A fault diagnosis model based on singular value manifold features, optimized SVMs and multi-sensor information fusion

To achieve better fault diagnosis of rotating machinery, this paper presents a novel intelligent fault diagnosis model based on singular value manifold features (SVMF), optimized support vector machine (SVMs) and multi-sensor information fusion.Firstly, a new fault feature denoted as SVMF is developed to better represent faults.SVMF is acquired by extracting manifold topology features of the singular spectrum.Compared with frequently-used fault features, the feature scale of SVMF is constant under variable rotating speed, and the extraction process of SVMF also has the effect of self-weighting.So SVMF has a better representation of faults.Then, to select optimal parameters for model training of SVMs, an improved fruit fly algorithm is proposed by introducing guidance search mechanism and enhanced local search operation, and as a result, both the convergence speed and accuracy are improved.At last, the Dempster-Shafer evidence theory is introduced to fuse decision-level information of SVMs models of multiple sensors.By information fusion, the conflict of fault diagnosis conclusions from multi-sensor is eliminated, which lead to high robustness and accuracy of fault diagnosis model.As a summary, the proposed method combines the advantages of SVMF in fault representation, SVMs in fault identification and the Dempster-Shafer evidence theory in information fusion, and as a result the proposed method will get better fault diagnosis performance.The proposed intelligent fault diagnosis model is subsequently applied to the fault diagnosis of the gearbox.Experimental results show that the proposed fault diagnostic framework is versatile at detecting faults accurately.