A novel feature-based probability of detection assessment and fusion approach for reliability evaluation of vibration-based diagnosis systems
Autor: | Dirk Söffker, Sandra Rothe, Daniel Adofo Ameyaw |
---|---|
Rok vydání: | 2019 |
Předmět: |
Online and offline
Computer science Biophysics 02 engineering and technology reliability evaluation computer.software_genre 01 natural sciences Statistical power Fault detection and isolation Maschinenbau Vibration based fault detection and isolation 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Feature based multi-sensor 010301 acoustics Reliability (statistics) vibration analysis structural health monitoring Mechanical Engineering 020208 electrical & electronic engineering fault diagnosis Fakultät für Ingenieurwissenschaften » Maschinenbau und Verfahrenstechnik » Institut für Mechatronik und Systemdynamik Probability of detection Key (cryptography) Structural health monitoring Data mining ddc:620 computer |
Zdroj: | Structural Health Monitoring. 19:649-660 |
ISSN: | 1741-3168 1475-9217 |
Popis: | Structural health monitoring systems are based on suitable sensor techniques allowing online and offline supervision of technical systems. The quantification of sensors/measurement devices is a key issue for qualifying their effectiveness and efficiency and therefore to ensure safe operations. Probability of detection serves as a performance measure for quantifying the reliability of conventional nondestructive testing procedures taking into account statistical variability of sensor-based measurements. For vibration-based supervision approaches and fault detection and isolation methods, the probability of detection approach cannot be applied similarly. This results mainly from the complexity of the dynamical behavior of systems monitored in relation to faults, sensors position (observability), and the related feature extraction or monitoring task. In this contribution, probability of detection evaluation of vibration-based fault detection of elastic mechanical structures to be monitored is developed. Beside a principal discussion of the problem serving as introduction, an example using different sensor types in combination with mechanical modifications of an elastic beam is presented. The a90/95-criteria representing probability of 90% detection at a confidence level of 95% is examined to the measurements and related outcomes. Based on the analysis of a suitably chosen feature (like eigenfrequency or band power) and dependent on the mechanical modes considered, the efficiency and deficiency of the different combinations are shown. Based on the proposed approach, a new insight into the usefulness of different sensor type and fault position combination becomes possible. To improve the detection quality, suitable assumptions in combination with sensor/information fusion are applied to feature-based analysis as detection task using vibration measurements. In addition, based on an experimental evaluation, it can be concluded that a suitable fault-feature probability of detection analysis can be successfully implemented as a new reliable measure for vibration-based fault detection and isolation approaches. Furthermore, decision fusion as the combination of different measurements will allow the improvement of results. Dependent on noise analysis, a trade-off between flaw size detection and probability of falsely characterizing a fault with a90/95 reliability level can be attained. |
Databáze: | OpenAIRE |
Externí odkaz: |