Many systems are instrumented with various types of sensors. Such sensors provide signals that can be analyzed to detect problems with the operation of the system. For example, oil and gas wells may have flow sensors that indicate the rate of flow in the well at the location of the sensors. Detection of, and response to, an erroneous condition may help avoid a serious problem.
For a detailed description of various examples, reference will now be made to the accompanying drawings in which:
Certain terms are used throughout the following description and claims to refer to particular system components. As one skilled in the art will appreciate, computer companies may refer to a component by different names. This document does not intend to distinguish between components that differ in name but not function. In the following discussion and in the claims, the terms “including” and “comprising” are used in an open-ended fashion, and thus should be interpreted to mean “including, but not limited to . . . . ” Also, the term “couple” or “couples” is intended to mean either an indirect, direct, optical or wireless electrical connection. Thus, if a first device couples to a second device, that connection may be through a direct connection or through an indirect connection via other devices and connections.
The following discussion is directed to various embodiments of the invention. Although one or more of these embodiments may be preferred, the embodiments disclosed should not be interpreted, or otherwise used, as limiting the scope of the disclosure, including the claims. In addition, one skilled in the art will understand that the following description has broad application, and the discussion of any embodiment is meant only to be exemplary of that embodiment, and not intended to intimate that the scope of the disclosure, including the claims, is limited to that embodiment.
Many types of data have an oscillatory pattern that is normal (i.e., indicative of problem-free behavior). Such data is referred to herein as normal oscillation (NO) data. However, during various types of problem conditions, the data may become characteristic of high amplitude oscillation (HAO) or low amplitude oscillation (LAO). Data that is HAO or LAO may be indicative of various problems that can be addressed and resolved if detected in time. HAO and LAO data may have a frequency that is similar, but higher than that of NO data. HAO data may be characterized by amplitude swings that are greater than that of NO and LAO data, while the amplitude swings for LAO data may be less than that of NO and HAO data. Each of the NO, LAO and HAO data are referred to as a “regime.” The disclosed technique classifies data as NO, LAO, or HAO regime data, but the technique is applicable as well to data classification for other than a three-regime application.
An example of a system that has NO type data during normal system operation, but may become HAO or LAO during abnormal system operation is an oil/gas well. The data may be generated by flow rate sensors that are provided along the drill string. Each flow rate sensor generates a signal indicative of the rate of flow of the produced material (oil, gas). During normal well operation, the rate of flow may increase and decrease over time and at a normal level of oscillation. During certain problem conditions, the flow rate may become HAO or LAO in nature. Another example of a system that may have NO, LAO and HAO tendencies is an electrocardiogram (ECG) of a patient.
The disclosed technique involves processing of NO, LAO and HAO training data to generate a bivariate vector characteristic of each of the NO, LAO and HAO regimes. The bivariate vectors them may be used to classify “live” data as the NO, LAO, or HAO regime. Live data comprises data that is not training data for which classification is desired into one of the regimes.
The system 100 may be a standalone system or may be part of an integrated package. For example, system 100 may be a component of a data analytics system. Such a data analytics system may include various functionality. For example, the data analytics system may include a clustering engine to cluster various types of data, such as customer comments and reviews. As another example, the data analytics system may include a speech analysis engine to perform speech recognition. In some examples, the functionality of system 100 may be integrated with other functionality of the data analytics system to perform additional analysis.
A classification engine is used for classification of live data, not during the training process, and thus is not shown in
Any references herein to the operation performed by a particular engine should be understood, in at least some implementations, to be performed by the processor 150 executing the corresponding module.
Referring back to
Once the frames are determined, the amplitude feature for each frame is computed at 122 by the amplitude feature computation engine 132. The process for computing the amplitude feature is illustrated in
At 124 (
where N is the number of frames.
The bivariate vector for each of the various regimes is provided below in Table I. The mean, standard deviation, and
f
f
f
Once the bivariate vector for each regime is computed, the vectors can be used to classify live data. The classification process may be performed in real time to detect the occurrence of a problem as it is occurring.
To classify live data, the method of
Referring to
At 304, the classification engine 244 determines whether the max−min amplitude difference from 300 is greater than the HAO amplitude threshold (e.g., μA-σA based on the HAO training data) and whether the largest squared spectral coefficient is closer to the HAO frequency threshold than the other regimes' frequency thresholds. If these conditions are true, then the classification engine 244 determines at 306 that the frame's data is characteristic of the HAO regime. At 308, the system 100 may take an appropriate corrective action. The corrective action depends on the nature of the data and may include generating an alert (visual, audible, text message, email, automated phone call, etc.),
If the determination in 304 is false (i.e., the frame's data is not determined to be characteristic of the HAO regime), the classification engine 244 determines whether the data is instead characteristic of the LAO regime. At 310, the classification engine 244 determines whether the max−min amplitude difference from 300 is between the LAO amplitude threshold and the NO amplitude and whether the largest squared spectral coefficient is closer to the LAO frequency threshold than the other regimes' frequency thresholds. If these conditions are true, then the classification engine 244 determines at 312 that the frame's data is characteristic of the LAO regime. At 314, the system 100 may take an appropriate corrective action. The corrective action depends on the nature of the data and may include generating an alert (visual, audible, text message, email, automated phone call, etc.),
If the determination in 310 is false (i.e., the frame's data is not determined to be characteristic of the LAO regime), the classification engine 244 determines whether the data is instead characteristic of the NO regime. At 316, the classification engine 244 determines whether the max−min amplitude difference from 300 is less than the NO amplitude and whether the largest squared spectral coefficient is closer to the NO frequency threshold than the other regimes' frequency thresholds. If these conditions are true, then the classification engine 244 determines at 320 that the frame's data is characteristic of the NO regime. If the frame's data is not characteristic of any of the regimes, then at 318, the classification engine 244 determines the data to be characteristic of an unidentified regime.
The above discussion is meant to be illustrative of the principles and various embodiments of the present invention. Numerous variations and modifications will become apparent to those skilled in the art once the above disclosure is fully appreciated. It is intended that the following claims be interpreted to embrace all such variations and modifications.