Design, Simulation and Evaluation of Effective Industrial Information Systems: Case of Machine Condition Monitoring and Maintenance Management Information Systems

Abstract: Industrial information systems can improve the management of industrial operations by providing relevant and timely information on the status of physical assets via condition and process monitoring systems, and by enabling the effective use of data by systems and processes that restore proper functionality via control and maintenance systems. A framework and methods have been developed that extend current information system design methods for design and evaluation of effective industrial information systems, as a method for more concretely demonstrating the value of system features prior to system design, and for assessing system performance after implementation for continuous system improvement. An effective system from the framework’s perspective is first, performance-based, i.e. benefits due to the system are balanced with associated costs. Second, it is fit for the system owner’s purpose, i.e. it has been designed with respect to organizational business strategy, objectives, and constraints. And third, it is holistic, i.e. it addresses various performance criteria that are important to the system stakeholders, e.g. operational excellence, financial, safety and environmental. This thesis describes the development and application of the framework and methods, and verifies their effectiveness using data from an industrial operation. The application case is on the development of a machine condition monitoring and maintenance management information system for mobile machine management of mining and production operations. Evaluation methods have been developed to assess several candidate systems and to select the most effective alternative as part of the framework. The first method is a risk assessment method for analytical performance evaluation of the system using a time-based, event-risk tree model. The second method involves simulating the system for evaluating system performance based on a developed probability model linked, stochastic, discrete event simulation method, which includes machine events, operations workflow, information flow, and decision making related to the case operations. After system alternatives are evaluated, the process for selecting the most effective system is then demonstrated using the analytical hierarchy process (AHP) method coupled with the framework as a multi-criteria decision analysis method. Verification of the case description, models, and the assumed parameter values has been achieved by subject matter expert review.