Process plant trip or perturbation prevention
Systems and methods for simultaneously analyzing time-series and dominant frequency data from both a process domain and an electrical domain of a facility, such as an industrial plant, to detect instances of deviation from optimal, normal, or other predefined process conditions or electrical trips. Detecting when changes in frequencies occur in the time-series data creates a time-series of the changes in dominant frequencies. A data analysis server detects instances of deviation from the predefined process conditions or electrical trips by detecting one or more of correlations, patterns, clusters, and the like in the rates of change. The data analysis server employs one or more of statistical analyses, data mining, machine learning, deep neural networks, parallel coordinate analyses, etc. to identify the deviations and predict or detect onset of an undesired event such as a process perturbation or electrical trip.
1 . A system for detecting interruptions in operations of a facility, the facility having a process domain and an electrical domain, the system comprising:
one or more process domain sensors associated with the process domain of the facility, the process domain sensors configured to acquire samples at a first frequency for providing first time-series data, the first-time series data comprising one or more operating characteristics from the process domain sampled at the first frequency, the first time-series data having a first resolution;
one or more electrical domain sensors associated with the electrical domain of the facility, the electrical domain sensors configured to acquire samples at a second frequency different than the first frequency for providing second time-series data, the second time-series data comprising one or more electrical parameters from the electrical domain sampled at the second frequency, the second time-series data having a second resolution different than the first resolution;
a data analysis processor receiving and responsive to the first and second time-series data;
a memory coupled to the data analysis processor, the memory storing processor-executable instructions that, when executed, configure the data analysis processor for:
performing a wavelet transformation on the first and second time-series data to extract features therefrom indicating when frequency changes occur in the first and second time-series data;
converting the extracted features of at least one of the first time-series data or the second time-series data to form a combined data set having a common resolution;
identifying, from the combined data set, one or more dominant frequency changes over time in at least one of the first time-series data from the process domain or the second time-series data from the electrical domain or both;
detecting one or more instances of deviation from predefined conditions in the process domain or the electrical domain or both based on the identified one or more dominant frequency changes over time and the first and second time-series data, each of the detected one or more instances of deviation indicating an onset of a facility interruption; and
in response to detecting the one or more instances of deviation from the predefined conditions indicating the onset of the facility interruption, performing at least one automated action configured to mitigate the facility interruption.
2 . The system of claim 1 , wherein the facility interruption comprises at least one of a process perturbation in the process domain or an electrical trip event in the electrical domain.
3 . The system of claim 1 , wherein identifying, from the combined data set, one or more dominant frequency changes comprises analyzing at least one of: patterns, correlations, clusters, or rate of change indications therein.
4 . The system of claim 1 , wherein the at least one action configured to mitigate the facility interruption comprises at least one of: generating an alert or providing a recommendation to prevent the facility interruption from occurring.
5 . The system of claim 1 , wherein the memory stores processor-executable instructions that, when executed, further configure the data analysis processor for generating at least one corrective action configured to be taken by an operator to prevent the facility interruption from occurring.
6 . The system of claim 1 , wherein the memory stores processor-executable instructions that, when executed, further configure the data analysis processor for:
simulating operation of the facility with a digital twin thereof; and
producing, by the digital twin, simulated output parameters based on the detected one or more instances of deviation.
7 . The system of claim 6 , wherein the simulated output parameters produced by the digital twin correspond to output parameters associated with the facility.
8 . The system of claim 7 , wherein the memory stores processor-executable instructions that, when executed, further configure the data analysis processor for:
plotting the output parameters associated with the facility and the simulated output parameters on at least one parallel coordinates plot; and
identifying, from the parallel coordinates plot, an operating range for which the simulated output parameters are within an optimum range of values.
9 . The system of claim 1 , wherein converting the extracted features of at least one of the first time-series data or the second time-series data comprises synchronizing the first time-series data from the process domain and the second time-series data from the electrical domain with each other.
10 . The system of claim 1 , wherein the memory stores processor-executable instructions that, when executed, further configure the data analysis processor for combining the converted extracted features of the second time-series data from the electrical domain with the first time-series data from the process domain to form the combined data set.
11 . The system of claim 10 , wherein detecting the one or more instances of deviation from predefined conditions comprises executing machine learning on the combined data set.
12 . The system of claim 1 , further comprising a controller configured to generate a command for adjusting an operating parameter of one or more of a pump, a motor, a valve, an actuator, an electrostatic precipitator, an electrolyzer, a vibrator, or a heater.
13 . The system of claim 1 , wherein noise is injected into at least one of the electrical domain or the process domain before performing the wavelet transformation.
14 . A method for detecting interruptions in operations of a facility, the facility having a process domain and an electrical domain, the system comprising:
acquiring, by one or more process domain sensors associated with the process domain of the facility, samples at a first frequency for providing first time-series data, wherein the first-time series data comprises one or more operating characteristics from the process domain sampled at the first frequency, and wherein the first time-series data has a first resolution;
acquiring, by one or more electrical domain sensors associated with the electrical domain of the facility, samples at a second frequency different than the first frequency for providing second time-series data, wherein the second time-series data comprises one or more electrical parameters from the electrical domain sampled at the second frequency, and wherein the second time-series data having a second resolution different than the first resolution;
executing, by a data analysis processor receiving and responsive to the first and second time-series data, processor-executable instructions stored in a memory coupled to the data analysis processor, wherein the instructions, when executed, configure the data analysis processor for:
performing a wavelet transformation on the first and second time-series data to extract features therefrom indicating when frequency changes occur in the first and second time-series data;
converting the extracted features of at least one of the first time-series data or the second time-series data to form a combined data set having a common resolution;
identifying, from the combined data set, one or more dominant frequency changes over time in at least one of the first time-series data from the process domain or the second time-series data from the electrical domain or both;
detecting one or more instances of deviation from predefined conditions in the process domain or the electrical domain or both based on the identified one or more dominant frequency changes over time and the first and second time-series data, each of the detected one or more instances of deviation indicating an onset of a facility interruption; and
in response to detecting the one or more instances of deviation from the predefined conditions indicating the onset of the facility interruption, performing at least one automated action configured to mitigate the facility interruption.
15 . The method of claim 14 , wherein the facility interruption comprises at least one of a process perturbation in the process domain or an electrical event in the electrical domain.
16 . The method of claim 14 , wherein identifying, from the combined data set, one or more dominant frequency changes comprises analyzing at least one of: patterns, correlations, clusters, or rate of change indications therein.
17 . The method of claim 14 , wherein the at least one action configured to mitigate the facility interruption comprises at least one of: generating an alert or providing a recommendation to prevent the facility interruption from occurring.
18 . The method of claim 14 , further comprising wherein the memory stores processor-executable instructions that, when executed, further configure the data analysis processor for generating at least one corrective action configured to be taken by an operator to prevent the facility interruption from occurring.
19 . The method of claim 14 , wherein the memory stores processor-executable instructions that, when executed, further configure the data analysis processor for:
simulating operation of the facility with a digital twin thereof; and
producing, by the digital twin, simulated output parameters based on the detected one or more instances of deviation.
20 . The method of claim 19 , wherein the simulated output parameters produced by the digital twin correspond to output parameters associated with the facility.
21 . The method of claim 20 , wherein the memory stores processor-executable instructions that, when executed, further configure the data analysis processor for:
plotting the output parameters associated with the facility and the simulated output parameters on at least one parallel coordinates plot; and
identifying, from the parallel coordinates plot, an operating range for which the simulated output parameters are within an optimum range of values.
22 . The method of claim 14 , wherein converting the extracted features of at least one of the first time-series data or the second time-series data comprises synchronizing the first time-series data from the process domain and the second time-series data from the electrical domain with each other.
23 . The method of claim 14 , wherein the memory stores processor-executable instructions that, when executed, further configure the data analysis processor for combining the converted extracted features of the second time-series data from the electrical domain with the first time-series data from the process domain to form the combined data set.
24 . The method of claim 23 , wherein detecting the one or more instances of deviation from predefined conditions comprises executing machine learning on the combined data set.
25 . The method of claim 14 , further comprising generating, by a controller, a command for adjusting an operating parameter of one or more of a pump, a motor, a valve, an actuator, an electrostatic precipitator, an electrolyzer, a vibrator, or a heater.
26 . The method of claim 14 , further comprising injecting noise into at least one of the electrical domain or the process domain before performing the wavelet transformation.