Tianyi Wang

TL;DR: This approach is used to tackle the data challenge problem defined by the 2008 PHM Data Challenge Competition, in which, run-to-failure data of an unspecified engineered system are provided and the RUL of a set of test units will be estimated.

TL;DR: A novel RUL prediction method inspired by the Instance Based Learning methodology, called Trajectory Similarity Based Prediction (TSBP), is proposed, which uses the historical instances of a system with life-time condition data and known failure time to create a library of degradation models.

TL;DR: This paper proposes an online adaptive condition-based maintenance method with pattern discovery and fault learning capabilities for mechanical systems based on a subtype of neural network techniques called self-organizing map (SOM).

TL;DR: In this article, the winner of the 2012 IEEE PHM challenge for bearing remaining useful life prediction presented an algorithm consisting of extraction of bearing characteristic frequency features with envelop analysis, fault detection with PCA, and two RUL prediction strategies to address the scenarios when the bearing faults have and have not been detected.

TL;DR: In this article, the authors present a machine learning hanging protocol analysis system for display of clinical images in a study, which includes a learning engine to receive processed image data and additional data to learn and adapt a hanging protocol for repeated use by applying one or more machine learning algorithms.