Patent Application
20090030752
The systems and methods described herein relate generally to identifying outlying data in small sets of data. More specifically, the systems and methods relate to statistical techniques to quantify outlying engineering or operational data when compared to small sets of related engineering or operational data.
In the operation and maintenance of power generation equipment (e.g., turbines, compressors, generators, etc.), sensor readings corresponding to various attributes of the machine are received and stored. These sensor readings are often called “tags”, and there are many types of tags (e.g., vibration tags, efficiency tags, temperature tags, pressure tags, etc.).
Close monitoring of these tags across time has many benefits in understanding machine deterioration characteristics (e.g., internal damage to units, compressor events, planned vs. unplanned trips). For example, increasing values (over time) of rotor vibration in a compressor, may be an indication of a serious problem. Better knowledge of deterioration in machines also improves fault diagnostic capability via a set of built-in rules or alerts that act as leading indicators for machine events. Simultaneous display of all tag anomalies together with the designed rules-alerts makes machine monitoring and diagnostics, as well as, new rule/alert creation, extremely efficient and effective. Individuals responsible for monitoring and diagnostics can have their immediate attention directed to critical deviations.
However, there is a considerable amount of noise in sensor data. To remove noise and make observations comparable across time or across machines, many different corrections need to be made and many different controlling factors need to be used. Even then, it is still very hard to simultaneously monitor many tags (there can be several hundred to thousands of tags) and diagnose the anomalies in the data.
Removing the noise from data and catching or identifying anomalies in a usable format (e.g., magnitude and direction) and then using that anomaly information in rule or model building is a needed process in many different businesses, technologies and fields. In engineering applications, monitoring and diagnostic teams typically address the problem in routine and ad-hoc fashion via control charts, histograms, and scatter plots. However, this approach necessitates a subjective assessment as to whether a given tag is anomalously high or low.
There are known statistical techniques including z-scores to evaluate the degree to which a particular value in a group is an outlier, that is, anomalous. Typical z-scores are based upon a calculation of the mean and the standard deviation of a group. While a z-score can be effective in evaluating the degree to which a single observation is anomalous in a well populated group, z-scores have been shown to lose their effectiveness as an indication of anomalousness when used on sets of data that contain only a small number of values.
When calculating anomaly scores, it is often the case that there are only a few values with which to work. For instance, when comparing a machine (e.g., a turbine) to a set of peer machines (e.g., similar turbines), it is often the case that it is difficult to identify more than a handful of machines that can legitimately be considered peers of the target machine. In addition, it is often desirable to evaluate the performance of machines that may only have been in operation under the current configuration for a limited period of time. As a result, it is often not desirable or accurate to use standard z-scores as a measurement for anomaly scores since standard z-scores are not robust with small datasets.
Accordingly, a need exists in the art for a process, method and/or tool that can easily identify, quantify, and display anomalies experienced by various types of power generation equipment. Also, this process, method and/or tool should allow anomaly information to be turned into meaningful knowledge such as leading indicators to events of interest.
The invention provides a method for determining whether an operational metric representing the performance of a target machine has an anomalous value. The method comprises the steps of collecting operational data from at least one machine, and calculating an exceptional anomaly score from the operational data.
Additionally, the invention provides a method for determining whether an operational metric representing the performance of a target machine has an anomalous value. The method comprises the steps of: collecting operational data from at least one machine; calculating at least one exceptional anomaly score from operational data; aggregating the operational data; creating at least one sensitivity setting for the exceptional anomaly score; creating at least one alert, where the alert is based on the exceptional anomaly score and/or the operational data; creating at least one heatmap. The heatmap visually illustrates the exceptional anomaly score and/or the operational data.
Further, the invention provides a method for determining whether an operational metric representing the performance of a target machine has an anomalous value. The method includes the steps of collecting operational data from at least one machine; calculating at least one exceptional anomaly score from obtained operational data; aggregating the obtained operational data; creating at least one sensitivity setting for the at least one exceptional anomaly score; creating at least one alert, where the alert is based on the exceptional anomaly score and/or the operational data; and creating at least one heatmap. The heatmap visually illustrates the exceptional anomaly score and/or the operational data.
In monitoring and diagnostics (M&D), eliminating noise from data is a key concept. It becomes non-trivial when there are a lot of variables that need to be monitored simultaneously per second and even more so when condition adjustment (e.g., temperature, operating mode, pressure, etc.) is required. An anomaly detection process and heatmap tool is herein described that is highly useful and revolutionary for monitoring and diagnostics. The process and tool, as embodied by the present invention, is particularly useful when applied to power generation equipment, such as, compressors, generators and turbines. However, the process and tool can be applied to any machine or system that needs to be monitored. The process and tool comprises five main features:
(1) Calculating exceptional anomaly scores (EAS) for engineering data, (e.g., operational sensor data). Exceptional anomaly scores quantify outlying data when compared to small sets of related data. EAS outperforms Z-score and control chart statistics in identifying anomalous observations.
(2) Creating multiple sensitivity settings for the exceptional anomaly scores so that users can define which percentage of the data they can effectively and efficiently monitor across a given set of tags and time points. Moreover, these different sensitivity settings can be used to add diagnostics, (e.g., alert creation).
(3) Providing methodologies for aggregating various anomalous observations at different data granularities, (e.g., hourly vs. daily anomalous observations). These different anomalous observations can be interlinked and transferable to one another. An anomalous hourly observation may propagate up to a daily anomalous observation.
(4) Creating alerts. These alerts are rule-based triggers that may be defined by the end-user or provided based on analytical means to identify events (e.g., compressor events) with lead-time. Alerts are based on exceptional anomaly scores and raw sensor data. Alerts may also make use of sensitivity setting adjustments and aggregation properties of exceptional anomaly scores.
(5) Creating heatmaps that turn data into knowledge. A heatmap is an outlier-detection-visualization tool that can be performed on each specified machine unit for a large number of selected tags across many different time points. A heatmap illustrates the anomaly-intensity and the direction of a ‘target observation.’ A heatmap may also contain a visual illustration of alerts, and directs immediate attention to hot-spot sensor values for a given machine. Heatmaps can also provide comparison to peers analysis, which allows the operational team to identify leaders and lagers, as well as marketing opportunities on the fly with great accuracy across different time scales (e.g., per second, minute, hour, day, etc.).
Calculating Exceptional Anomaly Scores
In order to account for unit/machine and environmental variations and determine whether or not a given value for a tag for a target unit is outside an expected range (i.e., anomalous), context information may be used to form a basis for the analysis of the target unit's tag data. This context information can be taken from two primary sources: the target unit's past performance, and the performance of the target unit's peers. By using such context information to quantify the typical amount of variation present within the group or within the unit's own performance, it is possible to systematically and rigorously compare current tag data to context data and accurately assess the level of anomalous data in the target unit's tag values.
As noted above, context information is used to properly evaluate the degree to which a given tag is anomalous. In order to have an effective evaluation, the context data must be properly selected. When selecting the appropriate context data over the time domain, it is generally desirable to look at the closest data available to the time period of interest. Since the time period of interest is usually the most recent data available, the appropriate scope of time to consider is a sequence of the most recent data available for the unit—for example, the data corresponding to the last two calendar weeks. This mitigates the influence of seasonal factors.
Proper context data to take into account the behavior of the group and overall environment is found by using an appropriate group of ‘peer’ units to the target unit. For example, a group of turbines with the same frame-size and within the same geographic region are selected to act as the appropriate peer group for the target turbine.
In addition to the context considerations stated above, context data also includes comparable operating conditions. For this implementation, and as one example only, comparable operating conditions can be defined to mean any time period in the past where the unit has the same OPMODE, DWATT and CTIM values within a window of 10. OPMODE can be defined as the operation mode (e.g., slow cranking, peak output, 50% output, etc.). DWATT can be a metric for power (e.g., megawatt output). CTIM can be defined as a temperature metric (e.g., inlet temperature). For example, if the target observation's value of OPMODE is equal to 1 and DWATT is equal to 95, only the historical periods where OPMODE=1 and DWATT was between 90 and 100 could be used. These comparable operating conditions are defined as part of the system configuration.
By establishing the appropriate context, both in time, geography, frame size, and operating conditions, the need for a subjective assessment as to whether a given tag is anomalously high or low can be avoided, and objective and automatic calculations can be made to detect and quantify anomalies. To calculate the Z-Within (comparison to past) exceptional anomaly scores, we can use 10-15 historical observations where the unit was operating under comparable conditions (as defined above). These historical observations can be used to calculate an average and standard deviation. The z-score can then be calculated of the target observation using the historical observations' average and standard deviation. The minimum and maximum number of observations used for the calculation of Z-Within exceptional anomaly score is defined as part of the system configuration. Z-Within provides a comparison of a specific machine's current operating condition to the machine's prior operating condition. The equation used to calculate Z-Within may be generally of the form:
For each unit, up to 8 or more other units with the same frame-size with similar configurations and in the same geographic region can be identified as peers. The Z-Between exceptional anomaly score is an indication of how different a specific unit or machine is from its peers. For example, an F-frame gas turbine compared to other similar F-frame gas turbines. To calculate the Z-Between exceptional anomaly scores (comparison to peers), one can select the single most-recent observation from each of the peers where the peer is operating under comparable condition (as defined above). This results in up to 8 or more peer observations with which to calculate an average and standard deviation. The z-score of the target unit using the peer group's average and standard deviation can then be calculated. The minimum and maximum number of observations used for the calculation of Z-Between exceptional anomaly score is defined as part of the system configuration. The equation used to calculate Z-Between may be generally of the form:
Note that it is the case that a value can be either anomalously high, or anomalously low. While there generally is a particular direction that is recognized as being the preferable trend in a value (e.g., it is generally better to have low vibrations than high vibrations), it should be noted that this technique is designed to identify and quantify anomalies regardless of their polarity. In this implementation, the direction does not indicate the “goodness” or “badness” of the value. Instead, it represents the direction of the anomaly. If the exceptional anomaly score is a high negative number compared to the past, it means the value is unusually low compared to the unit's past. If the exceptional anomaly score is a high positive number, it means the value is unusually high compared to the unit's past. The interpretation is similar for peer anomaly scores. The anomaly direction of the individual tags can be defined as part of the system configuration.
By using these techniques to detect anomalies, alerts can be created. An alert can be a rule-based combination of tag values against customizable thresholds.
Creating Multiple Sensitivity Settings
For exceptional anomaly scores, a conversion between the scores and the percent tail calculations can be performed. Specifically, a range of magnitudes of exceptional anomaly scores will correspond to a range of percentages of the anomaly distribution given the distribution of the raw metric. Via this conversion, an analyst can pick the exceptional anomaly score cut off values that indicate ‘alarms’ or ‘red flags’ for the raw metrics. In addition, it provides an ease of use for the end-user who can freely decide what percentage is high enough to be named as an ‘anomaly.’ Moreover, via this conversion the ‘anomaly’ definition can be easily changed from application to application, business to business or metric to metric as needed.
For example, in a turbine or compressor the sensor data may comprise over 300 different tags with many different shapes of distributions. A sensitivity analysis is needed to see whether the same cut off values can be used across tags or whether different cut off values are needed for different tags. In other words, how robust the conversion tables are across different distributions needs to be tested given the high dimensional sensor data. Although different tags may exhibit different shapes and scales of distributions, the Z-Within and Z-Between scores on those tags may have less variety in shape and by design in scale. Across all the Z-Within and Z-Between distributions, there have been detected natural cutoffs at exceptional anomaly scores of 2, 6, 17, 50 and 150. However, an additional systematic empirical study to determine the cut offs and the corresponding anomaly distribution percentages needs to be conducted.
The exceptional anomaly scores are categorized into 11 buckets (i.e., (−2, 2)=bucket0, (2, 6)=bucket1, (6, 17)=bucket2, (17, 50)=bucket3, (50, 150)=bucket4, (150 and up)=bucket5, (−6, −2)=bucket−1, (−17, −6)=bucket−2, (−50, −17)=bucket−3, (−150, −50)=bucket−4, (−150 and below)=bucket−5). The percent of Z-Within scores falling into each bucket for every tag are calculated. Then, the distribution is drawn of those percentages across tags for each bucket and the quartiles are calculated as well as the 95% confidence interval for the median.
Calculations are performed similar to the ones in
The above results validate the expected conversions for the exceptional anomaly score cutoffs given real life data from power generation equipment sensor data. A second set of analysis was performed to validate that the suggested cutoffs and corresponding percentages are valid not just for all Z-Withins across all tags but also within each tag where the sample size is relatively smaller compared to the overall data. Continuous Z-Within scores were converted into an 11-category ordinal score with the predefined 11 buckets. The distribution was then drawn of the ordinal score for each tag separately (see
Aggregating Various Anomalous Observations
Many equipment users (e.g., power plants, turbine operators, etc.) have an abundance of data for monitoring & diagnostics. More importantly, this data often exists in small time units (e.g., every second or every minute). Although data abundance is an advantage, its aggregation should be done effectively so that data storage and data monitoring do not become problematic and data still keeps its useful knowledge.
Although aggregation is highly desirable, for some tasks it poses a risk. Anomaly aggregation in and of itself is an oxymoron. All anomalies imply specificity and concentrating on each and every data point, whereas aggregation implies summarization via excluding the specifics and the anomalies. However, regardless of its contradicting nature, anomaly aggregation is needed since per-second or per-hour data can not be stored for many tags across many time periods and more importantly, for certain types of events, it may be too much information to monitor every second or even every hour. More specifically, most equipment users are interested in catching ‘acute’ versus ‘chronic’ anomalies for their machine units. Acute anomalies are the rarely happening, high magnitude anomalies. Chronic anomalies frequently happen across different units and time for a specific metric.
As mentioned before, there are many different ways to aggregate data. Statistics by definition contains aggregation. Demonstrating the data via a handful of numbers, e.g., mean, median, standard deviation, variance, etc., is the simplistic definition of ‘statistics’ or ‘analytics’. However, none of these long-existing methods provide a solution for anomaly aggregation. A daily average cannot consistently illustrate an hourly anomaly. Aggregation of “exceptional anomaly scores” is a new method, as embodied by the present invention. Previously, monitoring hourly data was the only way to identify hourly anomalies. Data monitoring had to be done at the level of granularity in which the anomalies needed to be detected. In other words, it had to be done in the highest granularities, e.g., per second or per hour. At this granularity it is difficult to see longer-term trends or to effectively compare and contrast across units.
Two measures are described, according to embodiments of the present invention, which can be used to aggregate the exceptional anomaly scores: magnitude anomaly measure and frequency anomaly measure. Magnitude anomaly measure uses central tendency measures such as the average. Frequency anomaly measure uses ratios or percentages.
A magnitude anomaly measure can identify acute anomalies, and may use central tendency measures, such as the average. A daily absolute average (shown on the left of
A frequency anomaly measure can be used to identify chronic anomalies, and may use ratios or percentages. A daily percent anomaly (shown on the right on
When these two scores (i.e., daily absolute average and daily percent anomaly) are used simultaneously, they would demonstrate days with anomalous hours as well as differentiating acute vs. chronic anomalies. Acute anomalies (rarely occurring) would have high daily absolute averages and low daily percent anomalies. Acute anomalies could be illustrated by one or two high magnitude anomalies. On the other hand, chronic anomalies (frequently occurring) would have low or high daily absolute averages and high daily percent anomalies. Chronic anomalies could be illustrated by a few to a series of anomalies within a day. However, chronic anomalies do not necessarily need to have high magnitudes of exceptional anomaly scores.
Creating Alerts and Creating Heatmaps
The anomaly detection process and heatmap tool can be implemented in software with two Java programs called the Calculation Engine and the Visualization Tool, according to one embodiment of the present invention. The Calculation Engine calculates exceptional anomaly scores, aggregates anomaly scores, updates an Oracle database, and sends alerts when rules are triggered. The Calculation Engine can be called periodically from a command-line batch process that runs every hour. The Visualization Tool displays anomaly scores in a heatmap (see
An example command line call for the Calculation Engine is:
java -Xmx2700m -jar populate.jar - -update t7 n
This instructs the Calculation Engine to perform the periodic update, utilize up to 7 or more simultaneous threads, and identify any new sensor data in the database prior to proceeding. The program begins by calculating rules for any new custom alerts and any new custom peers of machine units created by the users of the Visualization Tool. It then retrieves newly arrived raw sensor data from a server, stores the new data in the Oracle database, and calculates exceptional anomaly scores and custom alerts for the newly added data. It stores results of all these calculations in a database, enabling the Visualization Tool to display a heatmap of the exceptional anomaly scores and custom alerts. If the calculations trigger a custom alert with a rule that has a high possibility of detecting a machine deterioration event with lead time, the Calculation Engine can be configured to send warning signals to members of the Monitoring & Diagnostics team. Alerts could be audio and/or visual signals displayed by the team's computers/notebooks, or signals transmitted to the team's communications devices (e.g., mobile phones, pagers, PDA's, etc).
The Visualization Tool's primary use is to display heatmaps for specific machine units to members of the Monitoring & Diagnostics team. Users of the Visualization Tool can change the date range, change the peer group, and drill into time series graphs of individual tags' data. The Visualization Tool may utilize Java Server Pages for its presentation layer and user interface. The Java Server Pages are the views in MVC architecture and contain no business logic. The only requirements on the server and client machines are a Java compliant servlet container and a web browser, for this example embodiment.
The Visualization Tool also supports several other use cases. Users of the Visualization Tool can view peer heatmaps; find machines with similar alerts; create custom peer groups; create custom alerts; and view several kinds of reports. Peer heatmaps merge each machine's heatmap into a single heatmap with adjacent columns showing peer machines' heatmap cells at the same instant in time instead of showing the machine's own heatmap cells at earlier and later times. Users can change the date; drill into time series graphs comparing peers' data for specific tags, and drill through to machine heatmaps. On other pages, users can also specify custom alerts and search for machines that have triggered these alerts. Users can create, modify, and delete rules for custom alerts. Reports summarize information about monitored units, the latency of units' raw sensor data (which differs among units), and the accuracy of the alerts triggered so far.
For example, the anomaly detection techniques, as embodied by the present invention, were applied to a set of turbines for which a significant failure event occurred. The failure event was rare, occurring in only 10 turbines during the 4-month period for which historical sensor data was available. For each turbine that experienced the event (event units), up to 2 months of historical data was collected. For the purposes of comparison, 4 months of historical data for 200 turbines that did not experience the event (non-event units) was obtained.
A peer group was created for each event unit consisting of 6-8 other turbines of similar configuration operating within the same geographic region. The Z-Within and Z-Between exceptional anomaly scores were then calculated for the event and non-event units. The Z-Withins represented how different a unit was compared to past observations when the unit was operating under similar conditions as measured by operating mode, wattage output, and ambient temperature. The Z-Betweens represented how different a unit was compared to its peers when they were operating under similar conditions. These deviations were then visualized via a heatmap, as illustrated in
The columns of the heatmap, shown in
The cells of the heatmap can display different colors or different shading or patterns to differentiate between different levels or magnitudes and/or directions/polarities of data. In two-row embodiments, the top row could represent the magnitude of the Z-Between exceptional anomaly scores whereas the bottom row could represent the magnitude of the Z-Within exceptional anomaly scores. If the anomaly score is negative (representing a value that is unusually low), the cell could be colored blue. Smaller negative values could be light blue and larger negative values could be dark blue. If the anomaly score is positive (representing a value that is unusually high), the cell could be colored orange. Smaller positive values could be light orange and larger positive values could be dark orange. The user can specify the magnitude required to achieve certain color intensities. There can be as many color levels displayed as desired, for example, instead of three color levels, 1, 2 or 4 or more color intensity levels could be displayed. In this example the cutoffs were determined by the sensitivity analysis.
The heatmap shown in
The top row of the heatmap shown in
While various embodiments are described herein, it will be appreciated from the specification that various combinations of elements, variations or improvements therein may be made, and are within the scope of the invention.
The present invention is related to the following application Ser. No. ______, titled “Anomaly Aggregation Method” and filed on ______.
