Romex - An Expert System Testbed For Turbomachinery Diagnostics.

Tutorialpg. 135-148A rule based system developed for vibration oriented diagnosis of turbomachinery for fault identification and for predictive maintenance is described. The system is implemented in a PC based PROLOG environment, with the Dempster Shafer theory of belief functions utilized for evidential support of hypotheses. The direct uses of PROLOG for knowledge representation, rule interpretation, control strategy, and user interaction are described. The vibration fault diagnosis system is considered to be one component of a comprehensive system for turbomachinery. The framework of this comprehensive system comprises hierarchical levels of generic rules (surface knowledge) and generic analytical simulation models (deep knowledge). The root level includes the surface and deep knowledge for vibration, bearings, lubricant, seals, gears and couplings, and mechanical/metallurgical aspects of fault detection. Another level comprises the generic but specific knowledge base for various categories of turbomachinery, i.e., pumps, compressors, turbines, engines. The third level includes the installation specific rules, maintenance, repair, and troubleshooting logs, and other specific usage experiences. It is shown that each component of the comprehensive system can be viewed as a distinct expert system which can be developed and utilized independently of the other subsystems while the comprehensive system is evolved over a period of time

Physics-Based Modeling Strategies for Diagnostic and Prognostic Application in Aerospace Systems

This paper presents physics-based models as a key component of prognostic and diagnostic algorithms of health monitoring systems. While traditionally overlooked in condition-based maintenance strategies, these models potentially offer a robust alternative to experimental or other stochastic modeling data. Such a strategy is particularly useful in aerospace applications, presented in this paper in the context of a helicopter transmission model. A lumped parameter, finite element model of a widely used helicopter transmission is presented as well as methods of fault seeding and detection. Fault detection through diagnostic vibration parameters is illustrated through the simulation of a degraded rolling-element bearing supporting the transmission’s input shaft. Detection in the time domain and frequency domain is discussed. The simulation shows such modeling techniques to be useful tools in health monitoring analysis, particularly as sources of information for algorithms to compare with real-time or near real-time sensor data.</p

A physicsbased model for predicting user intent in shared-control pedestrian mobility aids

Abstract — This paper presents a physics-based model approach to infer navigational intent of the user of a walker, based on measuring forces and moments applied to the walker’s handles. Our experiments use two 6-DOF force/moment sensors on the walker’s handles, a 2-D kinematic-dynamic model of the walker and a digital motion capture system to trace the path of the walker. The motion capture system records the path the walker follows while the 6 DOF sensors record the handle forces used to guide the walker along that path. A dynamic model of the walker that determines user navigational intent from force/moment data was developed and validated against the motion capture data streams. This paper describes the development and validation of the model as well as plans for using the model as a path predictor. The inferred user intent will be incorporated into a passive shared steering control system for the walker

Shared Navigational Control and User Intent Detection in an Intelligent Walker

This paper describes the navigational control scheme used in the CO-Operative Locomotion Aide (COOL Aide), an intelligent walker designed to assist the elderly or the disabled with normal, and routine walking tasks. Navigation is achieved through a shared control architecture that recognizes the goals of both the human user and the walker. The control system is based on a synthesis of heuristic logic that exploits a dynamic model of walker system that can detect sliding and loss of walker stability. The model is used to predict the user’s intended path, based on the history of information collected from the walker’s sensors. Sensor information consists primarily of the forces and moments the user exerts on the walker’s handles during the natural assisted walking process, as well as the user’s local environment. Based on the model’s prediction, the walker’s state, and the walker’s environment, the control system can confirm or overturn the hypotheses of user’s intent it put forward and can influence the walker’s heading if the system believes the user will not reach the perceived intended goal unassisted. This paper discusses the model’s use in the shared control scheme and the mechanism for detecting/handling errors in the model’s predictions

User Intent in a Shared Control Framework for Pedestrian Mobility Aids

This paper presents a novel approach to infer navigational intent of the user of a walker, based on measuring forces and moments applied to the walker’s handles. While there are many types of “intent ” that could be inferred for a given user action, the experiments conducted here focused on the determining user’s navigational intent, i.e. their desired heading. Our experiments used two 6-DOF force/ moment sensors on the walker’s handles and a digital motion capture system to correlate applied force with actual motion. Preliminary results revealed that the intent to turn, represented by changes in the heading angle, highly correlates with the overall turning moment around the vertical axis as well as the side forces applied by the user. Other force/moment components reveal additional information, such as support needs. The inferred user intent will be incorporated into a passive shared steering control system for the walker. 1