According to the prior art, it is known to e.g. provide time series data for a plurality of time series parameters relating at least to one aspect of the operation of a system over time. Such time series parameters can for example relate to operational parameters which are affected by the operation of the system and/or affect the operation of the system. For example, the operation of space crafts such as satellites or space probes can be monitored by receiving telemetry data from the space craft, wherein the telemetry data comprises time series data regarding a plurality of various time serious parameters which are related to the operation of the space craft.
Time series data for a certain time series parameter describes the characteristics and behaviour of the particular time series parameter over time, wherein the time series is a sequence of data points reflecting the value of the time series parameter at different times, e.g. periodically. Generally, a large number of various parameters is affected by or affects the operation of a system such as for example a satellite, a system of satellites, or a space probe so that generally a large amount of data is comprised in the telemetry data received from the system, i.e. time series data relating to time series of a large number of time series parameters related to the operation of the system.
According to the prior art, telemetry data comprising time series data of a plurality of time series parameters relating to the operation of the system can be analyzed, for example for the purpose of anomaly investigations, system/subsystem characterizations, or period characterizations such as e.g. mission phase characterizations (e.g. for characterizing different periods of different mission phases of a space craft, wherein the space craft is in different modes of operation during the different mission phases).
For example, relating to the issue of anomaly investigations, when an anomaly occurs during the operation of the system, the telemetry data comprising the time series data relating to the time series of the plurality of time series parameters has to be analyzed in order to find out which of the plurality of time series parameters is correlated to the occurrence of the anomaly or correlated to the anomaly. Here, a particular time series parameter is correlated with the anomaly, if the occurrence of the anomaly affects the behaviour of the particular time series parameter and/or is affected by the behaviour of the particular time series parameter. For example, a time series parameter which is correlated to the anomaly shows a different behaviour when the anomaly occurs and may be for example related to the cause of the anomaly, an effect of the anomaly, a knock-on effect of the anomaly, or in rare cases be only coincidental with the anomaly.
Such an analysis of time series data for the purpose of anomaly investigation is particularly important for understanding characteristics of the anomaly and the occurrence of the anomaly, regarding the cause, the effect and/or possible knock-on effects, for example, in order to make it possible to avoid the occurrence of such an anomaly in the future or to at least minimize the effects of the anomaly.
According to the prior art, when a space craft such as a satellite or a space probe faces an anomaly, flight control engineers on the ground have to perform an analysis of the received telemetry data including the time series data for trying to find the causes and/or effects of the anomaly, wherein such an anomaly investigation generally consists of very labour-intensive data analysis tasks because it is required that the flight control engineers performing the anomaly investigation try to guess or hypothesize which of the plurality of time series parameters describing the operation of the space craft may have a correlation to the anomaly. Thereafter, the flight control engineers have to check, whether the guessed or hypothesized parameter is actually correlated to the anomaly or not, i.e. the flight control engineers perform an analysis based on time series data to prove or discard the guess or hypothesis that a particular time series parameter of the plurality of time series parameters is indeed correlated to the anomaly. However, such an analysis has the large disadvantages that it is very labour-intensive on one hand, and on the other hand, the flight control engineer might overlook some of the actual correlations, if the flight control engineers miss to guess some of the parameters of the plurality of time series parameters which are indeed correlated to the anomaly, because the flight control engineers can only consider potential correlations according to their knowledge and experience. However, since the operation of a system, e.g. a space probe such as a satellite or a space probe is affected by or affects a huge number of different time series parameters, correlations may be unexpected and, therefore, be easily overlooked by the flight control engineers performing the analysis for the purpose of anomaly investigations.
Furthermore, similar analysis of time series data can be performed for the purpose of period characterizations such as mission phase characterizations, i.e. characterizations of different nodes of operations during a mission of a space craft such as a space satellite or a space probe. Here, it is also important to find out which of the plurality of time series parameters are correlated to a specific mission phase or operation mode of the space craft i.e. which of the plurality of time series parameters is affected by or affects the specific operational mode in a specific mission phase of the space craft.
However, similar to the problems of the time series data analysis performed for the purpose of anomaly investigations as discussed above, such mission phase characterizations or period characterizations are also very labour-intensive and also require that a flight control engineer performs a guess or hypothesis which of the plurality of time series parameters is correlated with a specific operational mode or mission phase of the space craft.
In view of the above-mentioned problems of the prior art, it is an object of the present invention to provide a method and an apparatus for determining which one or more time series parameters of a plurality of time series parameters relating to the operation of a system are correlated with a first operation state of the system, such as for example an operation state in which an anomaly occurs or an operation state corresponding to a specific mission phase or operation mode of the system, wherein the required analysis of the time series data can be performed more efficiently compared to the method according to the prior art as described above, and wherein the determination of the parameters which are correlated with the first operation state can be performed in a more robust way, for example, by reducing the risk or probability of overlooking actually correlated parameters, and wherein the time series data analysis can be performed more systematically. Furthermore, it is an object of the present invention to provide a method and an apparatus for automatically, quickly, systematically and reliably finding parameter correlations regarding all of the house-keeping telemetry time series parameters describing the operation of the system.