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Vibration fault diagnosis through genetic matching pursuit optimization

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Abstract

This paper addresses the problem of fault diagnosis performed on a mechanical system, based on acquired vibrations from bearings. In this aim, an optimization algorithm resulted from the alliance between a time–frequency–scale signal processing method (the matching pursuit) and an evolutionary computing technique (mainly, a genetic algorithm) is introduced. The matching pursuit method itself leads to a NP-hard procedure, but, with the help of a metaheuristic, the procedure becomes computationally efficient. A generalization of Baker’s procedure implementing the stochastic universal sampling mechanism, as well as a new concept, namely the Boltzmann annealing selection, is introduced, in order to design the genetic algorithm appropriately. This latter not only plays an important role in convergence speed, but also constitutes the basis of a (self) adaptive mechanism aiming to keep in balance the exploration and exploitation features. Based on the optimal solution found through the genetic matching pursuit procedure, the bearings fault diagnosis can successfully be performed, even in case of multiple defects and without prior training of some defect classification model.

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Notes

  1. *John Holland expressed the opinion that (Mitchell 1995) is “the best general book on genetic algorithms written to date” (i.e. 1995).

Abbreviations

ABCM:

Algorithm based on bees collaborative model

ANN:

Artificial neural networks

bma(s):

Best matching atom(s)

BMPA:

Bees matching pursuit algorithm

BPFI:

Ball pass frequency on inner race

BPFO:

Ball pass frequency on outer race

dB:

Decibel(s)

EA:

Envelope analysis

EC:

Evolutionary computing

ES:

Exhaustive search

FT:

Fourier transform

GA:

Genetic algorithm(s)

GMPA:

Genetic matching pursuit algorithm

LSB:

Least significant bit

MPA:

Matching pursuit algorithm

mw:

Mother waveform

PSO:

Particle swarm optimization

SA:

Simulated annealing

SNR:

Signal-to-noise ratio

SP:

Signal processing

SUS:

Stochastic universal sampling

SVM:

Support vector machines

tf:

Time–frequency

tfs:

Time–frequency–scale

UP:

Uncertainty principle

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Funding

This research has been developed with the support of Alexander von Humboldt Foundation in Bonn, Germany. The Project was hosted by the University of Applied Sciences in Constance, Germany. The authors would like to express their most sincere gratitude to those institutions.

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Authors and Affiliations

  1. Faculty of Automatic Control and Computer Science, “Politehnica” University of Bucharest, 313 Splaiul Independentei, Sector 6, 060042, Bucharest, Romania

    Dan Stefanoiu & Janetta Culita

  2. Steinbeis Transfer Center – Dynamic Systems Institute, Blarerstr. 56, 78462, Constance, Germany

    Florin Ionescu

Authors
  1. Dan Stefanoiu
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  2. Janetta Culita
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  3. Florin Ionescu
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Correspondence to Janetta Culita.

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The authors declare that they have no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Communicated by V. Loia.

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Stefanoiu, D., Culita, J. & Ionescu, F. Vibration fault diagnosis through genetic matching pursuit optimization. Soft Comput 23, 8131–8157 (2019). https://doi.org/10.1007/s00500-018-3450-0

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