Learning chronicles signing multiple scenario instances

Abstract

Chronicle recognition is an efficient and robust method for fault diagnosis. The knowledge about the underlying system is gathered in a set of chronicles, then the occurrence of a fault is diagnosed by analyzing the flow of observations and matching this flow with a set of available chronicles. The chronicle approach is very efficient as it relies on the direct association of the symptom, which is in this case a complex temporal pattern, to a situation. Another advantage comes from the efficiency of recognition engines which make chronicles suitable for one-line operation. However, there is a real bottleneck for obtaining the chronicles. In this paper, we consider the problem of learning the chronicles. Because a given situation often results in several admissible event sequences, our contribution targets an extension to multiple event sequences of a chronicle discovery algorithm tailored for one single event sequence. The concepts and algorithms are illustrated with representative and easy to understand examples.

Diagnosis in industrial processes

“…• Diagraph (Graph with directed arcs between the nodes) [45] • Bond graphs [46] • SDG -Signed direct graph [47] • ESDG -Extended SDG [48] • PCEG -Possible cause and effect graph models [49,50] • HDG -HAZOP-digraph models [51] • SCC -Strongly Connected Component [52] • MSCC -Maximal strongly connected component [53] • Causal Graphs [54] • Chronicles [13,55,56] • Fault trees [57] II. Qualitative physics:…”

Mapping Chronicles to a $k$-dimensional Euclidean Space via Random Projections

“…An interesting chronicle discovery algorithm that finds chronicles in a temporal sequence on a frequency criterion is proposed in [5]. An extension to multiple temporal sequences is presented in [6]. The chronicle discovery process is discussed in healthcare applications in [7] and in [8] where clinical pathways are analyzed.…”

A Learning Algorithm for Episodes

“…We propose an approach based discovery and learning patterns [1] [2]. It is inspired by [3] [4] that proposes an algorithm to learn chronicles. Chronicles are temporal patterns based on a set of events and temporal constraints relating their occurence dates.…”

“…Chronicles are temporal patterns based on a set of events and temporal constraints relating their occurence dates. They are used to represent the dynamic behavior of complex systems in an eventbased fashion and have found several applications in the supervision and diagnostic field [5,3,6]. In our case, the occurence dates are not recorded in the traces that are given as input sequences for the learning algorithm.…”