A comparative analysis of fault detection and process diagnosis methods based on a signal processing paradigm
I tiakina i:
| I whakaputaina i: | SN Applied Sciences vol. 7, no. 1 (Jan 2025), p. 10 |
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| I whakaputaina: |
Springer Nature B.V.
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| Ngā marau: | |
| Urunga tuihono: | Citation/Abstract Full Text Full Text - PDF |
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Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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MARC
| LEADER | 00000nab a2200000uu 4500 | ||
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| 001 | 3146655426 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2523-3963 | ||
| 022 | |a 2523-3971 | ||
| 024 | 7 | |a 10.1007/s42452-024-06390-3 |2 doi | |
| 035 | |a 3146655426 | ||
| 045 | 2 | |b d20250101 |b d20250131 | |
| 245 | 1 | |a A comparative analysis of fault detection and process diagnosis methods based on a signal processing paradigm | |
| 260 | |b Springer Nature B.V. |c Jan 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a An important paradigm in industrial engineering for fault detection and diagnosis purposes is signal processing. The various methods consider methods in the time, frequency, or time–frequency domain for signal processing as state and output signals from the considered process. The objective of this work is to perform a comparative analysis of the most used methods based on a signal processing paradigm and in the context of fault detection and process diagnosis. The electromechanical equipment that generates mechanical vibrations—as an effect of bearing faults—is considered and analyzed. The recorded data are explored with smaller and sliding frames, adapted to the processing criteria used. Seven methods are considered for evaluation: two in the time domain, two in the frequency domain and three in the time–frequency domain. The main problem is to extract and select the right features to use in the classification stage. The methods of the time domain are based on statistical moments and signal modeling. The methods in the frequency domain use either the discrete components of power spectra or the features of the frequency domain. In the time–frequency domain, the coefficients of the time–frequency transforms define digital images, which are further processed. For testing, the methods are evaluated with real recorded data from bearings with several types and sizes of faults, i.e., incipient, medium, advanced, and large. Finally, the considered methods are compared from the point of view of five criteria, namely, the recognition rate, window length, response time, computational resources, and complexity of the algorithms. A global quality criterion is built and used to assess the quality of the methods. The results of the computer-based experiments show acceptable performance for all methods for the test case of bearings but the potential to detect more complex faults and change detection in the behavior of the machines, in general. Time–frequency methods offer an optimum.Article highlights<list list-type="order"><list-item></list-item>A practical comparative overview of the main methods based on signal processing paradigm used in process diagnosis and detection problem.<list-item>Comparing methods from different domains of representations: time, frequency, and time-frequency domain.</list-item><list-item>An example of quality criterion in assessing the signal processing methods for process diagnosis and fault detection.</list-item> | |
| 653 | |a Digital imaging | ||
| 653 | |a Comparative analysis | ||
| 653 | |a Trends | ||
| 653 | |a Signal processing | ||
| 653 | |a Time domain analysis | ||
| 653 | |a Faults | ||
| 653 | |a Fault detection | ||
| 653 | |a Diagnosis | ||
| 653 | |a Response time (computers) | ||
| 653 | |a Statistical models | ||
| 653 | |a Machine learning | ||
| 653 | |a Power spectra | ||
| 653 | |a Vibrations | ||
| 653 | |a Fault diagnosis | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Hypotheses | ||
| 653 | |a Time-frequency analysis | ||
| 653 | |a Signal quality | ||
| 653 | |a Classification | ||
| 653 | |a Frequency domain analysis | ||
| 653 | |a Criteria | ||
| 653 | |a Statistical methods | ||
| 653 | |a Algorithms | ||
| 653 | |a Complexity | ||
| 653 | |a Industrial engineering | ||
| 653 | |a Construction standards | ||
| 653 | |a Economic | ||
| 773 | 0 | |t SN Applied Sciences |g vol. 7, no. 1 (Jan 2025), p. 10 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3146655426/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3146655426/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3146655426/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |