CONDITION MONITORING OF ROLLER BEARING USING ENHANCED DEMPSTER/SHAFER EVIDENCE THEORY

According to the generalized Jaccard coefficient and false degree, an improved approach is proposed by incorporating Dempster-Shafer proofs for determining the level of confidence in the evidence. It also determines the weight of proof in terms of trust and falsity. Then, the base probability of the original evidence is weighted and averaged, followed by the adoption of the combined Dempster's compositional rule. It is evident that the above combination can be applied in condition monitoring of bearings up to rupture. Firstly, the supporting vibration signal is decomposed by applying the empirical mode decomposition, empirical wavelet transformation and variational mode decomposition approaches. All the vectors of the fault characteristic are extracted by combining the sample entropy. Then, the fault probability is obtained by performing preliminary diagnosis using the relevance vector machine, where the obtained preliminary diagnostic result is considered as the primary probability of the Dempster-Shafer evidence theory. Finally, it is revealed that an accurate diagnosis could be achieved by performing fusion using the enhanced evidence combination method. Specifically, the accuracies of the initial condition monitoring based on the EMD, EWT and VMD sample entropies and RVM were found to be 97.5%, 98.75% and 95%, respectively. The closeness and high values of these accuracies show that the selected methods are valid. The obtained condition monitoring results show that the relevance vector machine combined with the Dempster-Shafer evidence could enhance the efficiency. This theory has the least error and better reliability in supporting failure diagnosis

Training method for vehicle detection

Recently, vehicle detection methods have been popularly used in the field of intelligent vehicles. The performance and processing time of vehicle detection is very important because it is associated with the life of a driver. However, all vehicle detection methods generate missing detections and false detections because of different vehicle appearances. However, in a general road environment, the appearance of most of these vehicles has a front and a rear. In this paper, we propose a training method to detect the front and rear of the vehicle. Our vehicle detection integrates state-of-the-art feature-based detection. © 2016 ACM