigated by means of structured interviews, and in some cases questionnaires. The study areas were aviation, a both scheduled airline and general commercial aviation; process control and factory applications; office automation; and automation in the power industry. The results of over eighty structured interviews were analyzed and responses categoried as various human factors issues for use by both designers and users of automated equipment. These guidelines address such items as general physical features of automated equipment; personnel orientation, acceptance, and training; and both personnel and system reliability

Pilot interaction with automated airborne decision making systems

Two project areas were pursued: the intelligent cockpit and human problem solving. The first area involves an investigation of the use of advanced software engineering methods to aid aircraft crews in procedure selection and execution. The second area is focused on human problem solving in dynamic environments, particulary in terms of identification of rule-based models land alternative approaches to training and aiding. Progress in each area is discussed

Introduction to Nuclear Propulsion: Lecture 15 - Nuclear Test Operations

The test operation of nuclear power plants, specifically nuclear rockets, bears some interesting similarities to the operation of chemical rocket tests as well as, of course, many differences. A significant feature common to both nuclear and chemical rocket tests is that all the fuel for the entire operation is loaded at the start of the test. As a direct consequence of this fact, the operation of nuclear power plants must be surrounded with adequate safety precautions, as is indeed the case in the operation of chemical rockets, A second direct consequence is that in both types of testing a very thorough and complete checkout is made before starting the test

AI at Ames: Artificial Intelligence research and application at NASA Ames Research Center, Moffett Field, California, February 1985

Charts are given that illustrate function versus domain for artificial intelligence (AI) applications and interests and research area versus project number for AI research. A list is given of project titles with associated project numbers and page numbers. Also, project descriptions, including title, participants, and status are given

Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

Computer-aided HAZOP of batch processes

The modern batch chemical processing plants have a tendency of increasing technological complexity and flexibility which make it difficult to control the occurrence of accidents. Social and legal pressures have increased the demands for verifying the safety of chemical plants during their design and operation. Complete identification and accurate assessment of the hazard potential in the early design stages is therefore very important so that preventative or protective measures can be integrated into future design without adversely affecting processing and control complexity or capital and operational costs. Hazard and Operability Study (HAZOP) is a method of systematically identifying every conceivable process deviation, its abnormal causes and adverse hazardous consequences in the chemical plants. [Continues.

Data Management in an Operational Context: A study at Volvo Group Trucks Operations

The present customer based and data driven world of business, together with the evolving social and technological trends continue to drive product complexity and product variety to a new height, one that is characterized by product customization. In the product realization pro-cess, the assembly floor remains the most vital part of the manufacturing process; to cope with the rigorous demands of product complexity and product variety, operators rely heavily on quality assembly instruction in manual assembly operations. Previous studies conducted at Volvo Group Trucks Operations (GTO) revealed the different working processes for creating assembly instructions, due to historical growth and acquisitions of other truck manufacturers. These different working processes are one of the main reasons for the reported quality devia-tion in assembly operations. This thesis therefore provides the current state analysis of data and information handling in the product realization process. Two research methods, direct observation and Semi-structured interview, were employed to investigate the availability and usage of data and information in the engineering phase and in the assembly phase. The study was conducted at manual assembly stations across three production plants within a case com-pany. The result indicates gaps and differences in opinion of how data is perceived, interpret-ed and utilized. Hence there are no standard approach on how data and information is man-aged in the manufacturing engineering IT systems and assembly information systems within all levels of the organization