Cross-Domain Fault Diagnosis Using Knowledge Transfer Strategy: A Review

Cross-Domain Knowledge Transfer Using Structured Representations

Previous work in knowledge transfer in machine learning has been restricted to tasks in a single domain. However, evidence from psychology and neuroscience suggests that humans are capable of transferring knowledge across domains. We present here a novel learning method, based on neuroevolution, for transferring knowledge across domains. We use many-layered, sparsely-connected neural networks i...

A Simulation Based Fault Diagnosis Strategy Using Extended

Because of the complexity and diversification of today’s HVAC systems, Fault Detection and Diagnosis (FDD) systems have become necessary to reduce maintenance cost and to provide building energy efficiency, but with a minimum of engineering cost. Based on a generic method of generating Fault Detection (FD) systems from Building Information Models (BIM), we now propose an extension of the underl...

A Novel Intelligent Fault Diagnosis Approach for Critical Rotating Machinery in the Time-frequency Domain

The rotating machinery is a common class of machinery in the industry. The root cause of faults in the rotating machinery is often faulty rolling element bearings. This paper presents a novel technique using artificial neural network learning for automated diagnosis of localized faults in rolling element bearings. The inputs of this technique are a number of features (harmmean and median), whic...

Prediction of Video Popularity Based on Cross - Domain Knowledge Transfer

Cross Domain Knowledge Transfer for Person Re-identification

Person Re-Identification (re-id) is a challenging task in computer vision, especially when there are limited training data from multiple camera views. In this paper, we propose a deep learning based person re-identification method by transferring knowledge of mid-level attribute features and high-level classification features. Building on the idea that identity classification, attribute recogni...