Anil Varma

ICARUS: design and deployment of a case-based reasoning system for locomotive diagnostics

Abstract Locomotives, like many complex modern machines, are equipped with the capability to generate on-board fault messages indicating the presence of anomalous conditions. Such messages tend to be generated in large quantities, and are difficult and time consuming to interpret manually. This paper presents the design and development of a case-based reasoning system for diagnosing locomotive faults using such fault messages as input. The process of using historical repair data and expert input for case generation and validation is described. An algorithm for case matching is presented, along with some results on pilot data.

Design of local fuzzy models using evolutionary algorithms

The application of local fuzzy models to determine the remaining life of a unit in a fleet of vehicles is described. Instead of developing individual models based on the track history of each unit or developing a global model based on the collective track history of the fleet, local fuzzy models are used based on clusters of peers-similar units with comparable utilization and performance characteristics. A local fuzzy performance model is created for each cluster of peers. This is combined with an evolutionary framework to maintain the models. A process has been defined to generate a collection of competing models, evaluate their performance in light of the currently available data, refine the best models using evolutionary search, and select the best one after a finite number of iterations. This process is repeated periodically to automatically update and improve the overall model. To illustrate this methodology an asset selection problem has been identified: given a fleet of industrial vehicles (diesel electric locomotives), select the best subset for mission-critical utilization. To this end, the remaining life of each unit in the fleet is predicted. The fleet is then sorted using this prediction and the highest ranked units are selected. A series of experiments using data from locomotive operations was conducted and the results from an initial validation exercise are presented. The approach of constructing local predictive models using fuzzy similarity with neighboring points along appropriate dimensions is not specific to any asset type and may be applied to any problem where the premise of similarity along chosen attribute dimensions implies similarity in predicted future behavior.

Towards autonomic systems for lifecycle support of complex equipment

GE has been a leader in remote monitoring and diagnostics of complex systems, such as medical imaging equipment and aircraft engines, for a number of years. The data gathered and the analytical capabilities developed have naturally lead toward service contracts to maintain individual machines and fleets of machines while lowering the cost of ownership for our customers. As information technology extends the reach of diagnostic, prognostic, and decision support systems, opportunities to optimize support of specific organizational objectives is enhanced. Such a decision support system is envisioned and is inspired by the human autonomic nervous system.

Managing Diagnostic Knowledge in Text Cases

A valuable source of field diagnostic information for equipment service resides in the text notes generated during service calls. Intelligent knowledge extraction from such textual information is a challenging task. The notes are characterized by misspelled words, incomplete information, cryptic technical terms, and non-standard abbreviations. In addition, very few of the total number of notes generated may be diagnostically useful. We present an approach for identifying diagnostically relevant notes from the many raw field service notes and information is presented in this paper. N-gram matching and supervised learning techniques are used to generate recommendations for the diagnostic significance of incoming service notes. These techniques have potential applications in generating relevant indices for textual CBR.

Anomaly Detection Using Non-Parametric Information

Diagnostics and prognostics is particularly challenging in systems with a restricted suite of sensors; e.g., in aircraft engines where harsh operating conditions, weight considerations, and regulatory concerns limit the number of sensors. In this paper, we investigate anomaly detection techniques subject to these constraints. Specifically, we use as input to these techniques only controller-generated, log-data for the system. While such log-data is not designed to carry predictive information related to system health, we show that it is possible to extract early warning signals related to the failure of the system by looking for the presence or onset of anomalous or novel patterns in the log-data. We present preliminary results obtained by the application of this approach to some complex systems. We also provide a roadmap for extending this approach by the incorporation of minimal amount of system-specific knowledge of the kind that is typically available for complex systems. This extension is expected to strengthen the applicability of the approach to diagnostic and prognostic analysis at the level of the system components, as well as to the estimation of the root cause of a detected system anomaly.Copyright