Maximal information-based nonparametric exploration for condition monitoring data

The system condition of valuable assets such as power plants is often monitored with thousands of sensors. A full evaluation of all sensors is normally not done. Most of the important failures are captured by established algorithms that use a selection of parameters and compare this to defined limits or references. Due to the availability of massive amounts of data and many different feature extraction techniques, the application of feature learning within fault detection and subsequent prognostics have been increasing. They provide powerful results. However, in many cases, they are not able to isolate the signal or set of signals that caused a change in the system condition. Therefore, approaches are required to isolate the signals with a change in their behavior after a fault is detected and to provide this information to diagnostics and maintenance engineers to further evaluate the system state. In this paper, we propose the application of Maximal Information-based Nonparametric Exploration (MINE) statistics for fault isolation and detection in condition monitoring data. The MINE statistics provide normalized scores for the strength of the relationship, the departure from monotonicity, the closeness to being a function and the complexity. These characteristics make the MINE statistics a good tool for monitoring the pair-wise relationships in the condition monitoring signals and detect changes in the relationship over time. The application of MINE statistics in the context of condition monitoring is demonstrated on an artificial case study. The focus of the case study is particularly on two of the MINE indicators: the Maximal information coefficient (MIC) and the Maximum Asymmetry Score (MAS). MINE statistics prove to be particularly useful when the change of system condition is reflected in the relationship between two signals, which is usually difficult to be captured by other metrics

Matching Users' Preference Under Target Revenue Constraints in Optimal Data Recommendation Systems

This paper focuses on the problem of finding a particular data recommendation strategy based on the user preferences and a system expected revenue. To this end, we formulate this problem as an optimization by designing the recommendation mechanism as close to the user behavior as possible with a certain revenue constraint. In fact, the optimal recommendation distribution is the one that is the closest to the utility distribution in the sense of relative entropy and satisfies expected revenue. We show that the optimal recommendation distribution follows the same form as the message importance measure (MIM) if the target revenue is reasonable, i.e., neither too small nor too large. Therefore, the optimal recommendation distribution can be regarded as the normalized MIM, where the parameter, called importance coefficient, presents the concern of the system and switches the attention of the system over data sets with different occurring probability. By adjusting the importance coefficient, our MIM based framework of data recommendation can then be applied to system with various system requirements and data distributions.Therefore,the obtained results illustrate the physical meaning of MIM from the data recommendation perspective and validate the rationality of MIM in one aspect.Comment: 36 pages, 6 figure

Unsupervised ensemble minority clustering

Cluster a alysis lies at the core of most unsupervised learning tasks. However, the majority of clustering algorithms depend on the all-in assumption, in which all objects belong to some cluster, and perform poorly on minority clustering tasks, in which a small fraction of signal data stands against a majority of noise. The approaches proposed so far for minority clustering are supervised: they require the number and distribution of the foreground and background clusters. In supervised learning and all-in clustering, combination methods have been successfully applied to obtain distribution-free learners, even from the output of weak individual algorithms. In this report, we present a novel ensemble minority clustering algorithm, Ewocs, suitable for weak clustering combination, and provide a theoretical proof of its properties under a loose set of constraints. The validity of the assumptions used in the proof is empirically assessed using a collection of synthetic datasets.Preprin