Evidence fusion-based framework for condition evaluation of complex electromechanical system in process industry

Novel Ensemble Approach with Incremental Information Level and Improved Evidence Theory for Attribute Reduction

With the development of intelligent technology, data in practical applications show exponential growth in quantity and scale. Extracting the most distinguished attributes from complex datasets becomes a crucial problem. The existing attribute reduction approaches focus on the correlation between attributes and labels without considering the redundancy. To address the above problem, we propose an ensemble approach based on an incremental information level and improved evidence theory for attribute reduction (IILE). Firstly, the incremental information level reduction measure comprehensively assesses attributes based on reduction capability and redundancy level. Then, an improved evidence theory and approximate reduction methods are employed to fuse multiple reduction results, thereby obtaining an approximately globally optimal and a most representative subset of attributes. Eventually, using different metrics, experimental comparisons are performed on eight datasets to confirm that our proposal achieved better than other methods. The results show that our proposal can obtain more relevant attribute sets by using the incremental information level and improved evidence theory.

Solving Sensor Identification Problem Without Knowledge of the Ground Truth Using Replicator Dynamics

In this article, we consider an emergent problem in the sensor fusion area in which unreliable sensors need to be identified in the absence of the ground truth. We devise a novel solution to the problem using the theory of replicator dynamics that require mild conditions compared to the available state-of-the-art approaches. The solution has a low computational complexity that is linear in terms of the number of involved sensors. We provide some sound theoretical results that catalog the convergence of our approach to a solution where we can clearly unveil the sensor type. Furthermore, we present some experimental results that demonstrate the convergence of our approach in concordance with our theoretical findings.

Game-Theoretic Learning for Sensor Reliability Evaluation Without Knowledge of the Ground Truth

Sensor fusion has attracted a lot of research attention during the few last years. Recently, a new research direction has emerged dealing with sensor fusion without knowledge of the ground truth. In this article, we present a novel solution to the latter pertinent problem. In contrast to the first reported solutions to this problem, we present a solution that does not involve any assumption on the group average reliability which makes our results more general than previous works. We devise a strategic game where we show that a perfect partitioning of the sensors into reliable and unreliable groups corresponds to a Nash equilibrium of the game. Furthermore, we give sound theoretical results that prove that those equilibria are indeed the unique Nash equilibria of the game. We then propose a solution involving a team of learning automata (LA) to unveil the identity of each sensor, whether it is reliable or unreliable, using game-theoretic learning. The experimental results show the accuracy of our solution and its ability to deal with settings that are unsolvable by legacy works.

Uncertain texture features fusion based method for performance condition evaluation of complex electromechanical systems

Accurate performance condition evaluation has a pivotal role in maintaining the operating reliability and preventing damage to complex electromechanical systems (CESs), which is still a challenging task. The uncertain features fusion inspired method is developed by utilizing the data-graph conversion, texture analysis, and improved evidence fusion. Unlike the conventional continuous time-series analysis-based methods, the 2D color-spectrums related to the performance conditions are constructed without information losing, and texture features of spectrums are extracted and fused to realize evaluation. The effectiveness of the proposed method is verified by actual evaluation applications. Moreover, the proposed method provides a new idea for large-scale high-dimensional data processing, decision making, uncertainty handling, and other engineering applications.