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

Evaluation of the condition of complex electromechanical systems in the process industry is one of the most important purposes of condition monitoring, and is an indispensable step to ensure safe operation and comprehensive coverage capabilities of a system. However, there are still difficulties in obtaining a precise evaluation result from uncertain, incomplete and even conflicting system monitoring data, and this is a key step for condition evaluation. Since evidence theory has shown high efficiency in handling uncertain information, an evidence fusion-based framework for condition evaluation has been presented in this paper to improve the certainty and precision of evaluation decisions by fusing features extracted from different sources of evidence. The proposed framework contains key points for condition evaluation that are driven by data, and evidence fusion is at the core of this method. First, the frame of discernment has been automatically constructed using time-series based clustering. Second, a kernel density estimation based non-parametric method for determining the basic probability assignment of evidence has been proposed. After combination, the conditions can be evaluated using pignistic probability. An actual condition evaluation requirement of complex electromechanical systems in the process industry has been used to verify the effectiveness of the proposed framework and to compare it with existing methods. This framework can handle common problems of condition evaluation and overcome some drawbacks of other existing similar methods since no particular distribution is assumed and a prior knowledge of system conditions is not required. Furthermore, it can be flexibly used in many engineering applications.