Novel adaptive loss weighted transfer network for partial domain fault diagnosis.

Mechanical fault transfer diagnosis has been confirmed as a feasible approach for tackling intelligent diagnosis with incomplete fault information and scarce labeled data on the basis of big data through the transfer of diagnostic knowledge from one or more conditions to any other condition. However, existing research has developed a hypothesis, i.e., the target domain shares an identical label space with the source domain, making it unfeasible to address the practical issue that the target domain label space is a subset of the source domain label space, resulting in low transfer diagnosis accuracy. To address this issue, a novel unsupervised intelligent diagnosis approach named double classifiers-dependent transfer diagnosis network is developed. In this approach, the label distribution weights are generated through the probability output of the classifier of source domain label space to target domain samples, by which small weights are assigned to irrelevant source samples to avoid negative transfer in the global-local maximum mean discrepancies (GL-MMD). In addition, classifiers of the source domain label space and the shared label space are built separately to improve the reliability of label distribution weights and GL-MMD. By training the network in the shared label space, diagnostic knowledge in partial domain issues is effectively transferred. Two cases are implemented to verify the effectiveness of the developed approach. Compared with other transfer diagnosis approaches, the developed approach achieved better diagnostic performance.

Development of an FBG Sensor Array for Multi-Impact Source Localization on CFRP Structures.

We proposed and studied an impact detection system based on a fiber Bragg grating (FBG) sensor array and multiple signal classification (MUSIC) algorithm to determine the location and the number of low velocity impacts on a carbon fiber-reinforced polymer (CFRP) plate. A FBG linear array, consisting of seven FBG sensors, was used for detecting the ultrasonic signals from impacts. The edge-filter method was employed for signal demodulation. Shannon wavelet transform was used to extract narrow band signals from the impacts. The Gerschgorin disc theorem was used for estimating the number of impacts. We used the MUSIC algorithm to obtain the coordinates of multi-impacts. The impact detection system was tested on a 500 mm × 500 mm × 1.5 mm CFRP plate. The results show that the maximum error and average error of the multi-impacts' localization are 9.2 mm and 7.4 mm, respectively.