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The widespread use of sensors has resulted in an unprecedented amount of time series data. Time series mining has experienced a particular surge of interest, among which, subsequence matching is one of the most primary problem that serves as a foundation for many time series data mining techniques, such as anomaly detection and classification. In literature there exist many works to study this problem. However, in many real applications, it is uneasy for users to accurately and clearly elaborate the query intuition with a single query sequence. Consequently, in this paper, we address this issue by allowing users to submit a small query set, instead of a single query. The multiple queries can embody the query intuition better. In particular, we first propose a novel probability-based representation of the query set. A common segmentation is generated which can approximate the queries well, in which each segment is described by some features. For each feature, the corresponding values of multiple queries are represented as a Gaussian distribution. Then, based on the representation, we design a novel distance function to measure the similarity of one subsequence to the multiple queries. Also, we propose a breadth-first search strategy to find out similar subsequences. We have conducted extensive experiments on both synthetic and real datasets, and the results verify the superiority of our approach.

The present research shows the implementation of a virtual sensor for fault detection with the feature of recovering data. The proposal was implemented over a bicomponent mixing machine used for the wind generator blades manufacture based on carbon fiber. The virtual sensor is necessary due to permanent problems with wrong sensor measurements. The solution proposed uses an intelligent model able to predict the sensor measurements, which are compared with the measured value. If this value belongs to a specified range, it is valid. Otherwise, the prediction replaces the read value. The process fault detection feature has been added to the proposal, based on consecutive erroneous readings, obtaining satisfactory results.

In this paper, we study the fault diagnosis problem for distributed discrete event systems. The model assumes that the system is composed of distributed components that are modeled in labeled Petri nets and interact with each other via sets of common resources (places). Further, a component's own access to a common resource is an observable event. Based on the diagnoser approach proposed by Sampath et al, a distributed fault diagnosis algorithm with communication is presented. The distributed algorithm assumes that the local diagnosis process can exchange messages upon the occurrence of observable events. We prove the distribute diagnosis algorithm is correct in the sense that it recovers the same diagnostic information as the centralized diagnosis algorithm. And then, the OBDD (Ordered Binary Decision Diagrams) is introduced to manage the state explosion problem in state estimation of the system.

The checking fidelity of the systems with built-in fault detection circuits is investigated here taking into account the failure rates of functional units, also checking and transmission facilities of right and wrong information on the state of functional units. When fault detection circuits or information transmission facilities fail, then the information given by them may cause false or undetected failures in comparison with the real state of the corresponding functional unit.