Large machinery, such as power generation equipment, is typically very expensive to purchase, install, maintain and operate. Accordingly, determining whether such equipment is operating within desired operating parameters is important. Detecting conditions that indicate that the equipment is operating outside these desired parameters, which may result in damage to the equipment is, therefore, also important. In order to detect such conditions, sensors are typically used to measure operating parameters, such as pressure, temperature, etc., of various components and, if a predetermined threshold for a particular parameter is crossed by a particular measurement, a fault is declared. Recently, learning techniques for fault detection systems have become more prevalent in attempts to improve the accuracy of determining whether a fault exists. Well-known techniques, such as neural networks, multivariate state estimation techniques (MSET) and fuzzy logic have been used for such purposes. All such methods use historical data, indicative of past normal operations and fault conditions, to monitor future data generated by operations of the equipment. If the future data deviates too much from the historical data model, an alarm is generated and a fault is declared.
While prior fault detection methods were advantageous in many implementations, they were also disadvantageous in certain regards. Specifically, these prior fault detection methods typically relied on historical data to generate estimates of the boundaries between data measurements that could be considered faults and those measurements that could be considered normal operating conditions. However, these boundary estimates were typically relatively inaccurate. Therefore, due to this inaccuracy, these methods could potentially identify system faults as normal operating conditions. Similarly, a normal operating condition could be classified as a fault simply because it was not previously observed in the historical data. Such normal, not previously observed conditions are referred to herein as out-of-range conditions.
The present inventors have invented a method and apparatus for detecting out-of-range conditions representing normal operations. Specifically, the present invention uses a Support Vector Machine (SVM), described further herein below, to generate an improved representation of historical training data from power generation equipment that facilitates a more accurate determination of the boundary between measurements that should be considered faults and those that represent normal operating conditions. In one embodiment, a method is disclosed whereby an SVM is used to receive data collected from a plurality of independent sensors associated with the power generating equipment in order to generate a boundary substantially separating a first class of data (e.g., a fault) from a second class of data (e.g., a normal operating condition) in a support vector machine feature space. Elements of operational data are collected and compared to the boundary generated from historical training data. A determination is then made whether the element of operational data is in a particular class, such as a class associated with faults.
In another embodiment, a method for detecting faults in power generation equipment is disclosed whereby a set of training data, comprising measurements of operational characteristics of said power generation equipment, is used to train a support vector machine. A class is assigned to each element of data in said training data and a boundary substantially separating different classes is generated.
As discussed above, data associated with the operation of power generation equipment can be collected by sensors placed at desired locations on that equipment. For example, where power generation equipment uses turbine engines as a component in the power generation process, pressure and temperature may be measured at various points in the engine. The sensors at these points can be classified into two separate groups: independent sensors, which measure an input to the engine, and dependent sensors, which measure operational data associated with internal engine processes or the output of the engine. For example, inlet gas flow and inlet temperature are input measurements taken by input sensors. Since input measurements are the starting point for the processes performed by the engine, input sensors are also referred to herein as “process driver sensors.” Measurements taken by input sensors cannot determine whether a fault exists with the power generation equipment as such sensors are independent of any operation of the equipment. Thus, measurements taken by input sensors can only represent in range conditions, herein defined as input conditions that have been observed previously, or out-of-range conditions, herein defined as input conditions that have not been observed previously. Dependent sensors, on the other hand, take measurements that are dependent upon the input conditions measured by the independent sensors, such as the aforementioned inlet gas flow and inlet temperature. Dependent sensors can be used to detect conditions that may indicate faults have occurred in the power generation equipment itself. For example, sensors measuring the temperature of the internal turbine blades while in operation are dependent sensors since the measurements they make will be dependent upon the inlet gas flow and temperature.
As one skilled in the art will recognize, an operating range of a particular operational characteristic of power generation equipment, such as the inlet gas flow of such power generation equipment, can be determined by estimating an upper and lower boundary representing the highest and lowest measurements of inlet gas flow taken by one or more sensors on that equipment. This range can then be used as a decision mechanism to classify future data measurements of inlet gas flow in operations. When used in conjunction with a monitoring system that functions to monitor operations of the power generating equipment, developing a range that can be used as such a decision mechanism is referred to herein as training the monitoring system. Specifically, if a measurement of the inlet gas flow of power generation equipment is outside the upper or lower boundary, then that measurement could be classified as a fault. For example, referring to
In many cases a more accurate representation of a desired operational range can be obtained by mapping the observed measurements as a function of one or more additional variables. For example,
Regardless the dimensions used, the key to enabling increased accuracy in detecting faults is to accurately determine the boundary of the area formed by the mapping of data, such as the inlet gas flow and temperature data discussed previously, from the input space (e.g., of
One illustrative embodiment of the present invention uses an SVM to develop a boundary around the data mapped into a feature space as is shown in
f(x)=hTΦ(x) (Equation 1)
where Φ(x) is a the mapping from the input space to the feature space; h is the solution vector in the feature space and, once again, T is the decision threshold. This mapping function from the input space to the feature space may be complex, depending upon the dimensionality of the feature space. However the explicit function of this mapping is unnecessary with the use of an SVM because the algorithm that finds a separating boundary in the feature space can be stated entirely in terms of vectors in the input space and dot products in the feature space. Therefore, an SVM can locate the boundary without ever representing the space explicitly, simply by defining a function, called a kernel function, that plays the role of a dot product in the feature space. This technique avoids the computational burden of explicitly representing the vectors in the potentially highly-dimensional feature space. Here, illustratively, the Gaussian kernel:
may be used as the kernel function, where xi and xj are any two vectors in the input space and σ is the width of the Gaussian kernel. Using the kernel function of Equation 2, the SVM minimizes the boundary of the volume of the decision region such that the false alarm rate is greatly reduced. Using such an SVM, out-of-range detection is also greatly enhanced. Specifically, in order to determine whether an out-of-range condition exists for the data collected at the independent sensors on power generation equipment, the evaluation function of Equation 1 is calculated and, if f(x)≧T=1, x is determined to be in range. Otherwise, x is out-of-range.
One skilled in the art will recognize that a monitoring system using an SVM such as that discussed above may be implemented on a programmable computer adapted to perform the steps of a computer program to calculate the functions of the SVM. Referring to
The foregoing Detailed Description is to be understood as being in every respect illustrative and exemplary, but not restrictive, and the scope of the invention disclosed herein is not to be determined from the Detailed Description, but rather from the claims as interpreted according to the full breadth permitted by the patent laws. It is to be understood that the embodiments shown and described herein are only illustrative of the principles of the present invention and that various modifications may be implemented by those skilled in the art without departing from the scope and spirit of the invention. Those skilled in the art could implement various other feature combinations without departing from the scope and spirit of the invention.
This patent application claims the benefit of U.S. Provisional Application No. 60/604,393, filed Aug. 25, 2004, which is hereby incorporated by reference herein in its entirety.
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