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Motor deficits are observed in Alzheimer's disease (AD) prior to the appearance of cognitive symptoms. To investigate the role of amyloid proteins in gait disturbances, we characterized locomotion in APP-overexpressing transgenic J20 mice. We used three-dimensional motion capture to characterize quadrupedal locomotion on a treadmill in J20 and wild-type mice. Sixteen J20 mice and fifteen wild-type mice were studied at two ages (4- and 13-month). A random forest (RF) classification algorithm discriminated between the genotypes within each age group using a leave-one-out cross-validation. The balanced accuracy of the RF classification was 92.3 ± 5.2% and 93.3 ± 4.5% as well as False Negative Rate (FNR) of 0.0 ± 0.0% and 0.0 ± 0.0% for the 4-month and 13-month groups, respectively. Feature ranking algorithms identified kinematic features that when considered simultaneously, achieved high genotype classification accuracy. The identified features demonstrated an age-specific kinematic profile of the impact of APP-overexpression. Trunk tilt and unstable hip movement patterns were important in classifying the 4-month J20 mice, whereas patterns of shoulder and iliac crest movement were critical for classifying 13-month J20 mice. Examining multiple kinematic features of gait simultaneously could also be developed to classify motor disorders in humans.

With the manufacturing industry stepping into the emerging new era of big data and intelligence, the amount of data collected from perception and monitoring systems with multiple smart sensors has increased tremendously. Such huge amount of multisensory data may not only power many aspects of fault diagnosis, but also bring great opportunities and challenges in modern manufacturing industry. In addition, with respect to intelligent fault diagnosis for machinery, few researches have been focused on the compound fault diagnosis under big-data circumstance. Therefore, a novel, intelligent, compound, fault decoupling method based on deep capsule network (CN) and ensemble learning is developed for compound fault decoupling and diagnosis using multisensory data. First, a decoupling CN (DCN) is constructed as the basic model. Second, taking the full advantage of multisensory data, the DCN model can be pretrained with multiple sensor data, which can obtain various pretrained DCN models. Finally, combining with ensemble learning skill, the pretrained DCN models are integrated by a combination strategy to obtain the deep ensemble CN (DECN) model for intelligent compound fault decoupling and diagnosis. The performance of the DECN model is validated on an automobile transmission (AT) data set with two compound faults, and the experimental results illustrate that the DECN model obtains higher diagnosis accuracy and decouples the compound fault correctly. [ABSTRACT FROM AUTHOR]

Accurate remaining useful life (RUL) prediction for rolling bearings encounters many challenges such as complex degradation processes, varying working conditions, and insufficient run-to-failure data. Transfer learning (TL), one paradigm of artificial intelligence technology, has demonstrated its powerful performance and great effectiveness for such challenges. As a result, many TL-based solutions have been widely developed and extensively studied for rolling bearing RUL prediction. Admittedly, several review articles have been published on RUL prediction. Nevertheless, the majority of these articles only concentrated on deep learning-based RUL prediction methods, and a review article that systematically overviews the status of TL-based RUL prediction has not been published. Therefore, it is urgent and significant to thoroughly summarize the academic publications and industrial applications related to TL-based RUL prediction, and present its potential challenges and future research directions. With such goals, the problem definitions of TL-based RUL prediction, the general procedure of RUL prediction, and typical TL-based RUL prediction algorithms are first introduced to help researchers quickly overview the state-of-the-art approaches and recent developments. Thereafter, relevant TL-based RUL prediction solutions are comprehensively discussed from the perspectives of three industrial scenarios, providing suggestions to researchers and engineers for selecting appropriate solutions in practical industrial applications. Finally, the key challenges and future trends in RUL prediction are presented to conclude this paper. We hope that this review of TL-based RUL prediction for rolling bearings can contribute to a better understanding of intelligent prognostic technology and will inspire researchers to extend their work on RUL prediction. [ABSTRACT FROM AUTHOR]

Deep Transfer Learning (DTL) is a new paradigm of machine learning, which can not only leverage the advantages of Deep Learning (DL) in feature representation, but also benefit from the superiority of Transfer Learning (TL) in knowledge transfer. As a result, DTL techniques can make DL-based fault diagnosis methods more reliable, robust and applicable, and they have been widely developed and investigated in the field of Intelligent Fault Diagnosis (IFD). Although several systematic and valuable review articles have been published on the topic of IFD, they summarized relevant research only from an algorithm perspective and overlooked practical applications in industry scenarios. Furthermore, a comprehensive review on DTL-based IFD methods is still lacking. From this insight, it is particularly important and more necessary to comprehensively survey the relevant publications of DTL-based IFD, which will help readers to conveniently understand the current state-of-the-art techniques and to quickly design an effective solution for solving IFD problems in practice. First, theoretical backgrounds of DTL are briefly introduced to present how the transfer learning techniques can be integrated with deep learning models. Then, major applications of DTL and their recent developments in the field of IFD are detailed and discussed. More importantly, suggestions on how to select DTL algorithms in practical applications, and some future challenges are shared. Finally, conclusions of this survey are given. At last, we have reason to believe that the works done in this article can provide convenience and inspiration for the researchers who want to devote their efforts in the progress and advance of IFD. [ABSTRACT FROM AUTHOR]