Despite the recent advances on intelligent fault diagnosis of rolling element bearings, existing research works mostly assume training and testing data are drawn from same distribution. However, due to variation operating condition, domain shift phenomenon generally exists, which results in significant performance deterioration. To address cross-domain problems, latest preferably apply adaptation techniques marginal distributions. it is usually assumed that sufficient available for training,...

In subway systems, the energy put into accelerating trains can be reconverted electric by using motors as generators during braking phase. general, except for a small part that is used onboard purposes, most of recovery transmitted backward along conversion chain and fed back overhead contact line. To improve utilization energy, this paper proposes cooperative scheduling approach to optimize timetable so generated train directly train. The less than required train; therefore, only...

Device-free passive indoor localization is playing a critical role in many applications such as elderly care, intrusion detection, smart home, etc. However, existing device-free systems either suffer from labor-intensive offline training or require dedicated special-purpose devices. To address the challenges, we present our system named MaTrack, which implemented on commodity off-the-shelf Intel 5300 Wi-Fi cards. MaTrack proposes novel Dynamic-MUSIC method to detect subtle reflection signals...

Rotating machinery fault diagnosis problems have been well-addressed when sufficient supervised data of the tested machine are available using latest data-driven methods. However, it is still challenging to develop effective diagnostic method with insufficient training data, which highly demanded in real-industrial scenarios, since high-quality usually difficult and expensive collect. Considering underlying similarities rotating machines, mining on different but related equipments...

In the past years, practical cross-domain machinery fault diagnosis problems have been attracting growing attention, where training and testing data are collected from different operating conditions. The recent advances in closed-set domain adaptation well addressed basic problem mode sets identical source target domains. While some attempts also made on partial open-set adaptations, no prior information of target-domain modes can be usually available real industries, that forms a...

Data-driven machinery fault diagnosis methods have been successfully developed in the past decades. However, cross-domain diagnostic problems not well addressed, where training and testing data are collected under different operating conditions. Recently, domain adaptation approaches popularly used to bridge this gap, which extract domain-invariant features for diagnostics. Despite effectiveness, most existing assume label spaces of identical that indicates mode sets same scenarios. In...

In the recent years, data-driven machinery fault diagnostic methods have been successfully developed, and tasks where training testing data are from same distribution well addressed. However, due to sensor malfunctions, can be collected at different places of machines, resulting in feature space with significant discrepancy. This challenging issue has received less attention current literature, existing approaches generally fail such scenarios. article proposes a domain adaptation method for...

We enrich a curated resource of commonsense knowledge by formulating the problem as one base completion (KBC). Most work in KBC focuses on bases like Freebase that relate entities drawn from fixed set. However, tuples ConceptNet (Speer and Havasi, 2012) define relations between an unbounded set phrases. develop neural network models for scoring arbitrary phrases evaluate them their ability to distinguish true held-out false ones. find strong performance bilinear model using simple additive...

In the past years, deep learning-based machinery fault diagnosis methods have been successfully developed, and basic diagnostic problems well addressed where training testing data are collected under same operating conditions. When from different distributions, domain adaptation approaches introduced. However, existing generally assume availability of target-domain in all health conditions during training, which is not accordance with real industrial scenarios. This article proposes a method...

Intelligent data-driven machinery fault diagnosis methods have been popularly developed in the past years. While fairly high accuracies obtained, large amounts of labeled training data are mostly required, which difficult to collect practice. The promising collaborative model solution with multiple users poses demands on privacy due conflict interests. Furthermore, real industries, from different can be usually collected machine operating conditions. domain shift phenomenon and concern make...