Combined compression of multiple correlated data streams for online-diagnosis systems.

Online fault-diagnosis is applied to various systems to enable an automatic monitoring and, if applicable, the recovery from faults to prevent the system from failing. For a sound decision on occurred faults, typically a large amount of sensor measurements and state variables has to be gathered, analyzed and evaluated in real-time. Due to the complexity and the nature of distributed systems all this data needs to be communicated among the network, which is an expensive affair in terms of communication resources and time. In this paper we present compression strategies that utilize the fact that many of these data streams are highly correlated and can be compressed simultaneously. Experimental results show that this can lead to better compression ratios compared to an individual compression of the data streams. Moreover, the algorithms support real-time constraints for time-triggered architectures and enable the data to be transmitted by means of shorter messages, leading to a reduced communication time and improved scheduling results. With an example data set we show that, depending on the parameters of the compression algorithm, more than one third of the bits (34.3%) in the data communication can be saved while only on about 0.2% of all data values a slight loss of accuracy occurs. This means 99.8% of the data values can be correctly delivered without any loss but with a significant reduction of bandwidth demands. [ABSTRACT FROM AUTHOR]