Abstract:
A system (10) and method for monitoring an industrial process and/or industrial data source (10). The system (10) includes a time correlation module (20), a training module (30), a system state estimation module (40) and a pattern recognition module (50). The system (10) generating time varying data sources, processing the data to obtain time correlation of the data (20), determining the range of data, determining learned states of normal operation (30) and using these states to generate expected values to identify a current state of the process closest to a learned, normal state (40); generating a set of modeled data, and processing the modeled data to identify a data pattern and generating an alarm (50) upon detecting a deviation from normalcy.
Abstract:
A method and system for automatically establishing operational parameters of a statistical surveillance system. The method and system performs a frequency domain transition (20) on time dependent data, first Fourier composite (30) is formed, serial correlation (35) is removed, a series of Gaussian whiteness tests (50) are performed along with an autocorrelation (35) test, Fourier coefficients (80) are stored and a second Fourier composite is formed. Pseudorandom noise is added, a Monte Carlo simulation is performed to establish SPRT missed alarm probabilities and tested with a synthesized signal. A false alarm test is then empirically evaluated and if less than a desired target value, then SPRT probabilities are used for performing surveillance.
Abstract:
A method and system for monitoring a process and determining its condition. Initial data is sensed, a first set of virtual data is produced by applying a system state (20) analyzation to the initial data, a second set of virtual data is produced by applying a neural network (40) analyzation to the initial data and a parity space (50) analyzation is applied to the first and second set of virtual data and also to the initial data to provide a parity space (50) decision about the condition of the process. A logic test (60) can further be applied to produce a further system decision about the state of the process.
Abstract:
The invention is a method and system for monitoring at least one of a system, a process and a data source. A method and system have been developed for carrying out surveillance, testing and modification of an ongoing process or other source of data, such as a spectroscopic examination. A signal from the system under surveillance is collected and compared with a learned states (40), a frequency domain transformation carried out for the system signal and reference signal, and a frequency domain difference function is established. The process is then repeated until a full range of data is accumulated over the time domain and an SPRT methodology (50) is applied to determine a three-dimensional surface plot (60) characteristic of the operating state of the system under surveillance.
Abstract:
A method and system for monitoring an industrial process and a sensor. The method and system include generating a first and second signal characteristic of an industrial process variable. One of the signals can be an artificial signal generated by an auto regressive moving average technique. After obtaining two signals associated with one physical variable, a difference function is obtained by determining the arithmetic difference between the two pairs of signals over time. A frequency domain transformation is made of the difference function to obtain Fourier modes describing a composite function. A residual function is obtained by subtracting the composite function from the difference function and the residual function (free of nonwhite noise) is analysed by a statistical probability ratio test.
Abstract:
The invention is a method and system for monitoring at least one of a system, a process and a data source. A method and system have been developed for carrying out surveillance, testing and modification of an ongoing process or other source of data, such as a spectroscopic examination. A signal from the system under surveillance (10) is collected and compared with a reference signal (20), a frequency domain transformation carried out for the system signal and reference signal, and a frequency domain difference function is established. The process is then repeated until a full range of data is accumulated over the time domain and a SPRT methodology (50) is applied to determine a three-dimensional surface plot (60) characteristic of the operating state of the system under surveillance.
Abstract:
A method and apparatus for monitoring a source of data for determining an operating state of a working system. The method includes determining a sensor (or source of data) arrangement associated with monitoring the source of data for a system, activating a method for performing a sequential probability ratio test if the data source includes a single data (sensor) source, activating a second method for performing a regression sequential possibility ratio testing procedure if the arrangement includes a pair of sensors (data sources) with signals which are linearly of non-linearly related; activating a third method for performing a bounded angle ratio test procedure if the sensor arrangement includes multiple sensors and utilizing at least one of the first, second and third methods to accumulate sensor signals and determining the operating state of the system.
Abstract:
A system (10) and method for monitoring an industrial process and/or industrial data source (10). The system (10) includes a time correlation module (20), a training module (30), a system state estimation module (40) and a pattern recognition module (50). The system (10) generating time varying data sources, processing the data to obtain time correlation of the data (20), determining the range of data, determining learned states of normal operation (30) and using these states to generate expected values to identify a current state of the process closest to a learned, normal state (40); generating a set of modeled data, and processing the modeled data to identify a data pattern and generating an alarm (50) upon detecting a deviation from normalcy.
Abstract:
A method and system (110) for monitoring both an industrial process and a sensor (104). The method and system include determining a minimum number of sensor pairs needed to test the industrial process as well as the sensor (104) for evaluating the state of operation of both. After obtaining two signals associated with one physical variable, a difference function is obtained by determining the arithmetic difference between the pair of signals over time. A frequency domain transformation is made of the difference function to obtain Fourier modes describing a composite function. A residual function is obtained by subtracting the composite function from the difference function and the residual function (free of nonwhite noise) is analyzed by a statistical probability ratio test.
Abstract:
A method and apparatus for monitoring a source of data for determining an operating state of a working system. The method includes determining a sensor (or source of data) arrangement associated with monitoring the source of data for a system, activating a method for performing a sequential probability ratio test if the data source includes a single data (sensor) source, activating a second method for performing a regression sequential possibility ratio testing procedure if the arrangement includes a pair of sensors (data sources) with signals which are linearly or non-linearly related; activating a third method for performing a bounded angle ratio test procedure if the sensor arrangement includes multiple sensors and utilizing at least one of the first, second and third methods to accumulate sensor signals and determining the operating state of the system.