Patent Application
20140039834
The present application claims priority from Japanese Patent Application JP 2012-171156 filed on Aug. 1, 2012, the content of which is hereby incorporated by reference into this application.
The present invention relates to an equipment-condition monitoring method for detecting an anomaly at an early time on the basis of multi-dimensional time-series data output from a plant or equipment (herein after, refer to equipment) and relates to an equipment-condition monitoring apparatus adopting the method.
A power company supplies regional-heating hot water generated by making use of typically heat dissipated by a gas turbine and supplies high-pressure steam or low-pressure steam to a factory. A petrochemical company operates a gas turbine or the like as power-supply equipment. In a variety of plants and various kinds of equipment which make use of a gas turbine as described above, preventive maintenance for detecting a problem of a plant or a problem of equipment is very important for suppressing damages to society to a minimum.
There are various kinds of equipment requiring the aforementioned preventive maintenance such as monitoring aging of batteries in use and battery lives. The equipment can be equipment at apparatus and component levels. The equipment includes not only a gas turbine and a steam turbine, but also a water wheel used in a hydraulic power generation station, an atomic reactor employed in a nuclear power generation station, a wind mill used in a wind power generation station, an engine of an airplane, a heavy-machinery engine, a railroad vehicle, a rail road, an escalator and an elevator, to mention a few.
Thus, a plurality of sensors are installed in the object equipment and the object plant. Each of the sensors is used in determining whether a signal detected by the sensor is an anomaly signal or a normal signal by comparison of the detected signal with a monitoring reference for the sensor. Patent Documents 1 and 2 which are the specifications of U.S. Pat. No. 6,952,662 and U.S. Pat. No. 6,975,962 respectively disclose methods for detecting an anomaly of an object which is mainly an engine. In accordance with the disclosed methods, past data such as time-series sensor signals is stored in a database in advance. Then, the degree of similarity between observed data and the past data, which is learned data, is computed by adoption of an original method. Subsequently, inferred values are computed by linear junction of data having a high degree of similarity. Finally, differences between the inferred values and the observed data are output.
In addition, Japanese Patent Laid-open No. 2011-70635-A is used as Patent Document 3 which discloses an anomaly detection method for detecting whether or not an anomaly exists on the basis of an anomaly measure computed by comparison with a model created from past normal data.
In accordance with the anomaly detection method, the normal model is created by adoption of a local subspace technique. Non-Patent Document 1: Stephen W. Wegerich; Nonparametric modeling of vibration signal features for equipment health monitoring, Aerospace Conference, 2003, Proceedings 2003, IEEE, Volume 7, Issue 2003, Page(s): 3113-3121.
In accordance with the methods described in Patent Documents 1 and 2, normal-time data is given as learned data and, if data not included in the learned data is observed, the observed data is detected as a symptom of an anomaly. Since the anomaly detection performance is much affected by the quality of the learned data, however, it is necessary to collect normal learned data accurately and comprehensively. If it is necessary to collect such learned data for equipment having a large number of normal states, the collection of the data entails an extremely large load to be borne. In addition, even if learned data having a high quality can be collected, due to a method entailing a heavy computation load, the amount of data that can be processed within a realistic computation time is small. As a result, there are many cases in which the comprehensiveness can no longer be assured. In accordance with the method described in Patent Document 3, normal-time data is stored in advance as learned data and a normal model is created by making use of pieces of data close to measured data. Thus, an anomaly can be detected with a high degree of sensitivity. Since entire learned data must be searched, however, the computation time is long.
In order to solve the problems described above, the present invention provides an equipment-condition monitoring method adopting a sensitive and fast anomaly detection technique and an equipment-condition monitoring apparatus adopting the equipment-condition monitoring method.
In order to solve the problems described above, the present invention provides an equipment-condition monitoring method including the steps of: extracting feature vectors from sensor signals output by a plurality of sensors installed in equipment; pre-accumulating the centers of clusters obtained by clustering the extracted feature vectors and feature vectors pertaining to the clusters as learned data; extracting feature vectors from new sensor signals output by the sensors installed in the equipment; selecting a cluster for feature vectors extracted from the new sensor signals from the clusters pre-accumulated as the learned data; selecting a predetermined number of feature vectors from the feature vectors pertaining to the cluster selected from the clusters pre-accumulated as the learned data in accordance with the feature vectors extracted from the new sensor signals; creating a normal model by making use of the predetermined number of selected feature vectors; computing an anomaly measure on the basis of newly observed feature vectors and the created normal model; and determining whether the condition of the equipment is abnormal or normal on the basis of the computed anomaly measure.
In addition, in order to solve the problems described above, the present invention provides an equipment condition monitoring method including: creating learned data on the basis of sensor signals output by a plurality of sensors installed in equipment or an apparatus and accumulating the learned data; and a process of identifying anomalies of sensor signals newly output by the sensors installed in the equipment or the apparatus. The process of creating and accumulating the learned data includes: extracting feature vectors from the sensor signals; clustering the extracted feature vectors; accumulating the centers of clusters obtained by clustering the extracted feature vectors and feature vectors pertaining to the clusters as the learned data; selecting one cluster or a plurality of clusters in accordance with the extracted feature vectors from the clusters accumulated as the learned data for each of the extracted feature vectors; selecting a predetermined number of feature vectors in accordance with the extracted feature vectors from feature vectors pertaining to the selected cluster and creating a normal model by making use of the predetermined number of selected feature vectors and pertaining to the selected cluster; computing an anomaly measure on the basis of feature vectors and the created normal model; and computing a threshold value on the basis of the computed anomaly measure. On the other hand, the process of identifying anomalies of the sensor signals has: extracting feature vectors from newly observed sensor signals; selecting one cluster or a plurality of clusters in accordance with the newly observed feature vectors from the clusters accumulated as the learned data; selecting a predetermined number of feature vectors in accordance with the newly observed feature vectors from feature vectors pertaining to the selected cluster and creating a normal model by making use of the predetermined number of selected feature vectors and pertaining to the selected cluster; computing an anomaly measure on the basis of newly observed feature vectors and the created normal model; and determining whether a sensor signal is abnormal or normal on the basis of the computed anomaly measure and a threshold value.
In addition, in order to solve the problems described above, the present invention provides an equipment-condition monitoring method including: creating learned data on the basis of sensor signals output by a plurality of sensors installed in equipment or an apparatus and accumulating the learned data; and identifying anomalies of sensor signals newly output by the sensors installed in the equipment or the apparatus. The process of creating and accumulating the learned data includes: classifying operating conditions of the equipment or the apparatus into modes on the basis of event signals output from the equipment or the apparatus; extracting feature vectors from the sensor signals; clustering the extracted feature vectors; accumulating the centers of clusters obtained by clustering the extracted feature vectors and feature vectors pertaining to the clusters as the learned data; selecting one cluster or a plurality of clusters in accordance with the extracted feature vectors from the clusters accumulated as the learned data for each of the extracted feature vectors; selecting a predetermined number of feature vectors in accordance with the extracted feature vectors from feature vectors pertaining to the selected cluster and creating a normal model by making use of the predetermined number of selected feature vectors and pertaining to the selected cluster; computing an anomaly measure on the basis of the extracted feature vectors and the created normal model; and computing a threshold value for each of the modes on the basis of the computed anomaly measure. On the other hand, the process of identifying anomalies of the sensor signals has: classifying operating conditions of the equipment or the apparatus into modes on the basis of event signals; extracting feature vectors from newly observed sensor signals; selecting one cluster or a plurality of clusters in accordance with newly observed feature vectors from the clusters accumulated as the learned data; selecting a predetermined number of feature vectors in accordance with the newly observed feature vectors from feature vectors pertaining to the selected cluster and creating a normal model by making use of the predetermined number of selected feature vectors and pertaining to the selected cluster; computing an anomaly measure on the basis of the newly observed feature vectors and the created normal model; and determining whether a sensor signal is abnormal or normal on the basis of the computed anomaly measure, the mode and a threshold value computed for the mode.
In addition, in order to solve the problems described above, the present invention provides an equipment-condition monitoring apparatus for monitoring the condition of equipment on the basis of sensor signals output by a plurality of sensors installed in the equipment. The equipment-condition monitoring apparatus includes: a raw-data accumulation section configured to accumulate the sensor signals output by the sensors installed in the equipment or the object apparatus; a feature-vector extraction section configured to extract feature vectors from the sensor signals; a clustering section configured to cluster the feature vectors extracted by the feature-vector extraction section; a learned-data accumulation section configured to accumulate the centers of clusters obtained as a result of the clustering carried out by the clustering section and feature vectors pertaining to the clusters as learned data; a cluster selection section configured to select a cluster in accordance with feature vectors extracted by the feature-vector extraction section from the learned data accumulated by the learned-data accumulation section; a normal-model creation section configured to select a predetermined number of feature vectors in accordance with feature vectors extracted by the feature-vector extraction section among feature vectors pertaining to a cluster selected by the cluster selection section and create a normal model by making use of the predetermined number of selected feature vectors; an anomaly-measure computation section configured to compute an anomaly measure on the basis of the predetermined number of feature vectors and the normal model created by the normal-model creation section; a threshold-value setting section configured to set a threshold value on the basis of an anomaly measure computed by the anomaly-measure computation section as an anomaly measure of a feature vector included in the learned data accumulated in the learned-data accumulation section; and an anomaly determination section configured to determine whether the condition of the equipment or the condition of the object apparatus is abnormal or normal by making use of the anomaly measure computed by the anomaly-measure computation section and the threshold value set by the threshold-value setting section.
In accordance with the present invention, in the determination of an anomaly of newly observed data, a normal model is created by making use of predetermined pieces of learned data and existing in close proximity to that of the observed data. Thus, the anomaly can be detected with a high degree of sensitivity. To put it in detail, the learned data is clustered in advance and a normal model is created by selecting predetermined pieces of data from data pertaining to a cluster selected in accordance with a newly observed feature vector. Thus, the data-piece count representing the number of pieces of data serving as a search object can be made small so that the computation time can be reduced substantially.
As described above, it is possible to implement a system capable of detecting an anomaly in various kinds of equipment and a variety of components with a high degree of sensitivity and a high speed by carrying out preventive maintenance such as monitoring aging of batteries in use and battery lives. The equipment can be equipment at apparatus and component levels. The equipment includes not only a gas turbine and a steam turbine, but also a water wheel used in a hydraulic power generation station, an atomic reactor employed in a nuclear power generation station, a wind mill used in a wind power generation station, an engine of an airplane, a heavy-machinery engine, a railroad vehicle, a rail road, an escalator and an elevator, to mention a few.
These features and advantages of the present invention will be apparent from the following more particular description of preferred embodiments provided by the invention as illustrated in the accompanying drawings.
The present invention is an invention for solving a problem that the computation time is long due to the fact that it is necessary to search the entire learned data for data close to newly observed data in case-based anomaly detection in equipment of a plant or the like. Thus, the present invention provides a method and an apparatus. In the method and the apparatus, feature vectors are extracted from sensor signals output by a plurality of sensors installed in the equipment or an object apparatus. Then, the extracted feature vectors are clustered. Subsequently, the centers of clusters obtained as a result of the clustering and feature vectors pertaining to the clusters are accumulated in advance as the learned data. When newly observed data is received, data pertaining to a cluster close to the newly observed data is selected from the learned data. Then, a normal model is created from the selected data and an anomaly measure is computed. Subsequently, a threshold value is determined and an anomaly measure is computed from the newly observed data and the normal model. Then, the anomaly measure is compared with the threshold value in order to detect an anomaly of the equipment. By clustering and storing the learned data in advance and by creating a normal model through the use of the learned data pertaining to a cluster close to the newly observed data as described above, the time it takes to search for data can be shortened and an anomaly of the equipment can be diagnosed in a short period of time
The present invention is explained in detail by referring to diagrams as follows.
As shown in the figure, the system includes a sensor-signal accumulation section 103, a feature-vector extraction section 104, a clustering section 105, a learned-data accumulation section 106, a cluster select section 107, a normal-model creation section 108, an anomaly-measure computation section 109, a threshold-value computation section 110 and an anomaly determination section 111. The sensor-signal accumulation section 103 is a section configured to accumulate sensor signals 102 output from equipment 101. The feature-vector extraction section 104 is a section configured to extract a feature vector on the basis of a sensor signal 102. The clustering section 105 is a section configured to cluster feature vectors. The learned-data accumulation section 106 is a section configured to accumulate learned data on the basis of a clustering result. The cluster select section 107 is a section configured to select a cluster close to newly observed data from the accumulated learned data. The normal-model creation section 108 is a section configured to search learned data pertaining to a selected cluster for as many pieces of data close to observed data as specified by a predetermined number and to create a normal model by making use of the pieces of data. The anomaly-measure computation section 109 is a section configured to compute an anomaly measure of newly observed data on the basis of the normal model. The threshold-value computation section 110 is a section configured to compute a threshold value on the basis of an anomaly measure of learned data. The anomaly determination section 111 is a section configured to determine whether newly observed data is normal or abnormal.
The operation of this system has two phases referred to as learning and anomaly detection. At the learning phase, learned data is created by making use of accumulated data and saved. At the anomaly-detection phase, on the other hand, an anomaly is actually detected on the basis of an input signal. Basically, the learning phase is offline processing whereas the anomaly-detection phase is online processing. However, the anomaly-detection phase can also be carried out as offline processing. In the following description, the technical term “learning process” is used to express the learning phase whereas the technical term “anomaly-detection process” is used to express the anomaly-detection phase.
The equipment 101 serving as the object of condition monitoring represents equipment such as a gas turbine or a steam turbine and represents a plant. The equipment 101 outputs the sensor signal 102 representing the condition of the equipment 101. The sensor signal 102 is accumulated in the sensor-signal accumulation section 103.
Next, the flow of a learning process is explained by referring to
First of all, by referring to
Next, each of the steps described above is explained in detail as follows.
At step S302, every sensor signal is carried out a canonicalization. For example, on the basis of a specified period average and a standard deviation, every sensor signal is converted into a canonical signal to give an average of 0 and a variance of 1. The average of the sensor signals and the standard deviation of the sensor signals are stored in advance so that the same conversion can be carried out in the anomaly-detection process. As an alternative, by making use of maximum and minimum values of specified periods of the sensor signals, every sensor signal is converted into a canonical signal to give a maximum value of 1 and a minimum value of 0. As another alternative, in place of the maximum and minimum values, it is also possible to make use of upper and lower limits determined in advance. In the case of these alternatives, the maximum and minimum values of the sensor signals or the upper and lower limits of the sensor signals are stored in advance so that the same conversion can be carried out in the anomaly-detection process. In the conversion of the sensor signals into canonical ones, sensor signals having different units and different scales can be handled at the same time.
At step S303, a feature vector is extracted at each time. It is conceivable that sensor signals each completing the conversion into a canonical one are arranged as they are. However, for a certain time, a window of ±1, ±2, . . . and so on can be provided. Then, by making use of a product of a window width (3, 5, . . . and so on)×as many feature vectors as sensors, it is also possible to extract a feature representing a time change of the data. In addition, the DWT (Discrete Wavelet Transform) can also be carried out in order to disassemble a sensor signal into frequency components. In addition, at step S303, a feature is selected. As minimum processing, it is necessary to exclude a sensor signal having a very small variance and a monotonously increasing sensor signal.
In addition, it is conceivable that a signal invalidated by a correlation analysis is deleted by adoption of a method described as follows. If a correlation analysis carried out on a multi-dimensional time-series signal indicates very high similarity, that is, if the correlation analysis indicates that a plurality of signals having a correlation value close to 1 exist for example, the similar signals are considered to be redundant signals or the like. In this case, overlapping signals are deleted from the similar signals, leaving only remaining signals which do not overlap each other. As an alternative, the user may also specify signals to be deleted. In addition, it is conceivable that a feature with a large long-term variation is excluded. This is because the use of a feature with a large long-term variation tends to increase the number of normal conditions and gives rise of insufficient learned data. For example, it is possible to compute an average and a variance once a periodical interval and infer the magnitude of the long-term variation on the basis of changes of the average and the variance. In addition, at step S303, the number of signal dimensions can be reduced by adoption of any one of a variety of feature conversion techniques including a principal component analysis, an independent-component analysis, a non-negative matrix factorization, latency structure projection or a canonical correlation analysis, to mention a few.
At step S304, an initial position of the center of each cluster is set. The flow of processing carried out at this step is explained by referring to
Generally, in many cases, in the first center position setting of the clustering, the first center position is set at random. Also in the present invention, the first center position can be set at random as well. In equipment with operation or termination changeovers, however, the amount of data in a transient state is smaller than the amount of data in a steady state. Thus, if the first center position is selected at random, data in a transient state is difficult to be selected. In this case, the effect of the data in a transient state on the computation of the cluster center unavoidably becomes relatively smaller. The method for setting an initial position of a cluster center as described above is provided to serve as a method for setting cluster-center initial positions which are separated from each other by a long distance. Thus, the number of transient-state clusters can be raised.
At step S305, clustering is carried out. Processing flows of the clustering are explained by referring to
If the result of the determination carried out at step S603 is NO indicating that the number of members included in the cluster of interest is not smaller than the specified number of members, on the other hand, the flow of the processing goes on to step S606 at which one cluster is added. Then, at the next step S607, the members included in the cluster of interest are apportioned to the cluster of interest and the added cluster. To put it in detail, one feature vector is selected at random from members of the cluster of interest and used as the first center position of the cluster to be added. Then, the two clusters are created by adoption of the k averaging method explained before by referring to
At step S306, adjustment is carried out to set the number of members included in a cluster at a fixed value. The processing flow of this adjustment is explained by referring to
If the result of the determination carried out at step S702 is NO indicating that the number of members actually included in a cluster is equal to or greater than the specified number of members, on the other hand, the flow of the processing goes on directly to step S704 by skipping step S703. At step S704, the number of members actually included in the cluster is compared with the specified number of members in order to determine whether or not the number of members actually included in the cluster is greater than the specified number of members. If the result of the determination carried out at step S704 is YES indicating that the number of members actually included in a cluster is greater than the specified number of members, the flow of the processing goes on to step S705 at which the cluster is thinned out by reducing the number of members actually included therein so that the number of actually included members becomes equal to the specified number of members. Then, at the next step S706, the processing is terminated. In the operation to thin out the cluster, members to be eliminated can be determined at random. A cluster having a great number of members indicates that the density of feature vectors in a feature space is high so that, even if some members determined at random are eliminated, no big difference is resulted in. This operation is carried out for the purpose of reducing the number of search objects used at a close-data search time in creation of a normal model so that the normal model can be created at a higher speed. If the result of the determination carried out at step S704 is NO indicating that the number of members actually included in a cluster is equal to or smaller than the specified number of members, on the other hand, the flow of the processing skips step S705, going on directly to step S706 at which the processing is terminated. The processing described above is carried out for every cluster. If the processing represented by the flowchart shown in
Next, by referring to
Then, at the next step S805, the normal-model creation section 108 selects feature vectors the number of which has been specified in advance from feature vectors of a group different from the feature vector of interest in an increasing-distance order starting with a feature vector closest to the feature vector of interest. Each of the feature vectors is a member of a selected cluster. Then, at the next step S806, a normal model is created by making use of these feature vectors. Subsequently, at the next step S807, an anomaly measure is computed on the basis of a distance from the feature vector of interest to the normal model. Then, the flow of the processing goes on to the next step S808 to determine whether or not the anomaly-measure computation described above has been carried out for all feature vectors. If the result of the determination carried out at step S808 is YES indicating that the anomaly-measure computation described above has been carried out for all feature vectors, the flow of the processing goes on to step S810 at which the threshold-value computation section 110 sets a threshold value on the basis of the anomaly measures of all the feature vectors. If the result of the determination carried out at step S808 is NO indicating that the anomaly-measure computation described above has not been carried out for all feature vectors, on the other hand, the flow of the processing goes on to step S809 at which attention is paid to the next feature vector. Then, the flow of the processing goes back to step S804 in order to repeat the operations of steps S804 to S808.
Conceivable methods for creating a normal model include an LSC (Local Sub-space Classifier) and a PDM (Projection Distance Method).
The LSC (Local Sub-space Classifier) is a method for creating a (k−1)-dimensional affine subspace by making use of k adjacent vectors close to an attention vector q.
In order to find the point b from the interest drawing vector q and the k adjacent vectors xi (i=1, . . . , k) close to the attention vector q, a correlation matrix C is found from a matrix Q obtained by arranging k attention vectors q and a matrix X obtained by arranging the adjacent vectors xi in accordance with Eq. (1) given as follows:
(1)
Then, the point b is found in accordance with Eq.
(2) given as follows:
(2)
The processing described so far is the normal model creation carried out at step S806.
Since the anomaly measure d is a distance between the interest drawing vector q and the point b, the anomaly measure d is expressed by the following equation:
(3)
As described above, a case for k=3 is explained by referring to
The projection distance method is a method for creating a subspace having an independent origin for a selected feature vector. That is to say, the projection distance method is a method for creating an affine subspace (or a variance maximum subspace). The feature-vector count specified at step S805 can have any value. If the feature-vector count representing the number of feature vectors is too large, however, it undesirably takes long time to select a feature vector and compute a subspace. Thus, a proper feature-vector count is a number in a range of several tens to several hundreds.
The method for computing an affine subspace is explained as follows. First of all, the average p of selected feature vectors and a covariance matrix E are computed. Then, an eigenvalue problem is solved to calculate r eigenvalues. The number r is a number determined in advance. Then, the r eigenvalues are arranged in a decreasing order starting with the largest one and a matrix U is created by arranging eigenvector for the r arranged eigenvalues. Subsequently, the matrix U is taken as the orthonormal base of the affine subspace. The number r is a number smaller than the number of dimensions of the feature vector and smaller than the selected-data count. As an alternative, instead of setting the number r at a fixed value, the number r may be set at a value which is obtained when a contribution ratio cumulated in a direction from the large one of the eigenvalues exceeds a ratio determined in advance. The anomaly measure is the distance of projection onto the affine subspace of the vector of interest.
As another method, it is possible to adopt a local average distance method, a Mahalanobis-Taguchi method, a Gaussian process method or the like. In accordance with the local average distance method, the distance to an average vectors of the k adjacent vectors close to the attention vector q is taken as the anomaly measure.
Next, the following description explains a method for setting a threshold value at step S810. First of all, a histogram is created. The histogram is a frequency distribution of the anomaly measure for all feature vectors. Then, a cumulative histogram is created on the basis of the frequency distribution and a value attaining a ratio close to 1 specified in advance is found. Subsequently, processing is carried out to compute a threshold value. The processing includes an operation to add an offset by taking this value as a reference in order to give a multiple. If the offset is 0 and the multiple is 1, the value is taken as the threshold value. The computed threshold value is saved by associating the threshold value with the learned data as shown in none of the figures.
Next, the processing flow of the anomaly-detection process is explained by referring to
Then, at the next step S1004, the cluster select section 107 selects a cluster having the cluster center closest to the evaluation vector from the centers of clusters and members of the clusters. As an alternative, the cluster select section 107 selects a plurality of clusters specified in advance in an increasing-distance order starting with the closest cluster. As described before, the cluster centers and the cluster members have been stored as learned data. Then, at the next step S1005, the normal-model creation section 108 selects a plurality of feature vectors, the number of which has been specified in advance, in an increasing-distance order starting with a feature vector closest to the evaluation vector, from feature vectors each serving as one of members of the selected cluster. Subsequently, at the next step S1006, the normal-model creation section 108 creates a normal model by making use of the selected feature vectors. Then, at the next step S1007, the anomaly-measure computation section 109 computes an anomaly measure on the basis of the distance from the evaluation vector to the normal model. Subsequently, at the next step S1008, the anomaly determination section 111 compares the anomaly measure with the threshold value computed in the learning process in order to determine whether the anomaly measure is normal or abnormal. That is to say, if the anomaly measure is greater than the threshold value, the anomaly measure is determined to be abnormal. Otherwise, the anomaly measure is determined to be normal.
The above description explains an embodiment of a method for detecting an anomaly on the basis of sensor signals output from equipment. Another embodiment described below implements another method for detecting an anomaly by making use of event signals output from the equipment. In accordance with this other embodiment, on the basis of the event signals, the operating conditions of the equipment are classified into modes each representing one of the operating conditions. In addition, the processing is carried out at step S810 to set a threshold value in accordance with the mode of the equipment. On top of that, the processing carried out at step S1008 to determine whether the anomaly measure is normal or abnormal makes use of a threshold value set for the mode.
Except for the differences described above, the other method is entirely identical with the method explained before. Thus, by referring to
In order to cut out a sequence, the start event of the sequence and the end event of the sequence are specified in advance. Then, the sequence is cut out while scanning the event signal under the following conditions from the head of the event signal to the tail of the signal:
(1): In the case of the outside of a sequence, a start event is searched for. If a start event is found, the start event is taken as the start of the sequence.
(2): In the case of the inside of a sequence, an end event is searched for. If an end event is found, the end event is taken as the end of the sequence. The end events include not only a specified end event, but also a failure, a warning and a specified start event.
By making use of event information as described above, a variety of operating conditions can be identified with a high degree of accuracy. In addition, by setting a threshold value for every mode, an anomaly can be detected with a high degree of sensitivity in the steady-state off mode 1111 and the steady-state on mode 1113 even if it is necessary to lower the degree of sensitivity due to a lack of learning data in the activation mode 1112 and the termination mode 1114 which are each a transient-state mode.
A clustering-parameter setting window 1208 is a window for inputting a cluster count specified in processing carried out by the clustering section 105 and the number of members included in every cluster. In addition, a check button is used to indicate whether or not the processing to re-divide a cluster as explained before by referring to
A threshold-value setting parameter input window 1210 is a window for specifying how the group of cross-validation in the processing explained before by referring to
After all the information has been input, a test-period input window 1212 inputs a test-object period. Then, when a test button 1213 is pressed, a recipe test is carried out. By carrying out this operation, the sequence number of a test carried out under the same recipe name is taken. Then, the feature-vector extraction section 104 extracts feature vectors from sensor signals 102 in the specified learning period and the clustering section 105 clusters the feature vectors. Subsequently, the learned-data accumulation section 106 stores cluster centers and cluster members by associating with the recipe name and the test sequence number.
In the processing carried out at step S302 of the flowchart explained before by referring to
A sensor is specified by entering an input to a sensor-name select window 1308. Before the user specifies a sensor, however, the first sensor has been selected as a default. A cursor 1309 indicates the origin point of an enlarged display. The cursor 1309 can be moved by operating a mouse. The date of the position of the cursor 1309 is displayed on a date display window 1310. When an end button 1311 is pressed, the all-result display screen 1301, the result enlarged-display screen 1302 and the cluster-information display screen 1303 are erased to finish the display.
By making use of the sensor-name select window 1308, a cluster select number can also be selected.
A cluster-distribution display window 1316 displays learned data and each cluster center which are plotted on a scattering diagram of first and second main components. In the example shown in the figure, the learned data is represented by dots whereas the cluster centers are each represented by a triangle. By making use of these graphs, it is possible to check whether or not the clusters are scattered everywhere all over the entire learned data. If few cluster centers exist in a portion having a low data density, it is possible to determine that the number of clusters is not sufficient. In addition, test data can be displayed by superposing the test data on the existing ones. On top of that, even though this example displays two-dimensional main components, a three-dimensional display is also possible. Furthermore, it is possible to adopt a method whereby any two or three sensors are selected in order to plot the learned data and each cluster center so as to show distributions of learned data and each cluster center in a feature space.
A cluster-member count display window 1317 displays cluster-member counts prior to the member-count adjustment processing explained before by referring to
When the end button 1311 on any one of the displays shown in
A register button 1216 is pressed in order to register the information by associating the information with the name of the recipe and terminate the operations. The information has been stored in advance by associating the information with the name of the recipe and a test number displayed on the test-number display window 1214. If a cancel button 1217 is pressed, the operations are terminated by storing nothing.
In addition, if a test-result list button 1218 is pressed, a test-result list display screen 1401 shown in
In the example shown in
Registered recipes are managed by attaching a label to each of the recipes to indicate that the recipe is active or inactive. For newly observed data, by making use of information of an active recipe with a matching apparatus ID, processing from the step S1003 to the step S1008 is carried out and results of the processing are stored in advance by associating the results with the name of the recipe. As described before by referring to
Next, by referring to
In the embodiments described above, learned data is set in an off-line manner, the anomaly detection processing is carried out in a real-time manner and results are displayed in an off-line manner. However, the results can also be displayed in a real-time manner. In this case, it is sufficient to provide a configuration in which the length of the display period, the recipe to serve as a display object and information to serve as a display object are determined in advance and most recent information thereon may be displayed at fixed intervals.
Conversely, the scope of the present invention includes a configuration having additional functions to set an arbitrary period, select an arbitrary recipe and carry out anomaly determination processing in an off-line manner.
The embodiments described above are merely implementations of the present invention and, thus, the present invention is by no means limited to the embodiments. That is to say, the scope of the present invention also includes a configuration in which some of the steps explained in the embodiment are replaced by steps (or means) having equivalent functions and a configuration in which some non-essential functions are eliminated.
The present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the present invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meanings and range of equivalency of the claims are therefore intended to be embraced therein.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2012-171156 | Aug 2012 | JP | national |
