Research on mechanical fault diagnosis based on MADS evidence fusion theory

Abstract

Abstract In machine intelligence fault diagnostic and health status decision-making systems, rich, complex, and fuzzy feature information cannot facilitate fault decision-making merely on a single data source. This requires utilizing the heterogeneity of information gathered from multiple sources to diminish the system’s uncertainty and improve the accuracy of decision-making. In this work, a novel neural network-based multi-source fusion classification model is proposed to diagnose the pump mechanical faults. The multi-head attention Dempster–Shafer (D–S) evidence fusion (MADS) system extends the model’s ability to focus on rich features. Furthermore, the uncertain values throwing mechanism can effectively eliminate samples from uncertain categories and increase the model’s ability to distinguish diagnostic results with low confidence. Compared with a single sensor, our multi-sensor joint decision based on seven sensors considerably improved the fault diagnostic accuracy of MADS system, which has increased by at least 12.34%. Experimental validation demonstrates that utilizing the improved combination rules provided for multi-source evidence fusion fault diagnosis can significantly improve the efficacy of conventional D–S fusion and reduce the probability of misjudgment; combining the multi-head attention mechanism can dramatically increase the precision of model fault diagnosis. The proposed method has the potential to substantially accelerate research in the field of multi-source sensor joint fault diagnosis.