Abstract: A system for diagnosing disorders of geographically distributed objects from a remote location. Data for a current condition are compared with predefined data patterns for known disorders to identify a statistically significant match indicating that the monitored object is presently experiencing the corresponding disorder. A critical disorder time is forecasted by determining when a threshold value will be reached using trend analysis of probabilities. A new disorder pattern preceding an observed disorder is added to the knowledge base for future reference. The knowledge base may be used to diagnose one object based on data collected from a similar object in a geographically distinct location. Diverse equipment may be conceptually decomposed into a small set of basic components, and equipment may be diagnosed by analyzing operation of its basic component(s).

Abstract: A system and method for predicting mechanical failures in machinery driven by induction motors by using the motor as a diagnostic tool to detect present mechanical disturbances. The motor is monitored during operation to avoid down-time. The motor's torque fluctuations are used as an indicator of early-stage mechanical failures in the machinery. The motor's torque fluctuations are determined using indirect sensing techniques that are less expensive and less intrusive than previously known. More specifically, torque is derived from easily and inexpensively measurable parameters, such as motor slip and phase angle. Current operation is compared to known normal operation. Variations of the motor's characteristics from the known baseline indicate an actual or approaching mechanical failure. “Experimental Fractals” are disclosed that visually represent a current state of the monitored machinery and allow for visual comparison to a baseline for detection of mechanical failures.