Complex dynamical systems such as aircraft, manufacturing systems, chillers, motor vehicles, submarines, etc. exhibit continuous and event-driven dynamics. These systems undergo several discrete operating modes from startup to shutdown. For example, a certain shipboard system may be operating at half load or full load or may be at start-up or shutdown. Of particular interest are extreme or “shock” operating conditions, which tend to severely impact fault diagnosis or the progression of a fault leading to a failure. Fault conditions are strongly dependent on the operating mode. Therefore, it is essential that in any diagnostic/prognostic architecture, the operating mode be identified as accurately as possible so that such functions as feature extraction, diagnostics, prognostics, etc. can be correlated with the predominant operating conditions. This paper introduces a mode identification methodology that incorporates both time- and event-driven information about the process. A fuzzy Petri net is used to represent the possible successive mode transitions and to detect events from processed sensor signals signifying a mode change. The operating mode is initialized and verified by analysis of the time-driven dynamics through a fuzzy logic classifier. An evidence combiner module is used to combine the results from both the fuzzy Petri net and the fuzzy logic classifier to determine the mode. Unlike most event-driven mode identifiers, this architecture will provide automatic mode initialization through the fuzzy logic classifier and robustness through the combining of evidence of the two algorithms. The mode identification methodology is applied to an AC Plant typically found as a component of a shipboard system.
This paper introduces a novel methodology to prognostics based on a dynamic wavelet neural network construct and notions from the virtual sensor area. This research has been motivated and supported by the U.S. Navy's active interest in integrating advanced diagnostic and prognostic algorithms in existing Naval digital control and monitoring systems. A rudimentary diagnostic platform is assumed to be available providing timely information about incipient or impending failure conditions. We focus on the development of a prognostic algorithm capable of predicting accurately and reliably the remaining useful lifetime of a failing machine or component. The prognostic module consists of a virtual sensor and a dynamic wavelet neural network as the predictor. The virtual sensor employs process data to map real measurements into difficult to monitor fault quantities. The prognosticator uses a dynamic wavelet neural network as a nonlinear predictor. Means to manage uncertainty and performance metrics are suggested for comparison purposes. An interface to an available shipboard Integrated Condition Assessment System is described and applications to shipboard equipment are discussed. Typical results from pump failures are presented to illustrate the effectiveness of the methodology.
The U.S. demand for deboned chicken has risen greatly in the past 5 years, with the expectations that this demand will only continue at an accelerated level. The standard inspection process for bones in meat is for workers to manually feel for bones. It is clear that this time- consuming manual inspection method is insufficient to meet the increasing demand for deboned meat products. Georgia Tech Electrical Engineering faculty and Research Scientists in conjunction with a leading x-ray equipment manufacturer are working together on the development of a system to fuse information from visible images and x-ray images to enhance the accuracy of detection. Currently there are some bones that x-ray systems have difficulty detecting. These are usually relatively thin and are located near the surface of the meat. A primary example is a fanbone (so called because of its shape). We will describe and present results from work geared towards the development of an integrated system that would fuse visible and x-ray information. Significant benefits to the poultry industry are anticipated in terms of reduced processing costs, improved inspection performance and increased throughput through the use of the integrated system to be described. Additionally, generic aspects of the proposed technologies may be applicable to other food processing industries.
In the textile industry, the degree of fabric pilling is subjectively determined by human inspectors resulting in inconsistent quality control. The observed resistance to pilling is reported on an arbitrary scale ranging from No. 5 (no pillings) to No. 1 (very severe pilling). This paper presents a system and a methodology that counts the number of pillings on textile fabric samples automatically and classifies them into one of the pre-defined classes with repeatable accuracy while accounting for the human judgment by allowing the determination of the degree of confidence assigned to the sample's membership in each class. The system consists of an apparatus; an imaging and data processing software procedure for counting the number of pillings; and a methodology for classifying the fabric samples into one of the pre-defined classes with repeatable accuracy while accounting for human judgment. A CCD camera is used to capture successive gray scale images of the fabric sample. A series of segmentation, Radon transform, morphological filtering, and detrending operations are applied to the fabric images to determine the true pilling count. The structuring element for the morphological operations is designed such that fuzz balls (which are not pillings) are filtered. Using fuzzy membership functions, the fabric pilling count is mapped to fabric pilling resistance rating. The system has been successfully tested on a large number of fabric samples with different shades and textures provided by the textile industry.
Conference Committee Involvement (2)
Defense Transformation and Net-Centric Systems 2009
14 April 2009 | Orlando, Florida, United States
Defense Transformation and Net-Centric Systems 2008