Technology abstract

KETTY allows the detection and interpretation of systems novel behaviours, by the analysis of telemetry and SCADA systems data without using any a priori knowledge. Priority scores are generated according to how differently the parameters behave in two different time periods. The greater the difference, the higher the priority score, which suggests that the parameter should be further analysed by the engineers. It also correlates parameter behaviour to help understanding the system dynamics.

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Technology Description

KETTY (Knowledge Extraction from TelemeTrY) is a software tool that is designed for the detection of novel behaviour in spacecraft telemetry data without using any a priori knowledge. The purpose of this tool is to help the Flight Control Engineer get more insight into the monitored system dynamics and automatically detect and diagnose anomalous conditions. When thousands of relevant parameters or signals need to be checked by engineers to evaluate the process or system behaviour, the detection of an anomalous behaviour and its possible causes by visual analysis of the acquired raw data becomes an extremely demanding task, given the large amount of data and the possibly hidden cause-effect relationships among subsystems or parameters. KETTY may analyse thousands of parameters generating priority scores depending on the differences in the behaviour of the parameters between two different time periods. The greater the difference, the higher the priority score, which suggests that the parameter should be further analysed by the Flight Control Engineers to investigate if failures have occurred to a component or part of the satellite. Indeed the detection of incipient faults well before a complete failure occurs would potentially allow proper countermeasures, which may restrict the effects of the fault, avoiding the spread also to other parts of the system and, possibly recovery of the faulty component or subsystem. Although it was validated with spacecraft telemetry data, KETTY implements a general approach to knowledge extraction, which is applicable to any type of system. Any type of telemetry and SCADA systems data may be analysed. More specifically, the technology developed in this project may be useful in helping engineers in the automatic:
1. Extraction of synthetic characteristics of the monitored data;
2. Evaluation of the system behaviour, by the identification of subsystems of components with novel behaviour, ordered by priority score;
3. Determination of cause-effect relationships between parameters of the system that may not be known a priori.
In addition to this, another important characteristic of the developed technology is the fact that no a priori knowledge about the system is required to perform the analyses.

Innovations & Advantages

The main innovations and advantages of KETTY may be summarised as follows:
1. It requires no a priori knowledge about the system or data to be analysed;
2. It requires no configuration by field experts;
3. It extracts knowledge about the system behaviour automatically;
4. It is general, meaning that it may analyse data from any type of system, in addition to a spacecraft;
5. It analyses both numerical and categorical data under the same unique framework;
6. It applies different methods to evaluate the system behaviour under different perspectives;
7. It allows long term or short term analyses of the system behaviour to be performed both from a static and dynamic point of view.

Further Information

The detection of novelties may allow detecting novel working conditions in the system being monitored or, their association with anomalous conditions, detecting potential faults and, possibly, preventing complete failures of the system. The behaviour interpretation of the system may also be performed through the discovery of cause/effect relations among all data characterising the system itself. The knowledge extraction methods included in KETTY allows both a static and dynamic characterisation of the behaviour of parameters to be performed by comparing data acquired in 2 different time periods, performing both long-term and short-term analyses. One important characteristic of KETTY is that the generated outputs are not Boolean variables (0, normal, 1 novel) but are symptomatic variables ranging from 0 to 1, which are associated with the degree of novelty in the parameter behaviour, i.e. the comparison of the difference in the parameter behaviour in the two time periods. Therefore, the values of the symptomatic variables represent priority scores, which indicate how different the behaviour of the parameters is in two different time periods. The greater the symptomatic variable, the higher the priority score, which suggests that the parameter should be further analysed by Engineers or Technical Specialists to investigate if failures occurred to a component or part of the system being monitored or to confirm whether the observed changes are associated to new operating conditions of the system. Therefore these priority scores allow the generation of priority lists, i.e. lists of parameters ordered according to the priority scores, indicating which parameters should be further analysed first. In addition, by comparison of the symptomatic variables with suitable anomaly thresholds, the diagnostic decisions may be also generated, as Boolean variables equal to one when a novelty is detected in the parameter behaviour, 0 when the parameter behaviour is considered normal.

Current and Potential Domains of Application

Thanks to the general approach adopted in the project for the definition and implementation of the knowledge extraction techniques, the developed technology is deemed easily applicable to a wide set of different application domains. The application domains that can benefit from the developed technology are all those sectors (e.g. hydrocarbons, automotive) in which a large amount of sensors collect measurements of relevant parameters of the process or system. In synthesis, the characteristics of the application domains to which the developed technology may be applied are the following:
• Low or basic a priori knowledge about the system,
• Large amount of data.
The developed technology could be promoted and applied in the automotive, power generation, hydrocarbons, oil&gas, energy distribution and communication fields.