UAV rotor fault diagnosis based on interval sampling reconstruction of vibration signals and a 1D-CNN deep learning method

Aiming at the possible mechanical faults of UAV rotor in the working process, this paper proposes a UAV rotor fault identification method based on interval sampling reconstruction of vibration signals and one-dimensional convolutional neural network (1D-CNN) deep learning. Firstly, experiments were designed to collect the vibration acceleration signals of UAV working at high speed under three states (normal, rotor damage by varying degrees, and rotor crack by different degrees). Then considering the powerful feature extraction and complex data analysis abilities of 1D-CNN, an effective deep learning model for fault identification is established utilizing 1D-CNN. During analysis, it is found that the recognition effect of minor faults is not ideal, which causes by all states were identified as normal and then reduces the overall identification accuracy, when using conventional sequential sampling to construct learning. To this end, in order to make the sample data cover the whole process of data collection as much as possible, a learning sample processing method based on interval sampling reconstruction of vibration signal is proposed. And it is also verified that the sample set reconstructed can easily reflect the global information of mechanical operation. Finally, according to the comparison of analysis results, the recognition rate of deep learning model for different degrees of faults is greatly improved, and minor faults could also be accurately identified, through this method. The results show that, the 1D-CNN deep learning model, could diagnose and identify UAV rotor damage faults accurately, by combing the proposed method of interval sampling reconstruction.

A novel multi-information decision fusion based on improved random forests in HVCB fault detection application

Evaluating the mechanical state of high-voltage circuit breakers (HVCBs) based on vibration information has currently become an important research direction. In contrast to the unicity of the travel–time and current–time curves, the vibration information from the different positions is diverse. These differences are often overlooked in HVCB fault identification applications. Additionally, the fault recognition results based on different location information often vary, and conflicting diagnosis results directly cause the accurate identification of the fault type to fail. Therefore, in this paper, a novel multi-information decision fusion approach is proposed based on the improved random forest (RF) and Dempster-Shafer evidence theory. In the proposed method, the diagnostic distribution of all classification regression trees (CART) in the RF is considered to solve the conflicts among the multi-information diagnosis results. Experimental results show that the proposed method eases the contradiction of multi-position diagnostic results and improves the accuracy of fault identification. Furthermore, compared to the common classifiers and probability generation methods, the effectiveness and superiority of the proposed method are verified.

Fault identification of double-circuit transmission lines on the same pole based on EEMD energy ratio

In order to improve the sensitivity and reliability of traveling wave protection, on the basis of analyzing the relationship of the anti-traveling wave current amplitude in the window after the internal/external failure of the double circuit line on the same tower, a fault identification method based on EEMD energy ratio is proposed. Use EEMD decomposition to decompose the anti-traveling wave current in a time window after the fault into 7 scales, and extracts the EEMD energy ratio at each scale at both ends to form a feature vector. Then it is sent to the particle swarm optimization support vector machine (PSO-SVM) for training and testing, and the internal and external faults are identified. Experiments show that the algorithm has good fault identification ability, the fault accuracy is 95% and the method sensitivity is high.

Fault Identification in Electric Servo Actuators of Robot Manipulators Described by Nonstationary Nonlinear Dynamic Models Using Sliding Mode Observers

The problem of fault identification in electric servo actuators of robot manipulators described by nonstationary nonlinear dynamic models under disturbances is considered. To solve the problem, sliding mode observers are used. The suggested approach is based on the reduced order model of the original system having different sensitivity to faults and disturbances. This model is realized in canonical form that enables relaxing the limitation imposed on the original system. Theoretical results are illustrated by practical example.

Wind Turbine Fault Diagnosis by the Approach of SCADA Alarms Analysis

Wind farm operators are overwhelmed by a large amount of supervisory control and data acquisition (SCADA) alarms when faults occur. This paper presents an online root fault identification method for SCADA alarms to assist operators in wind turbine fault diagnosis. The proposed method is based on the similarity analysis between an unknown alarm vector and the feature vectors of known faults. The alarm vector is obtained from segmented alarm lists, which are filtered and simplified. The feature vector, which is a unique signature representing the occurrence of a fault, is extracted from the alarm lists belonging to the same fault. To mine the coupling correspondence between alarms and faults, we define the weights of the alarms in each fault. The similarities is measured by the weighted Euclidean distance and the weighted Hamming distance, respectively. One year of SCADA alarms and maintenance records are used to verify the proposed method. The results show that the performance of the weighted Hamming distance is better than that of the weighted Euclidean distance; 84.1% of alarm lists are labeled with the right root fault.