Statistical methods of partial discharge analysis utilizing histogram similarity measures are provided by quality assessment and condition monitoring methods of evaluating high voltage electrical insulation. The quality assessment method is utilized to evaluate the quality of insulation within the electrical equipment. The condition monitoring method is utilized during normal operation of the equipment to identify degradation in the insulation and to predict catastrophic insulation failures.

A system and method of verifying the installation and data path used by a tank level monitor is provided. Once an installer completes the installation of the tank level monitor, the monitor is activated. The monitor performs a self test and transmits information to a data center. Preferably, the transmission is via a cellular communication network. Once the data center receives the information from the newly installed tank level monitor, the data center generates a text based message that is sent to the installer to verify that the installation was successful. The message from the data center is preferably an email message that can be delivered to a wireless messaging device held by the installer. The generation and transmission of the verification message should be approximately immediate, and certainly within five minutes to provide rapid feed back to the installer.

An improved empirical model-based surveillance or control system for monitoring or controlling a process or machine provides identification of transitions between operational states. Empirical model-based estimates generated in response to receiving actual operational parameters are compared using a global similarity operator to the actual parameters to indicate whether the process or machine is operating in a stable state, or is in transition from one state to another.

A component trend monitoring system for monitoring the performance of components and comparing the performance with stored performance data to accurately trend and predict the failure of the components. The system includes computer chips attached to the various components, for receiving and storing historical and performance data about each component, and a processor for retrieving the stored data from the memory chips. The processor receives and analyzes the data against historical data for predicting failure based upon past trends within the historical data. The processor also provides a signal for impending failure of a given component.

Techniques for monitoring a machine for significant deviations from normal operations include collecting, at a first processing element, sensor data about the machine. The first processing element performs narrowband frequency domain processing to determine a segment of sensor data that indicates a deviation from normal operations that exceeds a threshold deviation. A message including the segment of sensor data is sent to a second processing element. In response to receiving the message, the second processing element performs different narrowband frequency domain processing to determine whether the deviation from normal operations is significant for maintaining the machine. If the deviation from normal operations is determined to be significant for maintaining the machine, then the deviation is reported to cause the machine to be maintained.