Data analysis applications

• US 20070109301A1
• Filed: 11/17/2005
• Published: 05/17/2007
• Est. Priority Date: 11/17/2005
• Status: Active Grant

• Current Assignee: Florida Power & Light Company (NextEra Energy, Inc.)
• Original Assignee: Florida Power & Light Company (NextEra Energy, Inc.)
• Inventors: Smith, Donville
• Granted Patent: US 7,966,150 B2
• Time in Patent Office: Days
• US Class Current: 345/440
• CPC Class Codes: G05B 23/0254 based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks
• CPC Class Codes: G06T 11/206 Drawing of charts or graphs

Abstract

A system and method of monitoring equipment performance and predicting failures. The system can include a data historian that stores data for a piece of equipment and designates the data to tags. The tags can correspond to sensors that gather the data from the piece of equipment. A matrix model builder application can allow a user to generate regression models for various time spans to determine whether new data is within a normal operating range.

Claims

25-1. The method of claim 1 and further comprising providing a statistical summary of a plurality of tags from at least one of the new data and the historical data.

36. A system for monitoring equipment performance and predicting failures, the system comprising:

a data historian that stores historical data for a piece of equipment and designates the historical data with at least two input tags;

at least one sensor that gathers new data from the piece of equipment corresponding to the at least two input tags; and

a matrix model builder application that allows a user to select a first time span for the at least two input tags from the historical data, that generates a regression model including an expected output tag for the at least two input tags, that allows a user to select a second time span for the at least two input tags from the new data, and that compares the second time span from the new data to the expected output tag to determine whether the new data is within a normal operating range.

37. The system of claim 36 wherein the matrix model builder application generates a polynomial regression model including at least one of a linear regression model, a quadratic regression model, and a cubic regression model.

38. The system of claim 36 wherein the matrix model builder application generates a regression model including a standard deviation of the data and an equation of a surface of the data.

39. The system of claim 36 wherein the matrix model builder application allows a user to select and plot upper and lower bounds for the normal operating range for the piece of equipment.

40. The system of claim 39 wherein the matrix model builder application displays a percentage of the new data that is included within the upper and lower bounds.

41. The system of claim 36 wherein the matrix model builder application plots at least one of the first time span of historical data, the second time span of new data, upper bounds, and lower bounds.

42. The system of claim 36 wherein the matrix model builder application represents the historical data with squares and represents the new data with lines.

43. The system of claim 36 wherein data historian designates the historical data and the new data with a plurality of tags.

44. The system of claim 36 wherein the matrix model builder application generates a regression model for the at least two input tags, the regression model providing an expected output tag for a normal operating range for the piece of equipment.

45. The system of claim 36 wherein the matrix model builder application allows a user to alter the first time span of the historical data and generate a new regression model.

46. The system of claim 36 wherein the matrix model builder application allows a user to plot at least one of the first time span of the historical data and the second time span of the new data on at least one of a two-dimensional plot and a three-dimensional plot.

47. The system of claim 36 wherein the matrix model builder application allows a user to alter the at least two input tags to generate a new regression model with a new expected output tag.

48. The system of claim 36 wherein the matrix model builder application allows a user to generate a plurality of regression models and choose one regression model to apply to the second time span of the new data.

49. The system of claim 36 wherein the matrix model builder application allows a user to generate and save a plurality of regression models for characterizing normal performance for the piece of equipment.

50. The system of claim 36 and further comprising an equation processor application that loads the regression model, retrieves the new data, calculates an actual output using the regression model, and writes the actual output to a database.

51. The system of claim 50 wherein the equation processor application at least one of monitors the actual output of the new data in real-time and compares the actual output to the expected output tag in real-time.

52. The system of claim 36 and further comprising an alarm system application that initializes an alarm signal when an actual output of the new data varies from the expected output tag.

53. The system of claim 52 wherein the alarm system application transmits an alarm notification to at least one user based on the alarm signal.

54. The system of claim 52 wherein the alarm system application color-codes a parameter block displayed on an alarm screen based on a severity of the alarm signal.

55. The system of claim 54 wherein the alarm system application displays a plurality of color-coded parameter blocks corresponding to a plurality of parameters on the alarm screen.

56. The system of claim 55 wherein the alarm system application allows a user to select one of the plurality of color-coded parameter blocks in order to alter alarm settings for one of the plurality of parameters.

57. The system of claim 52 wherein the alarm system application allows a user to specify at least one of temporary and permanent alarm settings.

58. The system of claim 52 wherein the alarm system application automatically specifies alarm bounds by applying a set threshold at least one of above and below a startup value.

59. The system of claim 36 and further comprising a trend viewer application that allows a user to view a trend of at least one of the new data and the historical data by plotting data for a plurality of time ranges on a single plot.

60. The system of claim 59 wherein the trend viewer application provides a statistical summary of a plurality of tags from at least one of the new data and the historical data.

61. The system of claim 60 wherein the trend viewer application creates a statistical summary with at least one of a capability chart, a box plot, and a histogram.

62. The system of claim 61 wherein the trend viewer application creates a capability chart with a grid of color-coded parameters grouped by a plurality of categories, each one of the color-coded parameters including at least one of a process capability value, a mean value, and a standard deviation value.

63. The system of claim 36 wherein the at least one sensor gathers new data from at least one of a turbine, a compressor, a combustion chamber, an air preheater, a boiler feed pump, a heating element, and a cooling tower.

64. The system of claim 36 and further comprising a model viewer application that allows a user to sort a plurality of regression models by at least one of plant, system, and contact person.

65. The system of claim 64 wherein the model viewer application compares an actual output of the new data to the expected output tag and generates at least one of a deviation scaled plot and an actual value scaled plot.

66. The system of claim 36 and further comprising a crew capability application that generates a crew capability report to compare performance among various employee crews.

67. The system of claim 36 and further comprising a deviation tracker application that tracks deviations between actual outputs of the new data and expected output tags by filtering and normalizing multiple tags and by displaying multiple charts on a single screen.

68. The system of claim 67 wherein the deviation tracker application tracks deviations between actual outputs of the new data and expected output tags by filtering and normalizing multiple tags and by generating at least one of a polar plot, an Xbar-R chart, and a histogram.

69. The system of claim 67 wherein the deviation tracker application monitors at least one of quantity and duration of excursions of an actual output from the expected output tag.

70. The system of claim 67 wherein the deviation tracker application generates an excursion notification when at least one of the quantity and the duration of the excursion exceeds a threshold.