Patent Application
20170024649
The invention relates generally to industrial assets and more particularly to systems and methods to provide anomaly detection for industrial assets.
An industrial asset may operate normally for substantial periods of time before a relative rare anomaly causes a malfunction of the asset. By way of example, combustors may be a critical component of gas turbine systems. Monitoring a combustor's health and, in particular, reliably detecting and/or predicting abnormal behaviors and incipient faults earlier, may be important to help ensure that gas turbines operate efficiently and help prevent costly unplanned maintenance and secondary damages to other turbine components. Using knowledge driven, manually created rules to infer an asset's health condition based on sensor data can be challenging because of, for example, low signal-to-noise ratio characteristics associated with such information. For example, a manually created rule detection process might typically result in a detection rate of approximately 55% for gas turbine combustors which may be below a desired detection rate.
It would therefore be desirable to facilitate a detection of anomalies in industrial asset operation, such as gas turbine combustor operation, in such a way so as to improve the efficiency and/or the accuracy of the process.
According to some embodiments, there may be a receipt, at a feature learning platform, of sensor data associated with normal operation of an industrial asset, the sensor data including values for a plurality of sensors over a period of time. The feature learning platform may extract a plurality of features via hierarchically deep learning, which may capture characteristics of normal operation of the industrial asset and provide the learned features to a classification modeling platform. The classification modeling platform may then create classification models utilizing the learned features, and the classification models may be executed to automatically identify a potential anomaly for an operating industrial asset.
Other embodiments are associated with systems and/or computer-readable medium storing instructions to perform any of the methods described herein.
Some embodiments disclosed herein facilitate a detection of anomalies in an operation of an industrial asset, such as gas turbine combustor operation, in such a way so as to improve the efficiency and/or the accuracy of the process. Some embodiments are associated with systems and/or computer-readable medium that may help perform such a method.
Reference will now be made in detail to present embodiments of the invention, one or more examples of which are illustrated in the accompanying drawings. The detailed description uses numerical and letter designations to refer to features in the drawings. Like or similar designations in the drawings and description have been used to refer to like or similar parts of the invention.
Each example is provided by way of explanation of the invention, not limitation of the invention. In fact, it will be apparent to those skilled in the art that modifications and variations can be made in the present invention without departing from the scope or spirit thereof. For instance, features illustrated or described as part of one embodiment may be used on another embodiment to yield a still further embodiment. Thus, it is intended that the present invention covers such modifications and variations as come within the scope of the appended claims and their equivalents.
Some embodiments described herein may automatically facilitate a detection of anomalies in an operation of an industrial asset in such a way so as to improve the efficiency and/or the accuracy of the process. For example,
Note that embodiments described herein may be applicable to many different types of industrial assets. By way of example only,
According to some embodiment, a set of thermocouple temperature sensors 100 may be arranged in a circular pattern to measure the temperate of exhaust within the turbine 10. For example the set of sensors 100 may include 27 individual sensors that each take temperature measurements (e.g., once per second). According to some embodiments, this information may be captured and used to facilitate the detections of anomalies associated with the combustor 16.
Consider, for example,
At S210, a feature learning platform may receive sensor data associated with normal operation of an industrial asset. The sensor data may include, for example, values for a plurality of sensors over a period of time and may include thermocouple sensor measurements of exhaust temperatures of the a turbine combustors (e.g., as collected by the set of sensors 100 in
At S220, the feature learning platform may automatically extract a plurality of learned features capturing characteristics of normal operation of the industrial asset. Note that the learned features might be associated with a multi-level transformation or abstraction of the sensor data. For example, the learned features might be extracted using a deep learning process associated with an auto-encoder, a de-noising auto-encoder, and/or a restricted Boltzmann machine.
At S230, the learned features may be provided to a classification modeling platform. The classification modeling platform may then create one or more classification models utilizing the learned features at S240. At S250, the classification model or models may be executed to automatically identify a potential anomaly for an operating industrial asset.
In addition to data associated with normal operation of the industrial asset, in some embodiments the system may further access and/or utilize information associated with abnormal operation of the industrial asset (e.g., instances where an anomaly such as a failure had occurred). Note that there may but much more data available in connection with the normal operation of the asset as compared to information associated with failure events. As a result, weights might be assigned to feature vectors associated with abnormal operation and the weights may be relatively more substantial as compared to weights assigned to the feature vectors associated with normal operation. The feature vectors associated with both normal and abnormal operation, and associated weights, may then be used to facilitate creation of the classification models by the classification modeling platform.
The feature learning platform 320 might, for example, apply deep neural networks for hierarchically learning salient features or signatures from a large number of unlabeled sensor data (unsupervised learning). Such learned features might capture complex relations across the thermocouple measurements and the underlying system's behavior, which may lead to more accurate and robust anomaly detection.
The detection modeling platform 330 might, for example, treat anomaly detection problems as a one-class classification problem or a two-class classification problem. For one-class classification, detection modeling platform 330 might only use so-called normal data and the model may essentially find and represent normality boundary and normality behavior and structure. Any testing points that are determined statistically to not belong to the normality described by the model may be deemed “abnormal.” In this one-class classification setting, the classification models may be built with no or a few abnormal samples. For two-class classification modeling, the detection modeling platform 330 might need both normal and abnormal samples. Since most real-world application, such as aircraft engine application, will include few abnormal samples (faulty events) and a large majority of samples will be normal. As a result, the system may address the so-called “unbalanced data” issues that will be faced in classification model building. Some embodiments described herein may use an extreme learning machine (a special type of feed-forward neural network), as the classifier and address the unbalanced data issue by giving different weights to normal and abnormal samples, respectively. Note that an extreme learning machine is only one example of a process that might be associated with a classifier, and any of the embodiments described herein might be associated with any other type of classifier, including a neural network, a support vector machine, and/or a random forest
Note that traditional approaches to industrial asset anomaly, such as combustor anomaly, detection typically rely on a set of decision rules that are applied to the raw sensor measurements. Those decision rules and their associated rule constants are empirically determined by domain or engineering experts (a knowledge-driven approach, which is not only a manual and labor intensive process, but also may have insufficient detection performance, including detection rates and false alarms). One reason for low detection performance associated with the traditional approaches is that the decision logic was executed either directly on sensor measurements or on the results of some simple algorithmic operations (e.g., addition, subtraction, and ratio) of the sensor measurements, or some combination of these two, all of which might not fully capture the underlying system behavior and structure. Another reason for low detection performance in traditional approaches is that simple decision rules (simple and transparent) might not be able to define a complex (e.g., highly non-linear) decision boundary.
In contrast, some embodiments described herein provide a combustor anomaly detection scheme that is based on more advanced machine learning technologies. Some embodiments may first finds features/signatures that best capture among-variable relations and best represent the underlying system behaviors by hierarchically learning features from unlabeled sensor measurements. The system may then apply advanced modeling techniques (e.g., using an extreme learning machine) on the learned features. Such an approach may provide an improved anomaly detection capability for gas turbine combustor systems. In addition, with the feature learning scheme, the system may automate anomaly detection design process, which may reduce the need of engineering knowledge—as well as the laborious manual efforts—in designing and implementing anomaly detection solutions.
The embodiments described herein may be implemented using any number of different hardware configurations. For example,
The processor 910 also communicates with a storage device 930. The storage device 930 may comprise any appropriate information storage device, including combinations of magnetic storage devices (e.g., a hard disk drive), optical storage devices, mobile telephones, and/or semiconductor memory devices. The storage device 930 stores a program 912 and/or a training engine 914 (e.g., associated with a training process) for controlling the processor 910. The processor 910 performs instructions of the programs 912, 914, and thereby operates in accordance with any of the embodiments described herein. For example, the processor 910 might receive sensor data associated with normal operation of an industrial asset, the sensor data including values for a plurality of sensors over a period of time. The processor 910 may extract a plurality of learned features capturing characteristics of normal operation of industrial asset and provide the learned features to a classification modeling platform. The processor 910 and/or the classification modeling platform may then create one or more classification models utilizing the learned features, and the classification model or models may be executed to automatically identify a potential anomaly for an operating industrial asset.
The programs 912, 914 may be stored in a compressed, uncompiled and/or encrypted format. The programs 912, 914 may furthermore include other program elements, such as an operating system, a database management system, and/or device drivers used by the processor 910 to interface with peripheral devices.
As used herein, information may be “received” by or “transmitted” to, for example: (i) the apparatus 900 from another device; or (ii) a software application or module within the apparatus 900 from another software application, module, or any other source.
As shown in
The engine ID 1002 might be a unique alphanumeric code identifying 1002 a gas turbine engine and the sensor ID 1004 might identify a thermocouple sensor that created the measurement at the specific time period 1006. The database includes data point 1 through N 1008, 1010, 1012, but embodiments may be associated with any number of data points. Finally, the database 1000 may include an indication 1014 as to type of operation of the turbine 1014 (e.g., whether the data is associated with normal or abnormal operation of the combustor).
Thus, some embodiments described herein may identify more salient signatures or features that best capture cross-variable relations and best represent the underlying behavior and structure of the system, which may lead to better anomaly detection performance. Moreover, embodiments may automatically create more accurate and robust detection performance and use unsupervised learning (which may help address a lack of labeled samples in real world applications). Still further, embodiments described herein may be data-driven, i.e., domain knowledge agnostic, but at the same time, may be able to easily incorporate domain knowledge. In addition, some embodiments may handle unbalanced data to address the fact that positive (i.e., faulty) samples may be relatively sparse in real-world applications.
The following illustrates various additional embodiments of the invention. These do not constitute a definition of all possible embodiments, and those skilled in the art will understand that the present invention is applicable to many other embodiments. Further, although the following embodiments are briefly described for clarity, those skilled in the art will understand how to make any changes, if necessary, to the above-described apparatus and methods to accommodate these and other embodiments and applications.
Although specific hardware and data configurations have been described herein, note that any number of other configurations may be provided in accordance with embodiments of the present invention (e.g., some of the information associated with the databases and apparatus described herein may be split, combined, and/or handled by external systems).
Applicants have discovered that embodiments described herein may be particularly useful in connection with turbine combustors used for airplanes and/or generators, although embodiments may be used in connection other any other type of industrial asset.
While only certain features of the invention have been illustrated and described herein, many modifications and changes will occur to those skilled in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the invention.
