Multi-fault-feature identification method based on sparse multiperiod-group lasso

Abstract

The invention discloses a multi-fault-feature identification method based on a sparse multiperiod-group lasso. The multi-fault-feature identification method comprises the following steps that an to-be-identified signal is analyzed so as to construct a binary periodic sequence b, based on a fault feature signal, the between-group sparse characteristic in period groups are presented to obtain a regularization term P (x;b) for promoting between-group sparseness in the period groups, and a sparse multiperiod-group lasso model is established based on discrimination of different fault feature frequencies; controlled optimization operators of a data fidelity term (please see the specifications for the formula) and the regularization term (please see the specifications for the formula) in the sparse multiperiod-group lasso model are constructed correspondingly, through decoupling of the controlled optimization operators, variables are separated, aiming at each controlled optimization operator,the closed-form solution optimized by the controlled optimization operator is established, through iteration, the closed-form solution corresponding to the controlled optimization operator of each fault is solved, and thus model solving is achieved; regularization parameters are set adaptively through simulation signal counting and analyzing, the adaptive solution of the algorithm is obtained through the parameters, and thus each fault is obtained through separation; and aiming at each fault obtained through separation, the fault type is identified through envelope analysis.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis methods, in particular to a multi-fault feature identification method based on a sparse multi-period group lasso. Background technique [0002] Predictive and health maintenance system (PHM) has attracted more and more attention. It is of great significance for reducing the cost of operation and maintenance of electromechanical systems and avoiding catastrophic accidents. The vibration signal analysis has become an indispensable and important part of PHM because of its comprehensive and timely reflection of fault characteristics. However, due to the complexity of the structure, the diversity of parts and the harsh operating environment of the electromechanical system, its key components, such as gears and bearings, often fail, and due to the influence of early failures, a chain reaction will occur, causing components Multiple damages, that is, coupling of multiple faults. However, the co...

AI Technical Summary

Problems solved by technology

But the multi-fault separation algorithm based on machine learning has two big problems: on the one hand, the machine learning algorithm needs a large number of samples for each type of fault, which is difficult to obtain in actual situations, even if it can be obtained, the cost also immeasurable

This brings great challenges to how to choose such different basis functions

Moreover, this kind of morphological component analysis method based on the sparse representation of the transform domain has a large number of matrix operations in the optimization process, and its computational complexity is high, which greatly restricts the method in engineering applications.

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