Application of Improved MFDFA and D-S Evidence Theory in Fault Diagnosis

Abstract

To improve the accuracy of centrifugal pump fault diagnosis, a novel fault diagnosis method based on improved multiple fractal detrended fluctuation analysis (MFDFA), the fusion of multi-sensing information derived from the back propagation (BP) neural network and the Dempster–Shafter (D-S) evidence theory, is accordingly proposed. Firstly, the multifractal spectral parameters of four sensor signals under four different operating conditions were extracted as centrifugal pump fault feature vectors using improved MFDFA and input to the BP neural network. Then, the basic trust assignment function was constructed by calculating trustworthiness (both local and global) as priori information, which is based on the output results of the neural networks specific to of each group of sensors. Finally, the basic trust assignment function was fused with decision processing in accordance with the D-S evidence combination rule in order to effectively achieve the multi-sensor information fusion centrifugal pump fault diagnosis. The experimental results show the multiple fractal spectrum feature parameters extracted by the improved MFDFA can accurately reflect the signal essence, and can be used as the fault feature vector. On this basis, this multi-sensor fault diagnosis reduces the uncertainty of fault classification and demonstrates improved accuracy compared to the single-sensor fault diagnosis thanks to being based on a combination of the BP neural networks and D-S evidence theory. Thereby, this method can facilitate accurate diagnosis of the centrifugal pump fault type with high confidence, subsequently providing a novel and alternative method to existing methods of diagnosis.

Scale-Fractal Detrended Fluctuation Analysis for Fault Diagnosis of a Centrifugal Pump and a Reciprocating Compressor

“…The method is evaluated for detecting four types of valve faults in a reciprocating compressor. In [ 38 ], multi-fractal spectral parameters of four sensor signals recorded from a centrifugal pump under different operating conditions are extracted as features fed to a backpropagation neural network. Then, the outputs from the neural networks are fused using the Dempster–Shafer evidence theory to attain the final diagnosis.…”

Centrifugal Pump Cavitation Fault Diagnosis Based on Feature-Level Multi-Source Information Fusion

“…However, the structure of centrifugal pumps is complex, and the operating environment often has random factors. Relying solely on one type of signal source can pose challenges in ensuring the accuracy and completeness of the acquired information and in providing immunity to interference [15]. MSIF is an emerging interdisciplinary field with significant development in recent years; it combines redundant or complementary information from one or multiple sensors, achieving cross-validation and mutual data compensation, which can enhance the performance of information systems, extract more valuable information, and strengthen system resilience and stability [16].…”