System-Level Predictive Maintenance: Review of Research Literature and Gap Analysis

This paper reviews current literature in the field of predictive maintenance from the system point of view. We differentiate the existing capabilities of condition estimation and failure risk forecasting as currently applied to simple components, from the capabilities needed to solve the same tasks for complex assets. System-level analysis faces more complex latent degradation states, it has to comprehensively account for active maintenance programs at each component level and consider coupling between different maintenance actions, while reflecting increased monetary and safety costs for system failures. As a result, methods that are effective for forecasting risk and informing maintenance decisions regarding individual components do not readily scale to provide reliable sub-system or system level insights. A novel holistic modeling approach is needed to incorporate available structural and physical knowledge and naturally handle the complexities of actively fielded and maintained assets.

Learning Mixtures of Multi-Output Regression Models by Correlation Clustering for Multi-View Data

In many datasets, different parts of the data may have their own patterns of correlation, a structure that can be modeled as a mixture of local linear correlation models. The task of finding these mixtures is known as correlation clustering. In this work, we propose a linear correlation clustering method for datasets whose features are pre-divided into two views. The method, called Canonical Least Squares (CLS) clustering, is inspired by multi-output regression and Canonical Correlation Analysis. CLS clusters can be interpreted as variations in the regression relationship between the two views. The method is useful for data mining and data interpretation. Its utility is demonstrated on a synthetic dataset and stock market dataset.

Noise-Tolerant Interactive Learning from Pairwise Comparisons

We study the problem of interactively learning a binary classifier using noisy labeling and pairwise comparison oracles, where the comparison oracle answers which one in the given two instances is more likely to be positive. Learning from such oracles has multiple applications where obtaining direct labels is harder but pairwise comparisons are easier, and the algorithm can leverage both types of oracles. In this paper, we attempt to characterize how the access to an easier comparison oracle helps in improving the label and total query complexity. We show that the comparison oracle reduces the learning problem to that of learning a threshold function. We then present an algorithm that interactively queries the label and comparison oracles and we characterize its query complexity under Tsybakov and adversarial noise conditions for the comparison and labeling oracles. Our lower bounds show that our label and total query complexity is almost optimal.

Performance Bounds for Pairwise Entity Resolution

One significant challenge to scaling entity resolution algorithms to massive datasets is understanding how performance changes after moving beyond the realm of small, manually labeled reference datasets. Unlike traditional machine learning tasks, when an entity resolution algorithm performs well on small hold-out datasets, there is no guarantee this performance holds on larger hold-out datasets. We prove simple bounding properties between the performance of a match function on a small validation set and the performance of a pairwise entity resolution algorithm on arbitrarily sized datasets. Thus, our approach enables optimization of pairwise entity resolution algorithms for large datasets, using a small set of labeled data.